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Optimal sensor placement for monitoring and controlling greenhouse internal environments
b i o s y s t e m s e n g i n e e r i n g 1 8 8 ( 2 0 1 9 ) 1 9 0 e2 0 6
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Research Paper
Optimal sensor placement for monitoring and controlling greenhouse internal environments Sang-yeon Lee a, In-bok Lee a,b,*, Uk-hyeon Yeo a, Rack-woo Kim a, Jun-gyu Kim a a
Department of Rural Systems Engineering, Research Institute for Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, 1, Gwanakno, Gwanakgu, Seoul, 08826, Republic of Korea b Research Institute of Green Eco Engineering, Institute of Green Bio Science and Technology, Seoul National University, 1477, Pyeongchang-daero, Daehwa-myeon, Pyeongchang-gun, Gangwon-do, 25354, Republic of Korea
article info
In large greenhouses with information and communication technology capability, envi-
Article history:
ronmental conditions can be measured and communicated to control the internal envi-
Received 11 July 2019
ronment. However, in such greenhouses, it is difficult to control the internal environment
Received in revised form
uniformly and appropriately. Additionally, there is uncertainty regarding whether the data
1 October 2019
measured at a particular location accurately represents the entire greenhouse environ-
Accepted 9 October 2019
ment. Furthermore, the locations of sensors are usually determined based on the experience of growers and the greenhouse designers. To accurately measure the internal environment of a greenhouse, it is necessary to properly select the installation locations of
Keywords:
the sensors. The objective of this study was to determine the optimal sensor placement for
Air temperature
monitoring and controlling the internal environment of a greenhouse. The study green-
Greenhouse
house was an eight-span plastic greenhouse growing Irwin mango crops. Air temperature
Monitoring
data measured at nine locations in the greenhouse were used. All of the possible combi-
Optimisation
nations of monitoring locations were evaluated, and optimal sensor placements were
Sensor placement
selected according to the number of sensors. The optimisation was conducted using two methods: error-based sensor placement and entropy-based sensor placement. Using the former approach, sensor locations for which the monitored data were close to the reference value, i.e. the average data of all the measurement locations, were selected. Using the latter approach, sensor locations influenced by the external weather conditions resulting in poor environmental control were selected. Using these methods, optimal sensor locations for representing the entire environment of the facility and for detecting areas with significant air temperature variations were determined. © 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.
* Corresponding author. 1, Gwanakno, Gwanakgu, Seoul, 08826, Republic of Korea. Fax: þ82 2 873 2087. E-mail address: [email protected] (I.-b. Lee). https://doi.org/10.1016/j.biosystemseng.2019.10.005 1537-5110/© 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.
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Nomenclature ANOVA Analysis of variance Value of the combination trend at a specific Ci time Error between the reference trend and Ei combination trend HSD Honest significant difference HðXÞ Information entropy of the data measured by one sensor HðX; YÞ Joint entropy of the data measured by two sensors HðY =XÞ Conditional entropy of the data measured by two sensors HðXk Þ þ … þ HðXp Þ Sum of the entropy values measured by the selected sensors mp p P P HðXi ; Xj Þ Amount of information delivered from i¼1 j¼k the unselected sensors to the selected sensors Performance index of a combination of sensors Ii MAPE Mean absolute percentage error m Range of the temperature data measured at a specific sensor n Number of installed sensors in the greenhouse N Total number of data p Number of selected sensors PðEÞ Uncertainty in the probability distribution Probability mass function for a variable Pi ðEÞ R Ranking RMSE Root-mean-square error Value of the reference trend at a specific time Ri Performance score for a specific combination of Si sensors Score for the mean error S1 ðEi Þ Score for the standard deviation S2 ðEi Þ Score for the outlier S3 ðEi Þ Score for the z-test S4 ðEi Þ THI Temperatureehumidity index m P TðXi ; Xj ; …Xp Þ Total entropy for the combinations of i¼1 sensors
1.
Introduction
Since the 1970s, greenhouse cultivation in South Korea has been increasing due to advantages such as improved environmental control, intensive cultivation, year-round production, and the production of high-quality agricultural products. The total greenhouse area in South Korea has increased from 700 ha in 1970 to 55,217 ha in 2017. Total production from protected cultivation (2,414,354 tonnes annually) accounted for approximately 28% of the total fruit and vegetable productions in 2017 (Ministry of Agriculture, Food and Rural Affairs of Korea, 2017). Energy-efficiency projects for energy saving in horticultural facilities and modernisation projects for applying information and communications technologies
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to horticultural facilities were implemented by the government, supply companies, and growers. With the increase in automation of greenhouses, the area of multi-span greenhouses in South Korea has increased from 5264 ha in 2005 to 6365 ha in 2016 (Ministry of Food, Agriculture, Forestry and Fisheries, 2017). Proper environmental control of the internal condition including the air temperature, humidity, and CO2 concentration in a greenhouse are necessary to affect the growth, productivity, and quality of crops. In a large greenhouse such as an information and communication technology (ICT)-applied greenhouse or smart farm, the internal conditions are monitored using sensors to control the greenhouse environment via greenhouse actuators, such as air conditioners, circulation fans, and heat pumps. For measuring the environment inside a greenhouse, different sensors are installed and used by growers and researchers. However, growers generally install a limited number of sensors, owing to economic limitations and management challenges. Typically, the sensor locations are decided according to the experience of growers and greenhouse designers. The centre location of facilities has been generally considered as the representative location for monitoring environmental factors such as the air temperature and humidity (Feng, Li, & Zhi, 2013). However, there is uncertainty regarding whether the environment measured at the centre of facilities properly represents the entire environment inside greenhouses. Therefore, it is necessary to select optimal installation locations for the limited number of sensors to accurately monitor the internal environments of large greenhouses. Previous studies on selecting the optimal sensor location have focused on monitoring the stability of structures (Hong, Hwang, & Kim, 1996; Lee, Kim, & Lee, 2016; Lee, Kim, Park, & Jang, 2009; Ren, Yan, & Jiang, 2001; Yi, Zhou, Li, & Wang, 2017) and measuring the internal environment of specific facilities (Arnesano, Revel, & Seri, 2016; Chang, Ha, Jun, & Kang, € hner & 2012; Hu & Patel, 2014; Huang, Zhou, & Zhang, 2014; Lo Camelli, 2005; Seabrook, 2017; Wang, Ma, & Wang, 2009; Worden & Burrows, 2001). During such studies for measuring the internal environment of facilities, attempts have been made to identify the optimal sensor locations. Arnesano et al. (2016) identified the optimal locations of temperature sensors for controlling a heating, venting, and air-conditioning system in a large sport stadium using an error-based method. The optimal sensor location was selected according to the z-index, the outliers, the standard deviation, and the average. Chang et al. (2012) applied the theory of information entropy to identify the optimal combination of sensor locations for measuring the pressure in a water pipe network. Using the information entropy, sensor locations that provided the largest amount of information were selected as the optimal locations. Feng et al. (2013) simulated the internal air temperature and wind-velocity distributions of a greenhouse via computational fluid dynamics (CFD) and suggested that the optimal sensor location is where the air temperature and wind speed do not change rapidly. Liu, Chen, Lv, and Li (2014) simulated the air temperature distribution inside a mechanically ventilated greenhouse according to four environmental conditions using CFD. According to the results of the CFD simulation, the centre of the greenhouse, where the fluctuation of the internal microclimate was low, was the
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optimal sensor location. Curi et al. (2017) measured the internal air temperature and humidity of a broiler house for a total of 52 sites at the height of a chicken (0.3 m) during the summer season. Using the ordinary kriging method, which is an interpolation method, the range of the temp eratureehumidity index (THI) was calculated. The three locations where the THI was the lowest, the median, and the highest were selected as the optimal sensor locations, because the internal environment of the mechanically ventilated facility was divided into three parts: the entrance area, the centre of the facility, and the area around the exhaust fans. However, the previous studies did not consider the timeseries of the environmental data or the number of sensors that could be installed in the facilities. Furthermore, during the previous studies conducted for agricultural facilities, the optimal sensor locations were selected as those where the environmental change was the smallest or where the difference between the environments was the greatest according to the judgement of the researchers, without quantitative criteria. Although studies have been conducted to select locations that can represent the entire environment inside a facility for maintaining uniform environmental conditions, it is also necessary to install sensors at locations that are significantly affected by the unstable external environment. The objective of this study was to select the optimal sensor location for accurately controlling the entire environment of the greenhouse and for detecting areas with significant air temperature variations, such as areas that are significantly affected by the external environment. In this study, data for the air temperature, which is one of the most important factors for crop growth, were used because it is easy to simultaneously measure the air temperature at multiple locations. Seasonal air temperature data were recorded at nine locations inside an eight-span plastic greenhouse of the 1e2 W type. Using error-based and entropy-based methods, the air temperature data measured at these nine locations were analysed. Finally, the optimal sensor locations were selected according to the season and the number of sensors installed.
2.
Materials and methods
The internal air temperature of the greenhouse was measured at nine locations to determine the optimal sensor placement for monitoring and controlling the internal environment accurately. Based on long-term data measured in a greenhouse, all the combinations of sensor locations were evaluated to determine which combinations would best reflect the overall environment of the greenhouse using the error-based method. Statistical analysis was performed to evaluate the accuracy of the data measured at selected sensor locations compared with the average data measured by all of the sensors. The optimal sensor placements for monitoring and controlling the internal environment were identified according to the number of sensors installed in the greenhouse. Additionally, the optimal sensor locations that often were significantly affected by the changing external environment were identified using the entropy-based method. The experimental procedure is shown in Fig. 1.
2.1.
Study greenhouse
The experiments were performed in a greenhouse located on the west coast of South Korea in Jugyo-myeon, Boryeong City, Chungcheongnam-do Province (126 290 E, 36 230 N). The study greenhouse was the eight-span 1e2 W type greenhouse, as shown in Fig. 2. The study greenhouse was oriented northesouth. The study greenhouse had a width of 34.4 m, a length of 30.0 m, an eave height of 4.5 m, and a ridge height of 5.7 m. The internal space of the study greenhouse was divided into a workspace and cultivation space. The workspace was 4.0 m from the entrance and was used for controlling the mechanical system. The cultivation space comprised of an area of 768 m2 (32 m 24 m) and was used for cultivating Irwin mangoes. The covering of the greenhouse was made of a 0.15-mm-thick polyolefin film. The sides of the greenhouse were covered with a doublelayer polyolefin film for added insulation. Additionally, thermal curtains were installed along the sidewalls and horizontally at eave height for energy saving during the winter season. A total of 100 Irwin mango plants were cultivated in pots, as shown in Fig. 3. The diameter of each pot was 0.8 m, and the pots were arranged in 13 rows. The internal environment of the greenhouse was controlled using a natural ventilation system with side and roof vents and was heated or cooled using three heat pumps (ADFSLX12WHB, A-San Inc., Korea). The cooling and heating capacities of heat pumps were 43,276 W and 36,786 W, respectively. Because the greenhouse had natural ventilation, it was often challenging to optimally and uniformly control the internal environment of the greenhouse.
2.2. Monitoring the internal environment of the greenhouse The micro-climate in greenhouses, such as the air flow rate, air temperature, humidity, CO2 concentration, and solar radiation, determines the growth rate and quality of the crops. In this study, air temperature data were used because the air temperature is one of the most important factors for crops growth. As shown in Fig. 4, the internal air temperature of the experimental greenhouse was observed at intervals of 1 s at nine points using air temperature sensors (SHT 71, Sensirion, Switzerland) with an error range of ±0.1 C. The 10-min averaged data of air temperature were logged and used to evaluate the optimal location of temperature sensors. The air temperature sensors were installed at the height of 0.9 m above the floor. The measured data for February 2017 and July 2017 were used for seasonal analysis. Additionally, a portable weather station was installed outside the greenhouse for monitoring the outside environment, including the wind direction, wind speed, relative humidity, and air temperature, at intervals of 10 min.
2.3.
Evaluation methods for optimal sensor placement
2.3.1.
Error-based method
For controlling the internal environment of a greenhouse, sensors should be installed at points that accurately represent the entire environment. In this study, the error-based method
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Fig. 1 e Flowchart of the experimental procedure.
Fig. 2 e Schematic of the experimental greenhouse.
was used to select the optimal combination of sensor locations for representing the entire environment inside the greenhouse (Arnesano et al., 2016), assuming that the average of the data measured by all the sensors represents the overall environment. The process of identifying the optimal sensor locations using error-based method is shown in Fig. 5. First, the reference trend was calculated by averaging the air temperature data measured by all the sensors. The combination trends were calculated by averaging the air temperature data measured at the selected sensor locations for all the combinations (2n1, n ¼ 9). The error trend of each combination was
calculated as the difference between the reference trend and the combination trend, as shown in Fig. 5(c). Finally, the combinations were ranked according to statistical indices calculated using the error trends: the average, standard deviation, outlier, and z-index. The z-index is an index for evaluating how close the distribution of error trends is to a Gaussian distribution, which is also called the normal distribution. Error trends closer to a Gaussian distribution indicate a smaller effect of external factors. The tolerance value of the air temperature data was set as 0.1 C considering the resolution of the temperature sensors.
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Fig. 3 e Vent openings, thermal screen, heat pump, air duck, and plants in the greenhouse. trend, S1 ðEi Þ is the score for the mean error, S2 ðEi Þ is the score for the standard deviation, S3 ðEi Þ is the score for the outlier, and S4 ðEi Þ is the score for the z-test.
2.3.2.
Fig. 4 e Locations of sensors for measuring the air temperature inside the greenhouse (top view). The air temperature sensors were installed at the height of 0.9 m above the floor.
The combinations were ranked according to each statistical index and then scored accordingly, as shown in Table 1. The performance index of combination ðIm Þ was calculated using the score obtained for each combination according to the ranking of the statistical indices. By maximising Im , the sensor placements were optimised based on the number of sensors. The equation for calculating the performance index of each combination is shown below. Ranking means the recommended order of sensor combinations as the optimal sensor locations. Ii ¼ S1 ðEi Þ þ S2 ðEi Þ þ S3 ðEi Þ þ S4 ðEi Þ
(1)
here, Ii is the performance index of a sensor combination i, Ei is the error between the reference trend and combination
Entropy-based method
In addition to the sensor location for monitoring the entire environment of the greenhouse, it is helpful to identify the optimal sensor location for detecting the areas with significant air temperature variations. The entropy-based method was used to evaluate sensor locations for detecting areas with significant air temperature variations, such as areas significantly affected by the external environment. Information entropy was introduced as a scientific discipline by Claude Shannon in 1948 and is a measure of the uncertainty of a probability distribution (Shannon & Weaver, 1949). Information entropy is derived from information theory, which is employed to analyse communication signals. In general, information entropy is defined as the amount of information of a particular signal. Information entropy is also a measure of disorder or uncertainty. That is, the information entropy is a measure of the uncertainty in the probability distribution PðEÞ. The entropy can be calculated for all random variables that can be analysed statistically. It can also be used as a criterion for objectively evaluating information. The information entropy can be expressed by Eq. (2). When an occurrence probability is 1 and another occurrence probability is 0, the minimum value of the information entropy is 0. When all occurrence probabilities are 1/q, the maximum value is log2 Pi ðEÞ. HðXÞ ¼
m X
Pi ðEÞlog2 Pi ðEÞ
0 HðXÞ log2 PðEÞ i¼1
HðX; YÞ ¼ HðXÞ þ HðY = XÞ
(2)
(3)
here, HðXÞ is the information entropy of the data measured by one sensor, Pi ðEÞ is the probability mass function for a variable, m is the range of the temperature data measured at a specific sensor, HðX; YÞ is the joint entropy of the data
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Fig. 5 e Data processing for the error-based method.
Table 1 e Evaluation of combination based on the ranking of the statistics. Combination ID 16 35 … 3
Ranking (R)
Statistics
Performance score (Si)
1 2 … 2n1
1st value 2nd value … Maximum value
S1(16) ¼ (2n 1 R þ 1) S2(35) ¼ (2n 1 R þ 1) … Sn2 1(3) ¼ (2n 1 R þ 1)
measured by two sensors, and HðY =XÞ is the conditional entropy of the data measured by two sensors. To optimise the combinations of sensor locations, the redundant information in the measured data should be minimised, and the amount of information of the measured data should be maximised. The optimal combination of sensor locations maximises the amount of information under specific conditions. The most sensitive location among all the sensor locations is determined as the optimal location. The internal environment of the greenhouse can be properly managed by installing sensors at the optimal locations. The total information entropy for the combinations of sensor locations was calculated using the following equation. np X p n X X T Xi ; Xj ; …Xp ¼ HðXk Þ þ … þ H Xp þ H Xi ; Xj i¼1
ðisjÞ
i¼1
j¼k
(4)
here,
n P
TðXi ; Xj ; …Xp Þ is the total entropy for the combinations
i¼1
of sensors, n is the total number of sensors in the greenhouse, p is the number of selected sensors, HðXk Þ þ … þ HðXp Þ is the sum of the entropy values at the selected sensors, and np p P P HðXi ; Xj Þ is the amount of information delivered from the i¼1 j¼k
unselected sensors to the selected sensors.
2.4.
Experimental procedures
2.4.1.
Analysis of environmental data
The basic features of the air temperature data measured during the summer and winter seasons were analysed using descriptive statistics according to the sensor locations. Any missing data were not used for the statistical analysis that was performed using the R software language. The
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distribution and standard deviation of the measured data at each point were analysed and represented as box plots. Box plots can be used to intuitively describe basic statistical indices such as the mean, first quartile, median, third quartile, and outliers. An analysis of variance (ANOVA) test and a Tukey’s Honest Significant Difference (HSD) test were conducted with p < 0.05 to evaluate the differences among the data measured at the different sensor locations. According to the results, the significant differences were indicated in the box plots. Because the thermal environment inside the study greenhouse during the summer was controlled by natural ventilation, the wind environments was also analysed using the R software language.
2.4.2.
Determination of optimal sensor placement
The error-based method was used to select the sensor location that would best represent the entire greenhouse environment. The performance indices for each sensor combination (2n 1, n ¼ 9) were calculated. Because the number of combinations was 511, R software coding was performed using parallel computation to reduce the computation time. Additionally, statistical indices such as the root mean square error (RMSE) and mean absolute percentage error (MAPE) were calculated to verify the accuracy of the measured data at the locations selected via the error-based method. The RMSE is a measure of the difference between the combination trend and the reference trend. However, there is no quantitative criterion for evaluating the RMSE. The MAPE is a measure of the predicted accuracy as a percentage of the error. Therefore, the MAPE was used to assess the accuracy of combination trends relative to the reference trend. Using these two statistical indices, the differences between the reference trend and combination trends were evaluated according to the number of installed sensors. The RMSE and MAPE were calculated using Eqs. (5) and (6), respectively.
RMSE ¼
MAPE ¼
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uN uP u ðRi Ci Þ2 ti¼1 N N 100 X Ri Ci N i¼1 Ri
(5)
(6)
here, the RMSE is expressed in C, the MAPE is expressed as a percentage (%), N is the total number of data, Ri is the value of the reference trend at a specific time, and Ci is the value of the combination trend at a specific time. The values of the total information entropy were calculated using the R software language for all the sensor combinations to identify the optimal locations for detecting areas with significant air temperature variations inside the greenhouse. The range of the air temperature distribution measured by all the sensors was divided into intervals of 0.05 C, which was the resolution of the temperature sensors and the probabilities of measuring the specific temperatures were used to calculate the information entropy. The values of the information entropy for each sensor were calculated using the probability measured at intervals of 0.05 C. The value of the total information entropy was calculated using the values of the information entropy for each sensor according to each combination
of sensors. Finally, by comparing the values of the total information entropy, the sensor combination exhibiting the highest entropy value was selected as the optimal combination of sensor locations based on the number of installed sensors.
3.
Results and discussion
3.1. data
Descriptive statistical analysis of environmental
The air temperature data measured at nine locations inside the greenhouse and one location outside the greenhouse during the summer season (July 2017) and the winter season (February 2017) were plotted with respect to time, as shown in Fig. 6. The results accompanying descriptive statistics are presented in Table 2. Because of sensor failure, there were missing data for July 2, 3, 4, and 19 and for February 13, 17, 18, and 19. Excluding the missing data, there were 3370 air temperature data for the summer period and 3411 for the winter period (measured at 10-min intervals). This amount of data was sufficient for identifying the optimal sensor locations. The average air temperature inside the greenhouse in July was close to the average outside air temperature, because the greenhouse was ventilated continuously in an attempt to minimise the thermal stress on the crop. The diurnal temperature change inside the greenhouse was larger than outside. The difference between the highest and lowest air temperatures and the standard deviation of the air temperatures inside the greenhouse were larger than those of the air temperatures outside the greenhouse. Although the mean temperature inside the greenhouse in July was 28.4 C, it often exceeded 30 C, as shown in Fig. 6(a). Considering that the optimal range of the air temperature for cultivating Irwin mangoes during the summer is 22e30 C, an additional cooling system was recommended for summer production. The outside wind conditions significantly affected the internal environment of the greenhouse in the summer because the greenhouse was naturally ventilated. Therefore, the wind conditions were analysed and described as a wind rose (Fig. 7). In the area where the greenhouse was located, the wind speed ranged between 0 and 2 m s1 43.1% of the time and exceeded 6 m s1 for 16.8% of the time. The wind directions were mainly north-northeast (NNE), northeast (NE), east-northeast (ENE), and east (E). The average wind speed was 2.7 m s1, and a calm condition was observed 37.4% of the time. Because the average wind speed in the study area was relatively high, natural ventilation was deemed appropriate for reducing the air temperature inside the greenhouse. Because the temperatures during the winter season in South Korea are relatively low for growing Irwin mangoes, the air temperature inside the greenhouse was kept higher than the outside air temperature using heat pumps. In the winter season, the difference between the highest and lowest air temperatures inside the greenhouse was large, because the greenhouse was vented for a short time during each day to control the humidity. Because the floral stage of the Irwin mangoes occurs during the winter season, the indoor temperature was kept between 8 and 20 C.
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Fig. 6 e Air temperatures measured inside and outside the greenhouse during July and February, 2017.
Table 2 e Descriptive statistics of the air temperature inside and outside of the greenhouse during July and February 2017 (inside: averages of nine sensors). Season July 2017 (summer) February 2017 (winter)
Inside Outside Inside Outside
Average air temperature ( C)
Standard deviation ( C)
Lowest air temperature ( C)
Highest air temperature ( C)
28.4 28.0 11.8 2.7
2.8 1.8 2.0 3.1
22.8 23.3 4.0 4.9
41.6 33.3 23.4 11.1
A summary of the recorded temperature data is presented in Table 3 showing the nine different sensor locations inside the greenhouse. In the summer season, the lowest average temperature (27.9 C) was measured at location P-2, and the highest average temperature (29.8 C) was measured at P-7. The standard deviations were largest at P-4, P-8, and P-9, indicating that the data variability was larger. P-4, P-8, and P-9 were located in the path of the main airflow due to wind, and were therefore more affected by the natural ventilation. The variability of the thermal environment inside the greenhouse in the winter season was higher than that in the summer season, as indicated by the higher coefficient of variation for the winter season. The average air temperatures measured at P-4, P-5, and P-6 (in the central part of the greenhouse) were lower, and the average air temperatures measured at both sides of the greenhouse were higher. Three heat pumps, each with the same capacity, were installed
inside the greenhouse. Four branch ducts were connected to the two heat pumps along the side of the greenhouse, and eight branch ducts were connected to the heat pump placed at the centre. Since more heat was supplied to the sides of the greenhouse, where fewer branch ducts were connected, the air temperature was higher along the sides of the greenhouse compared to the centre. The air temperatures at P-4, P-5, and P-6 (in the central part of the greenhouse) were relatively low. Among P-4, P5, and P-6, the lowest air temperature was measured at P-6, which was the farthest location from a heat pump.
3.2.
Need for optimal sensor placement
The ANOVA test and the Tukey HSD test were performed to determine whether the measured data were statistically different among the sensor locations. The box plots shown in
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Fig. 7 e Wind rose of the wind conditions outside the greenhouse in July 2017 (summer).
Fig. 8 describe the distribution of the air temperature data measured at each location. The different letters placed above the box plots indicate any significant difference (at p < 0.05) according to the Tukey HSD test. These different letters show that the groups were statistically different. According to the ANOVA test, there was a statistically significant difference between the air temperature measured the different locations inside the greenhouse, because the p-values were <105 for the July and February data. Based on the results of the Tukey HSD test, the sensor locations could be divided into five groups for July and four groups for February. Statistical analysis revealed significant differences in air temperatures measured at the different locations for both July and February. Therefore, it is important to select the optimal sensor location, since there may be seasonal differences in the internal environment of the greenhouse that affect the air temperature data used to control the internal environment.
The air temperature data measured by the different sensors and the average air temperature data measured by all the sensors were compared, and the RMSE and MAPE were used to estimate how well the air temperature data measured at each sensor matched the reference trend, as shown in Table 4. The RMSE and MAPE were generally smaller in July than in February, i.e., the data measured at each sensor were closer to the average data in July than in February. This is because in July, the air temperature distribution inside the greenhouse was more uniform due to the increased amount of natural ventilation. Conversely, the differences between the air temperature data measured at each sensor and the reference trend in February were relatively large, because the heat pumps were used for heating at night and natural ventilation was performed during the daytime. In July, the data measured at P-5 were the closest to the reference trend, since the RMSE and MAPE values at P-5 were the smallest (0.53 C and 1.23%, respectively). The indoor environment for the entire greenhouse was well represented (<1.23% error compared with the reference trend) by the measurements at P-5 in July. In February, the data measured at P-4 most closely represented the entire environment of the greenhouse, since the RMSE and MAPE at P-4 were the smallest (0.74 C and 4.96%, respectively). The indoor environment of the greenhouse was well represented (<5% error compared with the reference trend) by the measurements at P-4 in February. The average air temperature data measured by all the sensors inside the greenhouse and the air temperature data measured at the centre of the greenhouse on July 13, 2017 and February 4, 2017 are shown in Fig. 9. As shown in Fig. 9(a), on July 13, the internal air temperature increased as the outside air temperature increased during the daytime. The air temperature data measured at the centre of the greenhouse (P-5) differed significantly from the average air temperature data measured by all the sensors on July 13, 2017. The air temperature data measured at P-3 were more similar to the average air temperature data measured by all the sensors on July 13, 2017. On February 4, because the heat pumps were used for heating at night without ventilation, the average temperature was kept at approximately 13 C, as shown in Fig. 9(b). In the winter season, during the daytime, the air temperature inside the greenhouse dropped for a short period of time due to temporary ventilation necessary for controlling humidity and CO2 concentration. The air temperature data measured at P-4 and P-5 (centre of the greenhouse) closely matched with the
Table 3 e Descriptive statistics of the air temperature data accorded at the nine different sensor locations during July and February 2017. Location of sensor
July 2017 (summer) Average ( C) Standard deviation ( C) Coefficient of variation February 2017 (winter) Average ( C) Standard deviation ( C) Coefficient of variation
P-1
P-2
P-3
P-4
P-5
P-6
P-7
P-8
P-9
27.94 2.37 0.08
27.90 2.29 0.08
28.95 2.66 0.09
28.57 3.03 0.11
28.49 2.88 0.10
28.65 2.93 0.10
29.80 2.88 0.10
28.55 3.13 0.11
28.77 3.16 0.11
11.93 2.00 0.17
11.95 1.99 0.17
11.90 2.11 0.18
11.55 1.96 0.17
11.33 1.85 0.16
11.13 1.92 0.17
12.11 2.17 0.18
12.42 1.92 0.15
11.65 1.20 0.10
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Fig. 8 e Box plots of the measured temperature data based on the sensor locations. The letters above the box plots indicate any significant difference (at p < 0.05) according to the Tukey HSD test.
Table 4 e Root mean square error (RMSE) and mean absolute percentage error (MAPE) data for evaluating the accuracy of the air temperature data measured by each sensor with respect to the reference trend. Location of sensor P-1 July 2017 (summer) 0.97 RMSE ( C) MAPE (%) 1.99 February 2017 (winter) RMSE ( C) 1.02 MAPE (%) 5.96
P-2
P-3
P-4
P-5
P-6
P-7
P-8
P-9
1.59 2.82
0.85 2.42
0.80 1.59
0.53 1.23
0.60 1.32
0.70 1.68
1.26 2.60
0.84 1.70
1.10 6.40
1.03 6.23
0.74 4.96
0.86 5.00
0.89 5.48
0.92 5.08
1.01 6.49
1.00 5.34
average air temperature data measured by all the sensors at night. However, during the daytime, the air temperature data measured at the centre of the greenhouse (P-5) did not closely track the average air temperature data measured by all the sensors. However, the air temperature data measured at P-4 closely tracked the reference trend. Although temperature sensors are typically installed at the centre of a greenhouse for environmental control, environmental data measured at the centre of a greenhouse may not always accurately represent the entire environment of the greenhouse. Therefore, it is necessary to select appropriate sensor locations based on greenhouse design and control strategies instead of simply installing the sensor at the centre of the greenhouse. Additionally, it is necessary to quantitatively evaluate the sensor locations when sensors are installed at various locations.
3.3.
Evaluation of optimal sensor placement
The error-based method and entropy-based method were applied to the air temperature data measured at nine sensor locations to identify the optimal sensor location. For July, the optimal sensor locations according to the number of sensors are presented in Table 5. According to the error-based method, in the case of only one sensor installed in the greenhouse, P-5 was the optimal location for measuring the
air temperature inside the greenhouse. In the case of two sensors, P-1 and P-9 were the optimal locations for measuring the air temperature inside the greenhouse. The results of the entropy-based method exhibited a different tendency compared to the error-based method. This is because the locations where the data volatility was high and redundant information was minimised were preferentially selected. Because the information entropy was high at P-4, P-5, and P-7, these locations were preferentially selected. Conversely, P-1, P-2, and P3 were selected last. Because the main wind directions were NNE, NE, ENE, and E in July, P-4, P-5, and P-7 were more affected by the external wind conditions. Thus P-4, P-5, and P-7 were preferentially selected as the optimal locations for measuring the variability of the air temperature. For February, the results for the optimal sensor locations according to the number of sensors are presented in Table 6. According to the error-based method, P-4 was the optimal location for measuring the air temperature inside the greenhouse in the case when only one sensor is installed in the greenhouse. In the case of two sensors, P-1 and P-9 were the optimal locations for measuring the air temperature inside the greenhouse. In the entropy-based method, these locations were selected last because the information entropy was low at P-4, P-5, and P-6. As a result, locations at toward both sides of
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Fig. 9 e Evaluating whether the temperature data measured at the centre point accurately represents the temperature of the entire greenhouse.
Table 5 e Optimal sensor placement determined by the error-based and entropy-based methods for July 2017 (summer).
1
error-based method Location of sensor 2 3 4 5 6
7
8
9
the greenhouse were selected as the optimal sensor locations. The fluctuations of the temperature distribution at both sides of the greenhouse were larger because of the heat gain from the heat pumps and the heat loss through the greenhouse cladding.
1 Number of sensors
Number of sensors
1 2 3 4 5 6 7 8 9
entropy-based method Location of sensor 2 3 4 5 6
7
8
9
1 2 3 4 5 6 7 8 9
3.4. Evaluation of optimal sensor placement during the day and night in February In February, the greenhouse was periodically ventilated during the daytime to manage the internal environment. It was
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Table 6 e Optimal sensor placement determined by the error-based and entropy-based methods for February 2017 (winter).
1
error-based method Location of sensor 2 3 4 5 6
7
8
9
1 Number of sensors
Number of sensors
1 2 3 4 5 6 7 8 9
assumed that the inflow of outside air as a result of natural ventilation influenced the selection of the optimal sensor locations. Therefore, the optimal sensor locations were identified for the day and night time period based on the number of sensors available. The air temperature data measured at the nine locations were separated into day time (6:00e18:00) and night time (18:00e6:00). The error-based and entropy-based methods were applied to the separated data to identify the
entropy-based method Location of sensor 2 3 4 5 6
7
8
9
1 2 3 4 5 6 7 8 9
optimal sensor locations. The results for the optimal sensor locations for the day and night time periods are presented in Tables 7 and 8, respectively. The descriptive statistics of the air temperature data measured by each sensor in February during the day time and night time are presented in Table 9. The results of the error-based method exhibited similar trends for day time and night time. On the other hand, the results of the entropy-based method exhibited opposite trends for day
Table 7 e Optimal sensor placement determined by the error-based and entropy-based methods for the day time (6:00e18:00) during February 2017 (winter).
1
error-based method Location of sensor 2 3 4 5 6
7
8
9
1 Number of sensors
Number of sensors
1 2 3 4 5 6 7 8 9
entropy-based method Location of sensor 2 3 4 5 6
7
8
9
1 2 3 4 5 6 7 8 9
Table 8 e Optimal sensor placement determined by the error-based and entropy-based methods for the night time (18:00e6:00) during February 2017 (winter).
1
7
8
9
1 Number of sensors
Number of sensors
1 2 3 4 5 6 7 8 9
error-based method Location of sensor 2 3 4 5 6
1 2 3 4 5 6 7 8 9
entropy-based method Location of sensor 2 3 4 5 6
7
8
9
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Table 9 e Descriptive statistics of the air temperature data measured by each sensor during day time and night time in February 2017 (winter). Location of sensor
Day time Average ( C) Standard deviation ( C) Coefficient of variation Night time Average ( C) Standard deviation ( C) Coefficient of variation
P-1
P-2
P-3
P-4
P-5
P-6
P-7
P-8
P-9
12.4 1.96 0.16
12.4 2.09 0.17
12.4 2.09 0.17
11.8 1.85 0.16
11.4 1.57 0.14
11.3 1.8 0.16
12.6 2.0 0.16
12.2 1.98 0.16
11.5 1.99 0.17
12.1 1.33 0.11
12.0 1.03 0.09
11.7 1.42 0.12
11.8 1.50 0.13
11.6 1.52 0.13
11.6 1.58 0.14
11.8 1.28 0.11
12.3 1.31 0.11
11.9 1.63 0.14
time and night time. Table 9 shows that the standard deviations of the air temperature data measured at night were smaller than those for the daytime measurements. Additionally, the differences among the air temperature data measured by the sensors were small. Thus, the variability of the air temperature data at night was smaller than that during the day. P-4, P-5, and P-6 were selected least often for the daytime measurement, whereas they were selected as the optimal sensor locations for the night time measurement.
3.5. Verification of optimal sensor placement using statistical analysis The RMSE and MAPE were evaluated for comparing the reference trend and the combination trend for the air temperature data measured at the 1st and 2nd selected locations in July. The RMSE and MAPE values for July are presented in Table 10. A larger number of sensors yielded greater consistency between the combination trends and the reference trend, as shown in Table 10. In the case of a single sensor, the difference in accuracy between the air temperature data measured at the 1st and 2nd locations was significant. The difference in accuracy between the air temperature data measured at the 1st and 2nd locations generally decreased as the number of sensors increased. In the case of a single sensor, the RMSE and MAPE of the 1st-ranked data were calculated as 0.53 C and 1.23%, respectively. The combination
trend of the 1st-ranked data was similar to the reference trend when a single sensor was used for the 1st-ranked data. According to the results, in July, at least three sensors should be selected to ensure that the deviation between the combination trend and the reference trend is within 1%. The RMSE and MAPE were also evaluated for February, as shown in Table 11. The RMSE and MAPE for February were larger than those for July. In February, at least six sensors should be selected to ensure that the deviation between the combination trend and the reference trend is within 1%. Comparing the cases of 1, 3, and 5 sensors installed in the greenhouse in July and February, the reference trend and the combination trends of the 1st, 2nd, and 3rd-ranked data were compared, as shown in Figs. 10 and 11. Similar to the previous quantitative results, a larger number of sensors yielded greater consistency between the combination trends and the reference trend, regardless of the ranking. In the case of a single sensor, the difference between the reference trend and the combination trend was significant, even though the 1stranked sensor location was used for the combination trend. In particular, the difference between the reference trend and the combination trend was approximately 4% in February when one sensor (1st-ranked sensor location) was used. Our analysis confirmed that the air temperature data measured at the sensor locations and evaluated using the error-based method resulted in temperature close to the reference trend. Therefore, we determined error-based
Table 10 e Accuracy of the data measured at selected sensor locations with respect to the reference trend for July 2017 (summer). Number of selected sensors RMSE ( C) MAPE (%)
1st ranking 2nd ranking 1st ranking 2nd ranking
1
2
3
4
5
6
7
8
0.53 1.16 1.23 2.76
0.46 0.53 1.19 1.29
0.27 0.55 0.65 1.48
0.27 0.42 0.78 1.12
0.22 0.36 0.63 0.82
0.27 0.25 0.66 0.65
0.33 0.13 0.82 0.34
0.19 0.14 0.48 0.34
Table 11 e Accuracy of the data measured at selected sensor locations with respect to the reference trend for February 2017 (winter). Number of selected sensors RMSE ( C MAPE
1st ranking 2nd ranking 1st ranking 2nd ranking
1
2
3
4
5
6
7
8
0.74 1.47 4.96 6.08
0.60 0.51 2.55 2.26
0.29 0.36 1.38 1.61
0.42 0.32 1.93 1.39
0.34 0.36 1.55 1.51
0.34 0.15 0.67 1.41
0.15 0.21 0.64 0.88
0.19 0.14 0.76 0.52
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Fig. 10 e Accuracy of the air temperature data measured at selected locations compared to the reference trend during July (summer) based on the number of sensors. Ranking means the recommended order of sensor combinations as the optimal sensor locations.
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Fig. 11 e Accuracy of the air temperature data measured at selected locations compared to the reference trend during February (winter) based on the number of sensors. Ranking means the recommended order of sensor combinations as the optimal sensor locations.
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method suitable for selecting the locations of sensors for accurately representing the entire environment of the greenhouse. On the other hand, error-based method and entropybased method can complement each other when used together.
optimal sensor locations could also be applied to other environmental factors, such as the humidity, light, and CO2 concentration in future studies.
4.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Conclusions
The locations of air temperature sensors that could represent the entire environment of a greenhouse were evaluated using an error-based method. Additionally, the optimal sensor locations for detecting areas with significant air temperature variations were identified using an entropy-based method. In order to conduct these analyses, long-term air temperature data were recorded at various locations in the greenhouse. The optimal locations of the air temperature sensors in July and February, 2017 were analysed according to the number of sensors installed. For July, in the case where only one sensor was used to measure the entire environment of the greenhouse, P-5 (the centre of the greenhouse) was identified as the optimal sensor location. The MAPE was estimated to be 1.23% when a single sensor was installed at P-5. It was found that the sensor installed at P-5 measured the air temperature close to the reference trend. Our results also indicated that at least three sensors should be installed to ensure that the deviation between the combination trend and the reference trend is within 1%. For detecting areas with significant air temperature variations, sensors located at the windward side were preferentially selected. For February, in the case where only one sensor was used to measure the entire environment of the greenhouse, P-4 was identified as the optimal sensor location. The sensors located at both sides of the greenhouse, which were more affected by heat gain from the heat pumps and heat loss from the greenhouse cladding, were selected optimal detection of the areas with significant air temperature variations. Typically, a single temperature sensor is installed at the centre of a greenhouse, according to the experience of the designers or the growers. The results of our study are important and useful because sensor locations were quantitatively evaluated according to the number of installed sensors, using time-series data. Additionally, the accuracy of the selected sensors was evaluated using statistical analysis, and the number of sensors needed to achieve a certain level of accuracy was identified using statistical indices. The locations most affected by the external environment were selected as the optimal sensor locations for detecting areas with significant air temperature variations. Although the optimal sensor location in summer and winter were separately evaluated, growers hardly ever change the sensor location installed in greenhouse according to a change of season. However, the internal temperature data is not enough to evaluate fixed optimal sensor locations for year. If enough data is accumulated in the future, fixed sensor location can be evaluated for annual monitoring in the greenhouse. Additionally, the optimal sensor locations were identified using only the air temperature data for a greenhouse, among the various environmental factors. The methods used for identifying the
Conflict of interest
references
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wireless sensor networks. Journal of Parallel and Distributed Computing, 69(10), 838e847. Worden, K., & Burrows, A. P. (2001). Optimal sensor placement for fault detection. Engineering Structures, 23(8), 885e901.
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Research Paper
Optimal sensor placement for monitoring and controlling greenhouse internal environments Sang-yeon Lee a, In-bok Lee a,b,*, Uk-hyeon Yeo a, Rack-woo Kim a, Jun-gyu Kim a a
Department of Rural Systems Engineering, Research Institute for Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, 1, Gwanakno, Gwanakgu, Seoul, 08826, Republic of Korea b Research Institute of Green Eco Engineering, Institute of Green Bio Science and Technology, Seoul National University, 1477, Pyeongchang-daero, Daehwa-myeon, Pyeongchang-gun, Gangwon-do, 25354, Republic of Korea
article info
In large greenhouses with information and communication technology capability, envi-
Article history:
ronmental conditions can be measured and communicated to control the internal envi-
Received 11 July 2019
ronment. However, in such greenhouses, it is difficult to control the internal environment
Received in revised form
uniformly and appropriately. Additionally, there is uncertainty regarding whether the data
1 October 2019
measured at a particular location accurately represents the entire greenhouse environ-
Accepted 9 October 2019
ment. Furthermore, the locations of sensors are usually determined based on the experience of growers and the greenhouse designers. To accurately measure the internal environment of a greenhouse, it is necessary to properly select the installation locations of
Keywords:
the sensors. The objective of this study was to determine the optimal sensor placement for
Air temperature
monitoring and controlling the internal environment of a greenhouse. The study green-
Greenhouse
house was an eight-span plastic greenhouse growing Irwin mango crops. Air temperature
Monitoring
data measured at nine locations in the greenhouse were used. All of the possible combi-
Optimisation
nations of monitoring locations were evaluated, and optimal sensor placements were
Sensor placement
selected according to the number of sensors. The optimisation was conducted using two methods: error-based sensor placement and entropy-based sensor placement. Using the former approach, sensor locations for which the monitored data were close to the reference value, i.e. the average data of all the measurement locations, were selected. Using the latter approach, sensor locations influenced by the external weather conditions resulting in poor environmental control were selected. Using these methods, optimal sensor locations for representing the entire environment of the facility and for detecting areas with significant air temperature variations were determined. © 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.
* Corresponding author. 1, Gwanakno, Gwanakgu, Seoul, 08826, Republic of Korea. Fax: þ82 2 873 2087. E-mail address: [email protected] (I.-b. Lee). https://doi.org/10.1016/j.biosystemseng.2019.10.005 1537-5110/© 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.
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Nomenclature ANOVA Analysis of variance Value of the combination trend at a specific Ci time Error between the reference trend and Ei combination trend HSD Honest significant difference HðXÞ Information entropy of the data measured by one sensor HðX; YÞ Joint entropy of the data measured by two sensors HðY =XÞ Conditional entropy of the data measured by two sensors HðXk Þ þ … þ HðXp Þ Sum of the entropy values measured by the selected sensors mp p P P HðXi ; Xj Þ Amount of information delivered from i¼1 j¼k the unselected sensors to the selected sensors Performance index of a combination of sensors Ii MAPE Mean absolute percentage error m Range of the temperature data measured at a specific sensor n Number of installed sensors in the greenhouse N Total number of data p Number of selected sensors PðEÞ Uncertainty in the probability distribution Probability mass function for a variable Pi ðEÞ R Ranking RMSE Root-mean-square error Value of the reference trend at a specific time Ri Performance score for a specific combination of Si sensors Score for the mean error S1 ðEi Þ Score for the standard deviation S2 ðEi Þ Score for the outlier S3 ðEi Þ Score for the z-test S4 ðEi Þ THI Temperatureehumidity index m P TðXi ; Xj ; …Xp Þ Total entropy for the combinations of i¼1 sensors
1.
Introduction
Since the 1970s, greenhouse cultivation in South Korea has been increasing due to advantages such as improved environmental control, intensive cultivation, year-round production, and the production of high-quality agricultural products. The total greenhouse area in South Korea has increased from 700 ha in 1970 to 55,217 ha in 2017. Total production from protected cultivation (2,414,354 tonnes annually) accounted for approximately 28% of the total fruit and vegetable productions in 2017 (Ministry of Agriculture, Food and Rural Affairs of Korea, 2017). Energy-efficiency projects for energy saving in horticultural facilities and modernisation projects for applying information and communications technologies
191
to horticultural facilities were implemented by the government, supply companies, and growers. With the increase in automation of greenhouses, the area of multi-span greenhouses in South Korea has increased from 5264 ha in 2005 to 6365 ha in 2016 (Ministry of Food, Agriculture, Forestry and Fisheries, 2017). Proper environmental control of the internal condition including the air temperature, humidity, and CO2 concentration in a greenhouse are necessary to affect the growth, productivity, and quality of crops. In a large greenhouse such as an information and communication technology (ICT)-applied greenhouse or smart farm, the internal conditions are monitored using sensors to control the greenhouse environment via greenhouse actuators, such as air conditioners, circulation fans, and heat pumps. For measuring the environment inside a greenhouse, different sensors are installed and used by growers and researchers. However, growers generally install a limited number of sensors, owing to economic limitations and management challenges. Typically, the sensor locations are decided according to the experience of growers and greenhouse designers. The centre location of facilities has been generally considered as the representative location for monitoring environmental factors such as the air temperature and humidity (Feng, Li, & Zhi, 2013). However, there is uncertainty regarding whether the environment measured at the centre of facilities properly represents the entire environment inside greenhouses. Therefore, it is necessary to select optimal installation locations for the limited number of sensors to accurately monitor the internal environments of large greenhouses. Previous studies on selecting the optimal sensor location have focused on monitoring the stability of structures (Hong, Hwang, & Kim, 1996; Lee, Kim, & Lee, 2016; Lee, Kim, Park, & Jang, 2009; Ren, Yan, & Jiang, 2001; Yi, Zhou, Li, & Wang, 2017) and measuring the internal environment of specific facilities (Arnesano, Revel, & Seri, 2016; Chang, Ha, Jun, & Kang, € hner & 2012; Hu & Patel, 2014; Huang, Zhou, & Zhang, 2014; Lo Camelli, 2005; Seabrook, 2017; Wang, Ma, & Wang, 2009; Worden & Burrows, 2001). During such studies for measuring the internal environment of facilities, attempts have been made to identify the optimal sensor locations. Arnesano et al. (2016) identified the optimal locations of temperature sensors for controlling a heating, venting, and air-conditioning system in a large sport stadium using an error-based method. The optimal sensor location was selected according to the z-index, the outliers, the standard deviation, and the average. Chang et al. (2012) applied the theory of information entropy to identify the optimal combination of sensor locations for measuring the pressure in a water pipe network. Using the information entropy, sensor locations that provided the largest amount of information were selected as the optimal locations. Feng et al. (2013) simulated the internal air temperature and wind-velocity distributions of a greenhouse via computational fluid dynamics (CFD) and suggested that the optimal sensor location is where the air temperature and wind speed do not change rapidly. Liu, Chen, Lv, and Li (2014) simulated the air temperature distribution inside a mechanically ventilated greenhouse according to four environmental conditions using CFD. According to the results of the CFD simulation, the centre of the greenhouse, where the fluctuation of the internal microclimate was low, was the
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optimal sensor location. Curi et al. (2017) measured the internal air temperature and humidity of a broiler house for a total of 52 sites at the height of a chicken (0.3 m) during the summer season. Using the ordinary kriging method, which is an interpolation method, the range of the temp eratureehumidity index (THI) was calculated. The three locations where the THI was the lowest, the median, and the highest were selected as the optimal sensor locations, because the internal environment of the mechanically ventilated facility was divided into three parts: the entrance area, the centre of the facility, and the area around the exhaust fans. However, the previous studies did not consider the timeseries of the environmental data or the number of sensors that could be installed in the facilities. Furthermore, during the previous studies conducted for agricultural facilities, the optimal sensor locations were selected as those where the environmental change was the smallest or where the difference between the environments was the greatest according to the judgement of the researchers, without quantitative criteria. Although studies have been conducted to select locations that can represent the entire environment inside a facility for maintaining uniform environmental conditions, it is also necessary to install sensors at locations that are significantly affected by the unstable external environment. The objective of this study was to select the optimal sensor location for accurately controlling the entire environment of the greenhouse and for detecting areas with significant air temperature variations, such as areas that are significantly affected by the external environment. In this study, data for the air temperature, which is one of the most important factors for crop growth, were used because it is easy to simultaneously measure the air temperature at multiple locations. Seasonal air temperature data were recorded at nine locations inside an eight-span plastic greenhouse of the 1e2 W type. Using error-based and entropy-based methods, the air temperature data measured at these nine locations were analysed. Finally, the optimal sensor locations were selected according to the season and the number of sensors installed.
2.
Materials and methods
The internal air temperature of the greenhouse was measured at nine locations to determine the optimal sensor placement for monitoring and controlling the internal environment accurately. Based on long-term data measured in a greenhouse, all the combinations of sensor locations were evaluated to determine which combinations would best reflect the overall environment of the greenhouse using the error-based method. Statistical analysis was performed to evaluate the accuracy of the data measured at selected sensor locations compared with the average data measured by all of the sensors. The optimal sensor placements for monitoring and controlling the internal environment were identified according to the number of sensors installed in the greenhouse. Additionally, the optimal sensor locations that often were significantly affected by the changing external environment were identified using the entropy-based method. The experimental procedure is shown in Fig. 1.
2.1.
Study greenhouse
The experiments were performed in a greenhouse located on the west coast of South Korea in Jugyo-myeon, Boryeong City, Chungcheongnam-do Province (126 290 E, 36 230 N). The study greenhouse was the eight-span 1e2 W type greenhouse, as shown in Fig. 2. The study greenhouse was oriented northesouth. The study greenhouse had a width of 34.4 m, a length of 30.0 m, an eave height of 4.5 m, and a ridge height of 5.7 m. The internal space of the study greenhouse was divided into a workspace and cultivation space. The workspace was 4.0 m from the entrance and was used for controlling the mechanical system. The cultivation space comprised of an area of 768 m2 (32 m 24 m) and was used for cultivating Irwin mangoes. The covering of the greenhouse was made of a 0.15-mm-thick polyolefin film. The sides of the greenhouse were covered with a doublelayer polyolefin film for added insulation. Additionally, thermal curtains were installed along the sidewalls and horizontally at eave height for energy saving during the winter season. A total of 100 Irwin mango plants were cultivated in pots, as shown in Fig. 3. The diameter of each pot was 0.8 m, and the pots were arranged in 13 rows. The internal environment of the greenhouse was controlled using a natural ventilation system with side and roof vents and was heated or cooled using three heat pumps (ADFSLX12WHB, A-San Inc., Korea). The cooling and heating capacities of heat pumps were 43,276 W and 36,786 W, respectively. Because the greenhouse had natural ventilation, it was often challenging to optimally and uniformly control the internal environment of the greenhouse.
2.2. Monitoring the internal environment of the greenhouse The micro-climate in greenhouses, such as the air flow rate, air temperature, humidity, CO2 concentration, and solar radiation, determines the growth rate and quality of the crops. In this study, air temperature data were used because the air temperature is one of the most important factors for crops growth. As shown in Fig. 4, the internal air temperature of the experimental greenhouse was observed at intervals of 1 s at nine points using air temperature sensors (SHT 71, Sensirion, Switzerland) with an error range of ±0.1 C. The 10-min averaged data of air temperature were logged and used to evaluate the optimal location of temperature sensors. The air temperature sensors were installed at the height of 0.9 m above the floor. The measured data for February 2017 and July 2017 were used for seasonal analysis. Additionally, a portable weather station was installed outside the greenhouse for monitoring the outside environment, including the wind direction, wind speed, relative humidity, and air temperature, at intervals of 10 min.
2.3.
Evaluation methods for optimal sensor placement
2.3.1.
Error-based method
For controlling the internal environment of a greenhouse, sensors should be installed at points that accurately represent the entire environment. In this study, the error-based method
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Fig. 1 e Flowchart of the experimental procedure.
Fig. 2 e Schematic of the experimental greenhouse.
was used to select the optimal combination of sensor locations for representing the entire environment inside the greenhouse (Arnesano et al., 2016), assuming that the average of the data measured by all the sensors represents the overall environment. The process of identifying the optimal sensor locations using error-based method is shown in Fig. 5. First, the reference trend was calculated by averaging the air temperature data measured by all the sensors. The combination trends were calculated by averaging the air temperature data measured at the selected sensor locations for all the combinations (2n1, n ¼ 9). The error trend of each combination was
calculated as the difference between the reference trend and the combination trend, as shown in Fig. 5(c). Finally, the combinations were ranked according to statistical indices calculated using the error trends: the average, standard deviation, outlier, and z-index. The z-index is an index for evaluating how close the distribution of error trends is to a Gaussian distribution, which is also called the normal distribution. Error trends closer to a Gaussian distribution indicate a smaller effect of external factors. The tolerance value of the air temperature data was set as 0.1 C considering the resolution of the temperature sensors.
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Fig. 3 e Vent openings, thermal screen, heat pump, air duck, and plants in the greenhouse. trend, S1 ðEi Þ is the score for the mean error, S2 ðEi Þ is the score for the standard deviation, S3 ðEi Þ is the score for the outlier, and S4 ðEi Þ is the score for the z-test.
2.3.2.
Fig. 4 e Locations of sensors for measuring the air temperature inside the greenhouse (top view). The air temperature sensors were installed at the height of 0.9 m above the floor.
The combinations were ranked according to each statistical index and then scored accordingly, as shown in Table 1. The performance index of combination ðIm Þ was calculated using the score obtained for each combination according to the ranking of the statistical indices. By maximising Im , the sensor placements were optimised based on the number of sensors. The equation for calculating the performance index of each combination is shown below. Ranking means the recommended order of sensor combinations as the optimal sensor locations. Ii ¼ S1 ðEi Þ þ S2 ðEi Þ þ S3 ðEi Þ þ S4 ðEi Þ
(1)
here, Ii is the performance index of a sensor combination i, Ei is the error between the reference trend and combination
Entropy-based method
In addition to the sensor location for monitoring the entire environment of the greenhouse, it is helpful to identify the optimal sensor location for detecting the areas with significant air temperature variations. The entropy-based method was used to evaluate sensor locations for detecting areas with significant air temperature variations, such as areas significantly affected by the external environment. Information entropy was introduced as a scientific discipline by Claude Shannon in 1948 and is a measure of the uncertainty of a probability distribution (Shannon & Weaver, 1949). Information entropy is derived from information theory, which is employed to analyse communication signals. In general, information entropy is defined as the amount of information of a particular signal. Information entropy is also a measure of disorder or uncertainty. That is, the information entropy is a measure of the uncertainty in the probability distribution PðEÞ. The entropy can be calculated for all random variables that can be analysed statistically. It can also be used as a criterion for objectively evaluating information. The information entropy can be expressed by Eq. (2). When an occurrence probability is 1 and another occurrence probability is 0, the minimum value of the information entropy is 0. When all occurrence probabilities are 1/q, the maximum value is log2 Pi ðEÞ. HðXÞ ¼
m X
Pi ðEÞlog2 Pi ðEÞ
0 HðXÞ log2 PðEÞ i¼1
HðX; YÞ ¼ HðXÞ þ HðY = XÞ
(2)
(3)
here, HðXÞ is the information entropy of the data measured by one sensor, Pi ðEÞ is the probability mass function for a variable, m is the range of the temperature data measured at a specific sensor, HðX; YÞ is the joint entropy of the data
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Fig. 5 e Data processing for the error-based method.
Table 1 e Evaluation of combination based on the ranking of the statistics. Combination ID 16 35 … 3
Ranking (R)
Statistics
Performance score (Si)
1 2 … 2n1
1st value 2nd value … Maximum value
S1(16) ¼ (2n 1 R þ 1) S2(35) ¼ (2n 1 R þ 1) … Sn2 1(3) ¼ (2n 1 R þ 1)
measured by two sensors, and HðY =XÞ is the conditional entropy of the data measured by two sensors. To optimise the combinations of sensor locations, the redundant information in the measured data should be minimised, and the amount of information of the measured data should be maximised. The optimal combination of sensor locations maximises the amount of information under specific conditions. The most sensitive location among all the sensor locations is determined as the optimal location. The internal environment of the greenhouse can be properly managed by installing sensors at the optimal locations. The total information entropy for the combinations of sensor locations was calculated using the following equation. np X p n X X T Xi ; Xj ; …Xp ¼ HðXk Þ þ … þ H Xp þ H Xi ; Xj i¼1
ðisjÞ
i¼1
j¼k
(4)
here,
n P
TðXi ; Xj ; …Xp Þ is the total entropy for the combinations
i¼1
of sensors, n is the total number of sensors in the greenhouse, p is the number of selected sensors, HðXk Þ þ … þ HðXp Þ is the sum of the entropy values at the selected sensors, and np p P P HðXi ; Xj Þ is the amount of information delivered from the i¼1 j¼k
unselected sensors to the selected sensors.
2.4.
Experimental procedures
2.4.1.
Analysis of environmental data
The basic features of the air temperature data measured during the summer and winter seasons were analysed using descriptive statistics according to the sensor locations. Any missing data were not used for the statistical analysis that was performed using the R software language. The
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distribution and standard deviation of the measured data at each point were analysed and represented as box plots. Box plots can be used to intuitively describe basic statistical indices such as the mean, first quartile, median, third quartile, and outliers. An analysis of variance (ANOVA) test and a Tukey’s Honest Significant Difference (HSD) test were conducted with p < 0.05 to evaluate the differences among the data measured at the different sensor locations. According to the results, the significant differences were indicated in the box plots. Because the thermal environment inside the study greenhouse during the summer was controlled by natural ventilation, the wind environments was also analysed using the R software language.
2.4.2.
Determination of optimal sensor placement
The error-based method was used to select the sensor location that would best represent the entire greenhouse environment. The performance indices for each sensor combination (2n 1, n ¼ 9) were calculated. Because the number of combinations was 511, R software coding was performed using parallel computation to reduce the computation time. Additionally, statistical indices such as the root mean square error (RMSE) and mean absolute percentage error (MAPE) were calculated to verify the accuracy of the measured data at the locations selected via the error-based method. The RMSE is a measure of the difference between the combination trend and the reference trend. However, there is no quantitative criterion for evaluating the RMSE. The MAPE is a measure of the predicted accuracy as a percentage of the error. Therefore, the MAPE was used to assess the accuracy of combination trends relative to the reference trend. Using these two statistical indices, the differences between the reference trend and combination trends were evaluated according to the number of installed sensors. The RMSE and MAPE were calculated using Eqs. (5) and (6), respectively.
RMSE ¼
MAPE ¼
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uN uP u ðRi Ci Þ2 ti¼1 N N 100 X Ri Ci N i¼1 Ri
(5)
(6)
here, the RMSE is expressed in C, the MAPE is expressed as a percentage (%), N is the total number of data, Ri is the value of the reference trend at a specific time, and Ci is the value of the combination trend at a specific time. The values of the total information entropy were calculated using the R software language for all the sensor combinations to identify the optimal locations for detecting areas with significant air temperature variations inside the greenhouse. The range of the air temperature distribution measured by all the sensors was divided into intervals of 0.05 C, which was the resolution of the temperature sensors and the probabilities of measuring the specific temperatures were used to calculate the information entropy. The values of the information entropy for each sensor were calculated using the probability measured at intervals of 0.05 C. The value of the total information entropy was calculated using the values of the information entropy for each sensor according to each combination
of sensors. Finally, by comparing the values of the total information entropy, the sensor combination exhibiting the highest entropy value was selected as the optimal combination of sensor locations based on the number of installed sensors.
3.
Results and discussion
3.1. data
Descriptive statistical analysis of environmental
The air temperature data measured at nine locations inside the greenhouse and one location outside the greenhouse during the summer season (July 2017) and the winter season (February 2017) were plotted with respect to time, as shown in Fig. 6. The results accompanying descriptive statistics are presented in Table 2. Because of sensor failure, there were missing data for July 2, 3, 4, and 19 and for February 13, 17, 18, and 19. Excluding the missing data, there were 3370 air temperature data for the summer period and 3411 for the winter period (measured at 10-min intervals). This amount of data was sufficient for identifying the optimal sensor locations. The average air temperature inside the greenhouse in July was close to the average outside air temperature, because the greenhouse was ventilated continuously in an attempt to minimise the thermal stress on the crop. The diurnal temperature change inside the greenhouse was larger than outside. The difference between the highest and lowest air temperatures and the standard deviation of the air temperatures inside the greenhouse were larger than those of the air temperatures outside the greenhouse. Although the mean temperature inside the greenhouse in July was 28.4 C, it often exceeded 30 C, as shown in Fig. 6(a). Considering that the optimal range of the air temperature for cultivating Irwin mangoes during the summer is 22e30 C, an additional cooling system was recommended for summer production. The outside wind conditions significantly affected the internal environment of the greenhouse in the summer because the greenhouse was naturally ventilated. Therefore, the wind conditions were analysed and described as a wind rose (Fig. 7). In the area where the greenhouse was located, the wind speed ranged between 0 and 2 m s1 43.1% of the time and exceeded 6 m s1 for 16.8% of the time. The wind directions were mainly north-northeast (NNE), northeast (NE), east-northeast (ENE), and east (E). The average wind speed was 2.7 m s1, and a calm condition was observed 37.4% of the time. Because the average wind speed in the study area was relatively high, natural ventilation was deemed appropriate for reducing the air temperature inside the greenhouse. Because the temperatures during the winter season in South Korea are relatively low for growing Irwin mangoes, the air temperature inside the greenhouse was kept higher than the outside air temperature using heat pumps. In the winter season, the difference between the highest and lowest air temperatures inside the greenhouse was large, because the greenhouse was vented for a short time during each day to control the humidity. Because the floral stage of the Irwin mangoes occurs during the winter season, the indoor temperature was kept between 8 and 20 C.
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Fig. 6 e Air temperatures measured inside and outside the greenhouse during July and February, 2017.
Table 2 e Descriptive statistics of the air temperature inside and outside of the greenhouse during July and February 2017 (inside: averages of nine sensors). Season July 2017 (summer) February 2017 (winter)
Inside Outside Inside Outside
Average air temperature ( C)
Standard deviation ( C)
Lowest air temperature ( C)
Highest air temperature ( C)
28.4 28.0 11.8 2.7
2.8 1.8 2.0 3.1
22.8 23.3 4.0 4.9
41.6 33.3 23.4 11.1
A summary of the recorded temperature data is presented in Table 3 showing the nine different sensor locations inside the greenhouse. In the summer season, the lowest average temperature (27.9 C) was measured at location P-2, and the highest average temperature (29.8 C) was measured at P-7. The standard deviations were largest at P-4, P-8, and P-9, indicating that the data variability was larger. P-4, P-8, and P-9 were located in the path of the main airflow due to wind, and were therefore more affected by the natural ventilation. The variability of the thermal environment inside the greenhouse in the winter season was higher than that in the summer season, as indicated by the higher coefficient of variation for the winter season. The average air temperatures measured at P-4, P-5, and P-6 (in the central part of the greenhouse) were lower, and the average air temperatures measured at both sides of the greenhouse were higher. Three heat pumps, each with the same capacity, were installed
inside the greenhouse. Four branch ducts were connected to the two heat pumps along the side of the greenhouse, and eight branch ducts were connected to the heat pump placed at the centre. Since more heat was supplied to the sides of the greenhouse, where fewer branch ducts were connected, the air temperature was higher along the sides of the greenhouse compared to the centre. The air temperatures at P-4, P-5, and P-6 (in the central part of the greenhouse) were relatively low. Among P-4, P5, and P-6, the lowest air temperature was measured at P-6, which was the farthest location from a heat pump.
3.2.
Need for optimal sensor placement
The ANOVA test and the Tukey HSD test were performed to determine whether the measured data were statistically different among the sensor locations. The box plots shown in
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Fig. 7 e Wind rose of the wind conditions outside the greenhouse in July 2017 (summer).
Fig. 8 describe the distribution of the air temperature data measured at each location. The different letters placed above the box plots indicate any significant difference (at p < 0.05) according to the Tukey HSD test. These different letters show that the groups were statistically different. According to the ANOVA test, there was a statistically significant difference between the air temperature measured the different locations inside the greenhouse, because the p-values were <105 for the July and February data. Based on the results of the Tukey HSD test, the sensor locations could be divided into five groups for July and four groups for February. Statistical analysis revealed significant differences in air temperatures measured at the different locations for both July and February. Therefore, it is important to select the optimal sensor location, since there may be seasonal differences in the internal environment of the greenhouse that affect the air temperature data used to control the internal environment.
The air temperature data measured by the different sensors and the average air temperature data measured by all the sensors were compared, and the RMSE and MAPE were used to estimate how well the air temperature data measured at each sensor matched the reference trend, as shown in Table 4. The RMSE and MAPE were generally smaller in July than in February, i.e., the data measured at each sensor were closer to the average data in July than in February. This is because in July, the air temperature distribution inside the greenhouse was more uniform due to the increased amount of natural ventilation. Conversely, the differences between the air temperature data measured at each sensor and the reference trend in February were relatively large, because the heat pumps were used for heating at night and natural ventilation was performed during the daytime. In July, the data measured at P-5 were the closest to the reference trend, since the RMSE and MAPE values at P-5 were the smallest (0.53 C and 1.23%, respectively). The indoor environment for the entire greenhouse was well represented (<1.23% error compared with the reference trend) by the measurements at P-5 in July. In February, the data measured at P-4 most closely represented the entire environment of the greenhouse, since the RMSE and MAPE at P-4 were the smallest (0.74 C and 4.96%, respectively). The indoor environment of the greenhouse was well represented (<5% error compared with the reference trend) by the measurements at P-4 in February. The average air temperature data measured by all the sensors inside the greenhouse and the air temperature data measured at the centre of the greenhouse on July 13, 2017 and February 4, 2017 are shown in Fig. 9. As shown in Fig. 9(a), on July 13, the internal air temperature increased as the outside air temperature increased during the daytime. The air temperature data measured at the centre of the greenhouse (P-5) differed significantly from the average air temperature data measured by all the sensors on July 13, 2017. The air temperature data measured at P-3 were more similar to the average air temperature data measured by all the sensors on July 13, 2017. On February 4, because the heat pumps were used for heating at night without ventilation, the average temperature was kept at approximately 13 C, as shown in Fig. 9(b). In the winter season, during the daytime, the air temperature inside the greenhouse dropped for a short period of time due to temporary ventilation necessary for controlling humidity and CO2 concentration. The air temperature data measured at P-4 and P-5 (centre of the greenhouse) closely matched with the
Table 3 e Descriptive statistics of the air temperature data accorded at the nine different sensor locations during July and February 2017. Location of sensor
July 2017 (summer) Average ( C) Standard deviation ( C) Coefficient of variation February 2017 (winter) Average ( C) Standard deviation ( C) Coefficient of variation
P-1
P-2
P-3
P-4
P-5
P-6
P-7
P-8
P-9
27.94 2.37 0.08
27.90 2.29 0.08
28.95 2.66 0.09
28.57 3.03 0.11
28.49 2.88 0.10
28.65 2.93 0.10
29.80 2.88 0.10
28.55 3.13 0.11
28.77 3.16 0.11
11.93 2.00 0.17
11.95 1.99 0.17
11.90 2.11 0.18
11.55 1.96 0.17
11.33 1.85 0.16
11.13 1.92 0.17
12.11 2.17 0.18
12.42 1.92 0.15
11.65 1.20 0.10
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Fig. 8 e Box plots of the measured temperature data based on the sensor locations. The letters above the box plots indicate any significant difference (at p < 0.05) according to the Tukey HSD test.
Table 4 e Root mean square error (RMSE) and mean absolute percentage error (MAPE) data for evaluating the accuracy of the air temperature data measured by each sensor with respect to the reference trend. Location of sensor P-1 July 2017 (summer) 0.97 RMSE ( C) MAPE (%) 1.99 February 2017 (winter) RMSE ( C) 1.02 MAPE (%) 5.96
P-2
P-3
P-4
P-5
P-6
P-7
P-8
P-9
1.59 2.82
0.85 2.42
0.80 1.59
0.53 1.23
0.60 1.32
0.70 1.68
1.26 2.60
0.84 1.70
1.10 6.40
1.03 6.23
0.74 4.96
0.86 5.00
0.89 5.48
0.92 5.08
1.01 6.49
1.00 5.34
average air temperature data measured by all the sensors at night. However, during the daytime, the air temperature data measured at the centre of the greenhouse (P-5) did not closely track the average air temperature data measured by all the sensors. However, the air temperature data measured at P-4 closely tracked the reference trend. Although temperature sensors are typically installed at the centre of a greenhouse for environmental control, environmental data measured at the centre of a greenhouse may not always accurately represent the entire environment of the greenhouse. Therefore, it is necessary to select appropriate sensor locations based on greenhouse design and control strategies instead of simply installing the sensor at the centre of the greenhouse. Additionally, it is necessary to quantitatively evaluate the sensor locations when sensors are installed at various locations.
3.3.
Evaluation of optimal sensor placement
The error-based method and entropy-based method were applied to the air temperature data measured at nine sensor locations to identify the optimal sensor location. For July, the optimal sensor locations according to the number of sensors are presented in Table 5. According to the error-based method, in the case of only one sensor installed in the greenhouse, P-5 was the optimal location for measuring the
air temperature inside the greenhouse. In the case of two sensors, P-1 and P-9 were the optimal locations for measuring the air temperature inside the greenhouse. The results of the entropy-based method exhibited a different tendency compared to the error-based method. This is because the locations where the data volatility was high and redundant information was minimised were preferentially selected. Because the information entropy was high at P-4, P-5, and P-7, these locations were preferentially selected. Conversely, P-1, P-2, and P3 were selected last. Because the main wind directions were NNE, NE, ENE, and E in July, P-4, P-5, and P-7 were more affected by the external wind conditions. Thus P-4, P-5, and P-7 were preferentially selected as the optimal locations for measuring the variability of the air temperature. For February, the results for the optimal sensor locations according to the number of sensors are presented in Table 6. According to the error-based method, P-4 was the optimal location for measuring the air temperature inside the greenhouse in the case when only one sensor is installed in the greenhouse. In the case of two sensors, P-1 and P-9 were the optimal locations for measuring the air temperature inside the greenhouse. In the entropy-based method, these locations were selected last because the information entropy was low at P-4, P-5, and P-6. As a result, locations at toward both sides of
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Fig. 9 e Evaluating whether the temperature data measured at the centre point accurately represents the temperature of the entire greenhouse.
Table 5 e Optimal sensor placement determined by the error-based and entropy-based methods for July 2017 (summer).
1
error-based method Location of sensor 2 3 4 5 6
7
8
9
the greenhouse were selected as the optimal sensor locations. The fluctuations of the temperature distribution at both sides of the greenhouse were larger because of the heat gain from the heat pumps and the heat loss through the greenhouse cladding.
1 Number of sensors
Number of sensors
1 2 3 4 5 6 7 8 9
entropy-based method Location of sensor 2 3 4 5 6
7
8
9
1 2 3 4 5 6 7 8 9
3.4. Evaluation of optimal sensor placement during the day and night in February In February, the greenhouse was periodically ventilated during the daytime to manage the internal environment. It was
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Table 6 e Optimal sensor placement determined by the error-based and entropy-based methods for February 2017 (winter).
1
error-based method Location of sensor 2 3 4 5 6
7
8
9
1 Number of sensors
Number of sensors
1 2 3 4 5 6 7 8 9
assumed that the inflow of outside air as a result of natural ventilation influenced the selection of the optimal sensor locations. Therefore, the optimal sensor locations were identified for the day and night time period based on the number of sensors available. The air temperature data measured at the nine locations were separated into day time (6:00e18:00) and night time (18:00e6:00). The error-based and entropy-based methods were applied to the separated data to identify the
entropy-based method Location of sensor 2 3 4 5 6
7
8
9
1 2 3 4 5 6 7 8 9
optimal sensor locations. The results for the optimal sensor locations for the day and night time periods are presented in Tables 7 and 8, respectively. The descriptive statistics of the air temperature data measured by each sensor in February during the day time and night time are presented in Table 9. The results of the error-based method exhibited similar trends for day time and night time. On the other hand, the results of the entropy-based method exhibited opposite trends for day
Table 7 e Optimal sensor placement determined by the error-based and entropy-based methods for the day time (6:00e18:00) during February 2017 (winter).
1
error-based method Location of sensor 2 3 4 5 6
7
8
9
1 Number of sensors
Number of sensors
1 2 3 4 5 6 7 8 9
entropy-based method Location of sensor 2 3 4 5 6
7
8
9
1 2 3 4 5 6 7 8 9
Table 8 e Optimal sensor placement determined by the error-based and entropy-based methods for the night time (18:00e6:00) during February 2017 (winter).
1
7
8
9
1 Number of sensors
Number of sensors
1 2 3 4 5 6 7 8 9
error-based method Location of sensor 2 3 4 5 6
1 2 3 4 5 6 7 8 9
entropy-based method Location of sensor 2 3 4 5 6
7
8
9
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Table 9 e Descriptive statistics of the air temperature data measured by each sensor during day time and night time in February 2017 (winter). Location of sensor
Day time Average ( C) Standard deviation ( C) Coefficient of variation Night time Average ( C) Standard deviation ( C) Coefficient of variation
P-1
P-2
P-3
P-4
P-5
P-6
P-7
P-8
P-9
12.4 1.96 0.16
12.4 2.09 0.17
12.4 2.09 0.17
11.8 1.85 0.16
11.4 1.57 0.14
11.3 1.8 0.16
12.6 2.0 0.16
12.2 1.98 0.16
11.5 1.99 0.17
12.1 1.33 0.11
12.0 1.03 0.09
11.7 1.42 0.12
11.8 1.50 0.13
11.6 1.52 0.13
11.6 1.58 0.14
11.8 1.28 0.11
12.3 1.31 0.11
11.9 1.63 0.14
time and night time. Table 9 shows that the standard deviations of the air temperature data measured at night were smaller than those for the daytime measurements. Additionally, the differences among the air temperature data measured by the sensors were small. Thus, the variability of the air temperature data at night was smaller than that during the day. P-4, P-5, and P-6 were selected least often for the daytime measurement, whereas they were selected as the optimal sensor locations for the night time measurement.
3.5. Verification of optimal sensor placement using statistical analysis The RMSE and MAPE were evaluated for comparing the reference trend and the combination trend for the air temperature data measured at the 1st and 2nd selected locations in July. The RMSE and MAPE values for July are presented in Table 10. A larger number of sensors yielded greater consistency between the combination trends and the reference trend, as shown in Table 10. In the case of a single sensor, the difference in accuracy between the air temperature data measured at the 1st and 2nd locations was significant. The difference in accuracy between the air temperature data measured at the 1st and 2nd locations generally decreased as the number of sensors increased. In the case of a single sensor, the RMSE and MAPE of the 1st-ranked data were calculated as 0.53 C and 1.23%, respectively. The combination
trend of the 1st-ranked data was similar to the reference trend when a single sensor was used for the 1st-ranked data. According to the results, in July, at least three sensors should be selected to ensure that the deviation between the combination trend and the reference trend is within 1%. The RMSE and MAPE were also evaluated for February, as shown in Table 11. The RMSE and MAPE for February were larger than those for July. In February, at least six sensors should be selected to ensure that the deviation between the combination trend and the reference trend is within 1%. Comparing the cases of 1, 3, and 5 sensors installed in the greenhouse in July and February, the reference trend and the combination trends of the 1st, 2nd, and 3rd-ranked data were compared, as shown in Figs. 10 and 11. Similar to the previous quantitative results, a larger number of sensors yielded greater consistency between the combination trends and the reference trend, regardless of the ranking. In the case of a single sensor, the difference between the reference trend and the combination trend was significant, even though the 1stranked sensor location was used for the combination trend. In particular, the difference between the reference trend and the combination trend was approximately 4% in February when one sensor (1st-ranked sensor location) was used. Our analysis confirmed that the air temperature data measured at the sensor locations and evaluated using the error-based method resulted in temperature close to the reference trend. Therefore, we determined error-based
Table 10 e Accuracy of the data measured at selected sensor locations with respect to the reference trend for July 2017 (summer). Number of selected sensors RMSE ( C) MAPE (%)
1st ranking 2nd ranking 1st ranking 2nd ranking
1
2
3
4
5
6
7
8
0.53 1.16 1.23 2.76
0.46 0.53 1.19 1.29
0.27 0.55 0.65 1.48
0.27 0.42 0.78 1.12
0.22 0.36 0.63 0.82
0.27 0.25 0.66 0.65
0.33 0.13 0.82 0.34
0.19 0.14 0.48 0.34
Table 11 e Accuracy of the data measured at selected sensor locations with respect to the reference trend for February 2017 (winter). Number of selected sensors RMSE ( C MAPE
1st ranking 2nd ranking 1st ranking 2nd ranking
1
2
3
4
5
6
7
8
0.74 1.47 4.96 6.08
0.60 0.51 2.55 2.26
0.29 0.36 1.38 1.61
0.42 0.32 1.93 1.39
0.34 0.36 1.55 1.51
0.34 0.15 0.67 1.41
0.15 0.21 0.64 0.88
0.19 0.14 0.76 0.52
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Fig. 10 e Accuracy of the air temperature data measured at selected locations compared to the reference trend during July (summer) based on the number of sensors. Ranking means the recommended order of sensor combinations as the optimal sensor locations.
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Fig. 11 e Accuracy of the air temperature data measured at selected locations compared to the reference trend during February (winter) based on the number of sensors. Ranking means the recommended order of sensor combinations as the optimal sensor locations.
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205
method suitable for selecting the locations of sensors for accurately representing the entire environment of the greenhouse. On the other hand, error-based method and entropybased method can complement each other when used together.
optimal sensor locations could also be applied to other environmental factors, such as the humidity, light, and CO2 concentration in future studies.
4.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Conclusions
The locations of air temperature sensors that could represent the entire environment of a greenhouse were evaluated using an error-based method. Additionally, the optimal sensor locations for detecting areas with significant air temperature variations were identified using an entropy-based method. In order to conduct these analyses, long-term air temperature data were recorded at various locations in the greenhouse. The optimal locations of the air temperature sensors in July and February, 2017 were analysed according to the number of sensors installed. For July, in the case where only one sensor was used to measure the entire environment of the greenhouse, P-5 (the centre of the greenhouse) was identified as the optimal sensor location. The MAPE was estimated to be 1.23% when a single sensor was installed at P-5. It was found that the sensor installed at P-5 measured the air temperature close to the reference trend. Our results also indicated that at least three sensors should be installed to ensure that the deviation between the combination trend and the reference trend is within 1%. For detecting areas with significant air temperature variations, sensors located at the windward side were preferentially selected. For February, in the case where only one sensor was used to measure the entire environment of the greenhouse, P-4 was identified as the optimal sensor location. The sensors located at both sides of the greenhouse, which were more affected by heat gain from the heat pumps and heat loss from the greenhouse cladding, were selected optimal detection of the areas with significant air temperature variations. Typically, a single temperature sensor is installed at the centre of a greenhouse, according to the experience of the designers or the growers. The results of our study are important and useful because sensor locations were quantitatively evaluated according to the number of installed sensors, using time-series data. Additionally, the accuracy of the selected sensors was evaluated using statistical analysis, and the number of sensors needed to achieve a certain level of accuracy was identified using statistical indices. The locations most affected by the external environment were selected as the optimal sensor locations for detecting areas with significant air temperature variations. Although the optimal sensor location in summer and winter were separately evaluated, growers hardly ever change the sensor location installed in greenhouse according to a change of season. However, the internal temperature data is not enough to evaluate fixed optimal sensor locations for year. If enough data is accumulated in the future, fixed sensor location can be evaluated for annual monitoring in the greenhouse. Additionally, the optimal sensor locations were identified using only the air temperature data for a greenhouse, among the various environmental factors. The methods used for identifying the
Conflict of interest
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