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Automatic irrigation system with rain fall detection in agricultural field
Journal Pre-proofs Automatic Irrigation System with Rain Fall Detection in Agricultural Field S.R. Barkunan, V. Bhanumathi, V. Balakrishnan PII: DOI: Reference:
S0263-2241(20)30089-0 https://doi.org/10.1016/j.measurement.2020.107552 MEASUR 107552
To appear in:
Measurement
Received Date: Revised Date: Accepted Date:
28 April 2017 18 December 2019 26 January 2020
Please cite this article as: S.R. Barkunan, V. Bhanumathi, V. Balakrishnan, Automatic Irrigation System with Rain Fall Detection in Agricultural Field, Measurement (2020), doi: https://doi.org/10.1016/j.measurement. 2020.107552
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Automatic Irrigation System with Rain Fall Detection in Agricultural Field Barkunan.S.R1, Bhanumathi.V2, Balakrishnan.V3 1Assistant Professor, [email protected], 2Assistant Professor, [email protected], 3PG Scholar, [email protected] Department of ECE, Akshaya college of Engineering and Technology, Coimbatore,1 Department of ECE, Anna University, Regional Campus, Coimbatore, 2,3
ABSTRACT: Rainfall is an important natural phenomenon for agricultural activities to fulfill its water requirements. The proposed irrigation technique in agriculture saves water by making it as an automated one. By detecting the rain fall in real time, the amount of water needed for the field can be planned. A system is developed based on ARM micro controller combined with GSM module to inform the rain fall level to the farmer and as well as automatically regulates the water irrigation. The system can monitor the current state of the land and data is transmitted to the mobile. The results obtained from the prototype are compared with the actual data taken from the web and it is found that the difference in estimation is minimum. The results from the prototype are compared with the traditional systems and it is found that the automation reveals best results in terms of water utilization. Keywords— ARM Microcontroller, GSM, Rain Detector, Soil Moisture Sensor, Humidity Sensor, Temperature Sensor, Android app.
1. Introduction Rain fall monitoring is one of the important activities in automatic irrigation of agriculture. It is known that the hydrological factor affecting the total productivity of the agriculture is rain fall. To reduce the over irrigation and save the water resource the rain fall monitoring system is very much needed. In general, the arid and semi-arid agricultural areas are very closely dependent on the rainfall level for increasing the growth rate of the agricultural production and minimizing the water resource usages. The rainfall is the important phenomenon for cultivating the crop in the agricultural fields. In [1], the authors proposed the soft capacitive handy sensor to monitor the rain fall in the agricultural catchment and record the intensity of rain fall and water level. In [2], the authors discussed a method to increase the rain water usage by reducing runoff and erosion of cabo Verdi dry land. In [3], two different types of rain gauge are compared namely drop counter catching-type gauge and a tipping-bucket rain intensity gauge with two correction algorithm. The Ogawa catching type drop counter is used in [4] to measure the real world rain fall data and these data are taken as references for artificial rain generating system. The dynamic behavior of RI gauge is improved by including the laboratory simulation results of real world data. Interrupt driven Wireless Sensor Network (WSN) prototype has been designed in [7] to monitor the large scale and real time rain fall. Several autonomous nodes are deployed in smart irrigation decision support system for sensing the climatic variables and soil moisture level to control the irrigation [13]. The sensors are playing vital role in several automation applications such as vineyard hail protection [19], smart city applications [21] and to develop the remote control system for optimize the power utilization of street lights in the city [18], [20], [17]. Solar energy operated nodes used WSN is proposed in [14] to collect multiple parameters of plant, soil and atmosphere. The measured data are sent to remote server which contains sensor information in database and permits future utilization of data in simple way. Power optimized electronic system design is proposed in [8] to detect the wind direction and rain fall. A low cost WSN system is proposed in [9] for monitoring
the regular changes in crops due to pests, soil moisture droughts and floods in agricultural land. In [15], the authors proposed a system having plumbed rainwater tanks in houses for adequate water management. It monitors the real rain fall data for a period of twelve months. Software design has been proposed in [10] to regulate the sprinkler through click and play menu in GUI according to measured input field data. A low cost microcontroller based prototype system is used to monitor the soil, canopy, and air temperature, and soil moisture status in cropped fields. The PIC16F88 microcontroller used in this prototype system and the data are collected throughout the growing season [5]. In [6], the authors proposed a Dual probe heat pulse sensor for measuring water content and thermal properties of soil. A sub optimal irrigation scheduling is proposed in [11] by imperfect forecast values of weather and it can be implemented in real time. A WSN is proposed in [12] to control the water irrigation for the horticulture land. In this four nodes are used to monitor the environmental parameters and real time control has been performed by central office in the form. WSN is playing an important role in precision agriculture and it is understood that the WSN can be implemented for large agricultural lands by utilizing the clustering [22, 23] of sensor nodes. In [16], the authors proposed a distributed wireless sensor network based irrigation system with remotely connected sprinklers to the server at base station through wireless communication. A smart sensor based drip irrigation system for paddy cultivation was proposed in [24] which the soil image captured by the smartphone is utilized for finding the soil wetness and the environmental parameters such as temperature and humidity values are derived from the sensors. The automatic irrigation system proposed in the present paper for agricultural land is mainly based on sensing the environmental parameters and the real time rain fall status which is found to be playing a major role in controlling the usage of the water resources.
The rest of the paper is organized as follows: section 2 describes the working methodology of the proposed irrigation system. The detailed results and discussion are given in the section 3. Finally, section 4 concludes the work. 2. Proposed Irrigation System 2.1 Block diagram of Proposed Irrigation system The block diagram of the proposed automatic irrigation control system is shown in Figure 1. It consists of ARM 7 microcontroller as its main part. The temperature and humidity sensor is interfaced with the ARM 7 microcontroller for measuring the temperature and humidity values of the agricultural field. The rain detector is connected with ARM controller which is used to find out the real time rain fall and the moisture content of soil is measured by the soil moisture sensor according to the rain fall status of the land. Real time data measured by the corresponding sensors are used to control the irrigation pump motor as ON or OFF and the information is sent to the farmer’s mobile phone through GSM module.
Figure1. Block diagram of automatic irrigation system
2.2 Components used 2.2.1 ARM 7 Microcontroller The LPC2148 microcontroller is based on a 32/16 bit ARM7 TDMI CPU. The microcontroller consists of an embedded high speed flash memory ranging from 32kB to 512kB with real-time emulation and embedded trace support. By using maximum clock rate in the unique accelerator, the architecture enables the 32-bit code execution with 128-bit wide memory interface. The serial communication interfaces like USB, SPI, SSP, I2C and UART are present in the controller for effective data transmission. The microcontroller is having an in-built multiple 32-bit timers, 10-bit ADC, 10-bit Digital to Analog Converter (DAC), PWM channels and 45 fast GPIO lines with up to nine edge or level sensitive external interrupt pins. The real time clock and watchdog timers are present in the controller to manage the indefinite loop problems that occur during the time of execution. 2.2.2 Temperature and Humidity Sensor The temperature and humidity values in the environment are measured by DHT 11 sensor. The sensor consists of 4 pins, where the 1st and 4th pins are used for VDD and GND. The 2nd pin is used for connecting serial data bus and 3rd pin is meant for no connection. The operating voltage of the sensor is 3-5V DC. It can measure the temperature from 0-50°C with an accuracy of ±2°C and its relative humidity ranges from 20-95% with an accuracy of ±5%. 2.2.3 Soil Moisture Sensor Soil moisture sensors measure the water content in soil. It finds out the moisture level of the soil using the capacitance variations in the sensor. The capacitance variations depend on the dielectric permittivity of the soil which varies with the water content present in the soil. If the soil needs water, the sensor gives high
output and the soil has enough amount of water the sensor produces a low output. The operating voltage of the sensor is 5V DC. 2.2.4 Rain detector The rain detector is used to detect the amount of rain fall in the agricultural field. The amount of rain fall is calculated based on the current conduction induced in the detector module. The module has four supply lines at different levels along with one ground line which are used to find out the different water levels in the rain detector. The operating voltage of the rain detector is 5V DC. In Table 1 the technical specifications of the sensors used in proposed system is tabulated. Table 1.Sensor technical specification Component
Purpose
Range
Resolution
Accuracy
Name
Supply
Power
URL
2.5
http//:www.dr
mA
oboticsonline.c
voltage For
Temperature
temperature
and
measurement
0-50°C
1%
±2°C
5 V DC
om
Humidity Sensor
For humidity
20-
1%
DHT11
measurement
90%RH
±5%RH
5V DC
2.5
http//:www.dr
mA
oboticsonline.c om
Soil Moisture sensor
Rain Detector
For volumetric water content of soil measurement.
0-45%
For rainfall measurement
0-300
0.08%
±4%
3-5V
3mA
DC
mm
of
rainfall per hour
±2% up to
3.5-5V
200mm
DC
per hour
http//:www.ve mier.com
3mA
Figure 2. Flow diagram of automatic irrigation system 2.3 Working Principle of proposed Automatic irrigation system Figure 2 shows the work flow of automatic irrigation system. The system collects the environmental parameters from various sensors like temperature, humidity, soil moisture and rain detector. Based on the value of rain fall detector and soil moisture sensor, the system is categorized into 4 major ways. The categorizes are 1. Rain fall – No; Soil moisture < threshold value: The system turns the motor ON.
2. Rain fall – No; Soil moisture > threshold value: The system turns the motor OFF. 3. Rain fall – Yes; Soil moisture < threshold value: The system turns the motor ON. 4. Rain fall – Yes; Soil moisture > threshold value: The system turns the motor OFF. Finally, the status of the motor and environmental conditions of the agricultural field is periodically updated to the farmer through mobile phone. 3. Results and Discussion 3.1 Simulation results The execution of the proposed system is simulated using Proteus 7 software and the result is shown in Figure 3.
Figure 3. Measurement of environmental parameters
In this system, the ARM 7 microcontroller process the inputs like temperature, humidity, soil moisture and rain fall level and gives the output to control the irrigation motor. The various values of the inputs are fed to the program and the outputs are visualized and verified using virtual terminal in the simulation software. 3.2 Prototype model of the proposed automatic irrigation system
Figure 4. Prototype of the automatic irrigation system The hardware setup of the proposed automatic irrigation system is shown in Figure 4. It consists of ARM 7 microcontroller, motor, GSM module and various sensors for monitoring the environmental parameters. Based on the sensor values, the microcontroller controls the irrigation motor and sends the information to farmer’s mobile through GSM module. The hardware prototype is tested in the field and the sample values are shown here. The values received in farmer mobile such as
temperature, humidity, soil moisture, rain fall level and also the motor status is shown in Figure 5.
Figure 5. Message received in Android mobile 3.3 Real time implementation of the proposed system The developed hardware prototype model is deployed and tested in the agricultural field for monitoring the environmental parameters for a period of two months. Automatic irrigation has been done through this system according to the rain fall level measured in the field. The rain fall, temperature, soil moisture and humidity values are measured using the proposed system in the month of November and December 2016 in Coimbatore, Tamil Nadu, India and the data are compared with web data resources [25, 26]. The actual rainfall of the above mentioned place is taken from the web source and the values evaluated in the field using the proposed automatic irrigation system is tabulated in Table 2. Table 2 Rainfall during November and December 2016 Day Nov-01
Actual value in cm 1.5
Evaluated Value in cm 1.0
Day Nov-17
Actual value in cm 0.0
Evaluated Value in cm 0.0
Nov-02 Nov-03 Nov-04 Nov-05 Nov-06 Nov-07 Nov-08 Nov-09 Nov-10 Nov-11 Nov-12 Nov-13 Nov-14 Nov-15 Nov-16
0.3 1.1 1.3 1.3 0.7 0.0 0.6 0.6 0.2 0.1 0.0 0.9 1.0 0.0 0.1
0.2 1.0 1.0 1.0 0.5 0.0 0.5 0.5 0.1 0.1 0.0 0.5 1.0 0.0 0.1
Nov-18 Nov-19 Nov-20 Nov-21 Nov-22 Nov-23 Nov-24 Nov-25 Nov-26 Nov-27 Nov-28 Nov-29 Nov-30 Dec-01 Dec02-Dec31
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
It is observed from the Table 2 that the proposed system yields approximately the same value as that of the existing actual value for the rain fall. The difference in estimation of the rain fall using the prototype developed and the observed web data varies from 0 to 0.5.
Figure 6. Rainfall in November and December 2016
The analysis of rain fall for the month of November and December 2016 along with the estimation error is shown diagrammatically in Figure 6. The reason for the difference in estimation is due to presence of only four wires in the rain detector. If the rain fall detector has more wires, then the difference in estimation of rainfall will be minimum. Table 3: Temperature value in December 2016 Day
Dec-01 Dec-02 Dec-03 Dec-04 Dec-05 Dec-06 Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12 Dec-13 Dec-14 Dec-15 Dec-16
Actual temperature in Celsius High Low 27 18 29 18 25 22 29 22 31 23 31 23 31 19 31 20 32 22 30 22 32 22 31 20 29 22 29 21 29 22 31 20
Measured temperature in Celsius High Low 25 17 27 17 22 21 28 21 30 22 29 22 30 18 30 19 31 21 29 21 31 21 28 19 27 21 27 20 29 21 30 19
Day
Dec-17 Dec-18 Dec-19 Dec-20 Dec-21 Dec-22 Dec-23 Dec-24 Dec-25 Dec-26 Dec-27 Dec-28 Dec-29 Dec-30 Dec-31
Actual temperature in Celsius High Low 31 22 31 21 33 20 29 21 32 22 33 22 33 20 32 19 32 20 33 20 33 20 30 23 31 20 30 19 31 18
Measured temperature in Celsius High Low 30 21 29 20 31 19 28 20 30 21 31 20 32 19 30 18 31 19 31 19 31 19 28 22 29 19 29 18 30 17
The temperature values are measured for the month of November and December 2016. It is observed that the maximum temperature value in November is 33° Celsius and minimum is 15° Celsius. The temperature in the month of December alone is tabulated in Table 3 and it is seen that the actual value from the web data varies from 18 to 33° Celsius and the measured is from 17 to 32° Celsius. The temperature values are plotted in a graph and are shown in Figure 7. The graph consists of the actual high and low temperatures and the measured high and low temperatures and the difference in estimation of temperatures. It is noted that there is a
minimum difference between the values obtained using the proposed prototype model and the web data. It is obvious from the graph that the difference lies between 0 and 3° Celsius for the temperature high and between 1 and 2° Celsius for low.
Figure 7. Temperature value in December 2016
The humidity and soil moisture values for the month of November and December 2016 are measured. The December month Humidity values in percentage are tabulated in Table 4. It is observed from the table that the actual web data ranges from 29 to 96 % and the proposed hardware setup yields 28 to 95%. Table 4 Humidity in December 2016 Day Dec-01 Dec-02 Dec-03 Dec-04 Dec-05 Dec-06
Humidity in % Actual Measured 66 65 90 89 96 94 95 94 88 87 83 82
Day Dec-17 Dec-18 Dec-19 Dec-20 Dec-21 Dec-22
Humidity in % Actual Measured 87 86 83 82 29 28 76 75 89 87 83 82
Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12 Dec-13 Dec-14 Dec-15 Dec-16
79 77 88 91 96 82 83 96 95 87
78 76 87 90 95 81 82 95 94 86
Dec-23 Dec-24 Dec-25 Dec-26 Dec-27 Dec-28 Dec-29 Dec-30 Dec-31
93 82 75 87 77 80 90 84 46
92 81 74 86 76 79 88 82 44
The tabulated values are shown in Figure 8 along with the difference in estimation of humidity in percentage and it is observed that it is between 1 and 2.
Figure 8. Humidity in Dec 2016 The soil moisture values taken from the proposed design are tabulated in Table 5 and the same is shown diagrammatically in Figure 9. The volumetric water content in % denotes the minimum and maximum soil moisture on that particular day.
Table 5: Soil moisture in December 2016 Days Dec-01 Dec-02 Dec-03 Dec-04 Dec-05 Dec-06 Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12 Dec-13 Dec-14 Dec-15 Dec-16
Restored Un-Restored value in % value in % 1.03 17.6 3.6 18.4 1.18 18 2.36 16.4 3.66 18.2 2.85 19.1 6.33 18.5 6.68 16.7 4.38 16.2 3.8 18.4 7.48 18.3 3.11 16.9 9.81 16 7.19 17.7 8.68 14.9 4.05 15.2
Days Dec-17 Dec-18 Dec-19 Dec-20 Dec-21 Dec-22 Dec-23 Dec-24 Dec-25 Dec-26 Dec-27 Dec-28 Dec-29 Dec-30 Dec-31
Restored Un-Restored value in % value in % 4.54 15.9 10.8 14.5 3.32 14.6 3.12 17.4 1.76 17.4 4.37 17 3.69 17.3 3.4 17.1 2.8 16.5 3.62 17.1 3.76 16.7 1.5 16.1 1.93 16.2 2.04 16.2 2.86 17.2
Figure 9. Soil moisture in Dec 2016
3.4 Comparison Results The proposed automatic irrigation system is tested in one acre of drip irrigated paddy field. The amount of water required at different stages is noted. The estimated amount of water for the proposed irrigation control system is compared with the existing systems [24]. The comparison is shown as a graph in Figure 10. From the Figure, it is observed that the proposed system consumes approximately 42% and 14% lesser amount of water than the manual flood irrigation and drip irrigation respectively. The amount of water saved in the proposed system is achieved by taking rainfall as one of the factor for irrigation control.
Figure 10. Water requirements for different Irrigation methods 3.5 Statistical Analysis
The values obtained using the proposed hardware prototype model and the actual values are statistically analyzed and the statistical parameters such as mean, standard deviation, variance, Skewness and Kurtosis are found out. It is tabulated in Table 6. From the statistical mean, standard deviation and variance, it is noted that there is a minimum difference in the values. The terms Skewness and Kurtosis are mainly to find the distribution of the data to test for its normality. Table 6 Comparison of statistical parameters Statistical Parameters
Rain Fall
Temperature in Celsius - High
Temperature in Celsius - Low
Humidity
Actual
Evaluated
Actual
Measured
Actual
Measured
Actual
Evaluated
Mean
0.35
0.27
30.68
29.13
20.74
19.71
82.35
81.19
Standard Deviation
0.49
0.40
1.83
2.05
1.50
1.49
14.10
14.12
Variance
0.24
0.16
3.36
4.18
2.26
2.21
198.8 4
199.43
Skewness
1.12
1.12
-1.08
-1.64
-0.28
-0.24
-2.41
-2.43
Kurtosis
-0.24
-0.43
1.84
3.89
-0.92
-0.85
7.09
7.10
The standard deviation here is used to measure the spread from the average or mean value. If the difference between the measured and actual is low, then it reveals that the predicted results are very close to the average. A high standard deviation means that the predicted values are spread out. It is observed from the Table 6 that the difference in standard deviation of the actual and evaluated i.e., the result obtained from the prototype model is low and it can be said that the results obtained are close to the average of the actual value. From these, it can be concluded that the proposed model gives almost the same result as that of the actual values. Skewness is to measure the asymmetry of the probability distribution of a random variable with respect to its mean. It is stated that If Skewness is less than -1 or greater
than 1, the distribution is highly skewed. If Skewness is between -1 and -0.5 or between 0.5 and 1, the distribution is moderately skewed. In general, Kurtosis is measured against the normal distribution. If the Kurtosis is nearer to zero, it can be treated as a normal distribution and it is called mesokurtic distribution. If the Kurtosis is less than zero, then the distribution has light tails and it is called platykurtic distribution. If the Kurtosis value becomes greater than zero, the distribution will have heavier tails and it is called leptokurtic distribution. The analysis of skewness and kurtosis for the considered environmental parameters is detailed in Table.7. Table 7 Analysis of Skewness and Kurtosis for the environmental parameters Statistical parameters
Temperature High
Temperature Low
Humidity
Rain fall
Skewness
Moderately skewed
Highly skewed
Highly skewed
Highly skewed
Kurtosis
Leptokurtic distribution
Platykurtic distribution
Leptokurtic distribution
Platykurtic distribution
3.6 Implementation and Testing The developed prototype model is tested in an area in tomato cultivated field in Coimbatore. Tomato is an important protective and versatile vegetable because of its nutritive value. It is a widely used in Indian culinary tradition. Tomatoes need even moisture content and it can’t withstand to both excess water as well as very little water. The challenge in tomato cultivation lies in the moisture content of the soil. It is necessary to irrigate the plant once a week during the summer, while it is enough to irrigate it once in every two weeks. Hence, it is decided to implement the prototype model for tomato cultivation to utilize the water resource effectively. It requires irrigation once in 7 to 10 days. Earlier surface irrigation and sprinkler are used. Due to the specific crop requirements for high soil water content, drip irrigation is found to be a suitable choice. The requirements of water will vary from stage to stage. There are
five growing stages such as germination or establishment stage, flowering, fruit set, yield formation, ripening. The water requirement analysis is tabulated in the following table 8 based on the water need in each and every stage. The water requirement for the crop is taken from the web sources [27, 28]. The prototype model is implanted to analyze the rainfall and based on which the motor is switched on and off. The water saving is calculated. The report is the observation done in the period of June 2018 to September 2018. Table 8. Analysis of water requirement for Tomato cultivation using proposed system Stages of Tomato Crop
Irrigation Schedule
Average Water required in cm 0.4
Average Rainfall in cm
Motor status
Water Saving (%)
0.2
Average Water supplied in cm 0.2
Germination or plant establishment stage Flower initiation to flowering
10 to 15 days
ON
50
20 to 30 days
0.7
0.1
0.6
ON
15
Flowering to fruit set
30 to 40 days
1.05
0.0
1.05
ON
0
Yield formation
30 to 40 days
0.8
0.5
0.3
ON
37
Ripening
15 to 20 days
0.6
0.4
0.2
ON
33
It is observed from the table that the water utilization with the proposed hardware prototype model is very much reduced. It is because of the switching ON and OFF based on the rainfall level. And also, it can be claimed that the crop yield will not be affected due to this and the farmer can effectively plan to treat the water resource for other crops.
4. Conclusion Rain is the important water resource in the earth. The irrigation control system by sensing the rain fall of an agricultural field is planned to bring out the Indian agricultural activities as an automated one. It can be said from the statistical parameter i.e., standard deviation that the proposed system brings out almost the similar result obtained from the web data in terms of temperature, humidity and rain fall etc., The proposed hardware prototype model is tested in the field and the observed results indicate that the proposed model saves a considerable amount of water compared to the existing traditional flood and drip irrigation. The important design highlight of the design is that the data can be transmitted to the users i.e., farmers and they can know the status of their land at any time, wherever they are. This is achieved because of the utilization of the GSM / GPRS technology in order to transmit the collected information from the agricultural field to the farmer’s handset. And also the workload of the farmer is getting reduced with the proposed system because of the updating of all the data as a real time scenario of the agricultural field through the mobile. It is very useful for doing future plan in agricultural activity. References 1. Armand, C., Francois. C., Jean. S.B., Herve. A. & Francois. G. (2011). Soft Water Level Sensors for Characterizing the Hydrological Behaviour of Agricultural Catchments. Sensors. 11, 4656-4673. 2. Baptista, I., Ritsema, C., Querido, A., Ferreira, A.D. & Giessen, V. (2015) Improving rainwater-use in Cabo Verde dry lands by reducing runoff and erosion. Geoderma. 2, 283–297. 3. Colli, M., Lanza, L. & Chan, P. (2013). Co-located tipping-bucket and optical drop counter RI measurements and a simulated correction algorithm. Atmospheric Research. 119, 3–12.
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Credit author statement Barkunan.S.R.: Conceptualization, Methodology, Software, Data curation, WritingOriginal draft preparation. Bhanumathi.V. : Visualization, Investigation, Supervision Writing- Reviewing and Editing. Balakrishnan.V.: Software, Validation.
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Highlights Efficient prototype model for the Irrigation System by detecting rain fall. Rainfall, temperature, humidity and soil moisture are used to control the motor. Periodically updating the environmental status to the farmer through mobile phone. Amount of water utilization is less compared to existing traditional methods.
S0263-2241(20)30089-0 https://doi.org/10.1016/j.measurement.2020.107552 MEASUR 107552
To appear in:
Measurement
Received Date: Revised Date: Accepted Date:
28 April 2017 18 December 2019 26 January 2020
Please cite this article as: S.R. Barkunan, V. Bhanumathi, V. Balakrishnan, Automatic Irrigation System with Rain Fall Detection in Agricultural Field, Measurement (2020), doi: https://doi.org/10.1016/j.measurement. 2020.107552
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Automatic Irrigation System with Rain Fall Detection in Agricultural Field Barkunan.S.R1, Bhanumathi.V2, Balakrishnan.V3 1Assistant Professor, [email protected], 2Assistant Professor, [email protected], 3PG Scholar, [email protected] Department of ECE, Akshaya college of Engineering and Technology, Coimbatore,1 Department of ECE, Anna University, Regional Campus, Coimbatore, 2,3
ABSTRACT: Rainfall is an important natural phenomenon for agricultural activities to fulfill its water requirements. The proposed irrigation technique in agriculture saves water by making it as an automated one. By detecting the rain fall in real time, the amount of water needed for the field can be planned. A system is developed based on ARM micro controller combined with GSM module to inform the rain fall level to the farmer and as well as automatically regulates the water irrigation. The system can monitor the current state of the land and data is transmitted to the mobile. The results obtained from the prototype are compared with the actual data taken from the web and it is found that the difference in estimation is minimum. The results from the prototype are compared with the traditional systems and it is found that the automation reveals best results in terms of water utilization. Keywords— ARM Microcontroller, GSM, Rain Detector, Soil Moisture Sensor, Humidity Sensor, Temperature Sensor, Android app.
1. Introduction Rain fall monitoring is one of the important activities in automatic irrigation of agriculture. It is known that the hydrological factor affecting the total productivity of the agriculture is rain fall. To reduce the over irrigation and save the water resource the rain fall monitoring system is very much needed. In general, the arid and semi-arid agricultural areas are very closely dependent on the rainfall level for increasing the growth rate of the agricultural production and minimizing the water resource usages. The rainfall is the important phenomenon for cultivating the crop in the agricultural fields. In [1], the authors proposed the soft capacitive handy sensor to monitor the rain fall in the agricultural catchment and record the intensity of rain fall and water level. In [2], the authors discussed a method to increase the rain water usage by reducing runoff and erosion of cabo Verdi dry land. In [3], two different types of rain gauge are compared namely drop counter catching-type gauge and a tipping-bucket rain intensity gauge with two correction algorithm. The Ogawa catching type drop counter is used in [4] to measure the real world rain fall data and these data are taken as references for artificial rain generating system. The dynamic behavior of RI gauge is improved by including the laboratory simulation results of real world data. Interrupt driven Wireless Sensor Network (WSN) prototype has been designed in [7] to monitor the large scale and real time rain fall. Several autonomous nodes are deployed in smart irrigation decision support system for sensing the climatic variables and soil moisture level to control the irrigation [13]. The sensors are playing vital role in several automation applications such as vineyard hail protection [19], smart city applications [21] and to develop the remote control system for optimize the power utilization of street lights in the city [18], [20], [17]. Solar energy operated nodes used WSN is proposed in [14] to collect multiple parameters of plant, soil and atmosphere. The measured data are sent to remote server which contains sensor information in database and permits future utilization of data in simple way. Power optimized electronic system design is proposed in [8] to detect the wind direction and rain fall. A low cost WSN system is proposed in [9] for monitoring
the regular changes in crops due to pests, soil moisture droughts and floods in agricultural land. In [15], the authors proposed a system having plumbed rainwater tanks in houses for adequate water management. It monitors the real rain fall data for a period of twelve months. Software design has been proposed in [10] to regulate the sprinkler through click and play menu in GUI according to measured input field data. A low cost microcontroller based prototype system is used to monitor the soil, canopy, and air temperature, and soil moisture status in cropped fields. The PIC16F88 microcontroller used in this prototype system and the data are collected throughout the growing season [5]. In [6], the authors proposed a Dual probe heat pulse sensor for measuring water content and thermal properties of soil. A sub optimal irrigation scheduling is proposed in [11] by imperfect forecast values of weather and it can be implemented in real time. A WSN is proposed in [12] to control the water irrigation for the horticulture land. In this four nodes are used to monitor the environmental parameters and real time control has been performed by central office in the form. WSN is playing an important role in precision agriculture and it is understood that the WSN can be implemented for large agricultural lands by utilizing the clustering [22, 23] of sensor nodes. In [16], the authors proposed a distributed wireless sensor network based irrigation system with remotely connected sprinklers to the server at base station through wireless communication. A smart sensor based drip irrigation system for paddy cultivation was proposed in [24] which the soil image captured by the smartphone is utilized for finding the soil wetness and the environmental parameters such as temperature and humidity values are derived from the sensors. The automatic irrigation system proposed in the present paper for agricultural land is mainly based on sensing the environmental parameters and the real time rain fall status which is found to be playing a major role in controlling the usage of the water resources.
The rest of the paper is organized as follows: section 2 describes the working methodology of the proposed irrigation system. The detailed results and discussion are given in the section 3. Finally, section 4 concludes the work. 2. Proposed Irrigation System 2.1 Block diagram of Proposed Irrigation system The block diagram of the proposed automatic irrigation control system is shown in Figure 1. It consists of ARM 7 microcontroller as its main part. The temperature and humidity sensor is interfaced with the ARM 7 microcontroller for measuring the temperature and humidity values of the agricultural field. The rain detector is connected with ARM controller which is used to find out the real time rain fall and the moisture content of soil is measured by the soil moisture sensor according to the rain fall status of the land. Real time data measured by the corresponding sensors are used to control the irrigation pump motor as ON or OFF and the information is sent to the farmer’s mobile phone through GSM module.
Figure1. Block diagram of automatic irrigation system
2.2 Components used 2.2.1 ARM 7 Microcontroller The LPC2148 microcontroller is based on a 32/16 bit ARM7 TDMI CPU. The microcontroller consists of an embedded high speed flash memory ranging from 32kB to 512kB with real-time emulation and embedded trace support. By using maximum clock rate in the unique accelerator, the architecture enables the 32-bit code execution with 128-bit wide memory interface. The serial communication interfaces like USB, SPI, SSP, I2C and UART are present in the controller for effective data transmission. The microcontroller is having an in-built multiple 32-bit timers, 10-bit ADC, 10-bit Digital to Analog Converter (DAC), PWM channels and 45 fast GPIO lines with up to nine edge or level sensitive external interrupt pins. The real time clock and watchdog timers are present in the controller to manage the indefinite loop problems that occur during the time of execution. 2.2.2 Temperature and Humidity Sensor The temperature and humidity values in the environment are measured by DHT 11 sensor. The sensor consists of 4 pins, where the 1st and 4th pins are used for VDD and GND. The 2nd pin is used for connecting serial data bus and 3rd pin is meant for no connection. The operating voltage of the sensor is 3-5V DC. It can measure the temperature from 0-50°C with an accuracy of ±2°C and its relative humidity ranges from 20-95% with an accuracy of ±5%. 2.2.3 Soil Moisture Sensor Soil moisture sensors measure the water content in soil. It finds out the moisture level of the soil using the capacitance variations in the sensor. The capacitance variations depend on the dielectric permittivity of the soil which varies with the water content present in the soil. If the soil needs water, the sensor gives high
output and the soil has enough amount of water the sensor produces a low output. The operating voltage of the sensor is 5V DC. 2.2.4 Rain detector The rain detector is used to detect the amount of rain fall in the agricultural field. The amount of rain fall is calculated based on the current conduction induced in the detector module. The module has four supply lines at different levels along with one ground line which are used to find out the different water levels in the rain detector. The operating voltage of the rain detector is 5V DC. In Table 1 the technical specifications of the sensors used in proposed system is tabulated. Table 1.Sensor technical specification Component
Purpose
Range
Resolution
Accuracy
Name
Supply
Power
URL
2.5
http//:www.dr
mA
oboticsonline.c
voltage For
Temperature
temperature
and
measurement
0-50°C
1%
±2°C
5 V DC
om
Humidity Sensor
For humidity
20-
1%
DHT11
measurement
90%RH
±5%RH
5V DC
2.5
http//:www.dr
mA
oboticsonline.c om
Soil Moisture sensor
Rain Detector
For volumetric water content of soil measurement.
0-45%
For rainfall measurement
0-300
0.08%
±4%
3-5V
3mA
DC
mm
of
rainfall per hour
±2% up to
3.5-5V
200mm
DC
per hour
http//:www.ve mier.com
3mA
Figure 2. Flow diagram of automatic irrigation system 2.3 Working Principle of proposed Automatic irrigation system Figure 2 shows the work flow of automatic irrigation system. The system collects the environmental parameters from various sensors like temperature, humidity, soil moisture and rain detector. Based on the value of rain fall detector and soil moisture sensor, the system is categorized into 4 major ways. The categorizes are 1. Rain fall – No; Soil moisture < threshold value: The system turns the motor ON.
2. Rain fall – No; Soil moisture > threshold value: The system turns the motor OFF. 3. Rain fall – Yes; Soil moisture < threshold value: The system turns the motor ON. 4. Rain fall – Yes; Soil moisture > threshold value: The system turns the motor OFF. Finally, the status of the motor and environmental conditions of the agricultural field is periodically updated to the farmer through mobile phone. 3. Results and Discussion 3.1 Simulation results The execution of the proposed system is simulated using Proteus 7 software and the result is shown in Figure 3.
Figure 3. Measurement of environmental parameters
In this system, the ARM 7 microcontroller process the inputs like temperature, humidity, soil moisture and rain fall level and gives the output to control the irrigation motor. The various values of the inputs are fed to the program and the outputs are visualized and verified using virtual terminal in the simulation software. 3.2 Prototype model of the proposed automatic irrigation system
Figure 4. Prototype of the automatic irrigation system The hardware setup of the proposed automatic irrigation system is shown in Figure 4. It consists of ARM 7 microcontroller, motor, GSM module and various sensors for monitoring the environmental parameters. Based on the sensor values, the microcontroller controls the irrigation motor and sends the information to farmer’s mobile through GSM module. The hardware prototype is tested in the field and the sample values are shown here. The values received in farmer mobile such as
temperature, humidity, soil moisture, rain fall level and also the motor status is shown in Figure 5.
Figure 5. Message received in Android mobile 3.3 Real time implementation of the proposed system The developed hardware prototype model is deployed and tested in the agricultural field for monitoring the environmental parameters for a period of two months. Automatic irrigation has been done through this system according to the rain fall level measured in the field. The rain fall, temperature, soil moisture and humidity values are measured using the proposed system in the month of November and December 2016 in Coimbatore, Tamil Nadu, India and the data are compared with web data resources [25, 26]. The actual rainfall of the above mentioned place is taken from the web source and the values evaluated in the field using the proposed automatic irrigation system is tabulated in Table 2. Table 2 Rainfall during November and December 2016 Day Nov-01
Actual value in cm 1.5
Evaluated Value in cm 1.0
Day Nov-17
Actual value in cm 0.0
Evaluated Value in cm 0.0
Nov-02 Nov-03 Nov-04 Nov-05 Nov-06 Nov-07 Nov-08 Nov-09 Nov-10 Nov-11 Nov-12 Nov-13 Nov-14 Nov-15 Nov-16
0.3 1.1 1.3 1.3 0.7 0.0 0.6 0.6 0.2 0.1 0.0 0.9 1.0 0.0 0.1
0.2 1.0 1.0 1.0 0.5 0.0 0.5 0.5 0.1 0.1 0.0 0.5 1.0 0.0 0.1
Nov-18 Nov-19 Nov-20 Nov-21 Nov-22 Nov-23 Nov-24 Nov-25 Nov-26 Nov-27 Nov-28 Nov-29 Nov-30 Dec-01 Dec02-Dec31
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
It is observed from the Table 2 that the proposed system yields approximately the same value as that of the existing actual value for the rain fall. The difference in estimation of the rain fall using the prototype developed and the observed web data varies from 0 to 0.5.
Figure 6. Rainfall in November and December 2016
The analysis of rain fall for the month of November and December 2016 along with the estimation error is shown diagrammatically in Figure 6. The reason for the difference in estimation is due to presence of only four wires in the rain detector. If the rain fall detector has more wires, then the difference in estimation of rainfall will be minimum. Table 3: Temperature value in December 2016 Day
Dec-01 Dec-02 Dec-03 Dec-04 Dec-05 Dec-06 Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12 Dec-13 Dec-14 Dec-15 Dec-16
Actual temperature in Celsius High Low 27 18 29 18 25 22 29 22 31 23 31 23 31 19 31 20 32 22 30 22 32 22 31 20 29 22 29 21 29 22 31 20
Measured temperature in Celsius High Low 25 17 27 17 22 21 28 21 30 22 29 22 30 18 30 19 31 21 29 21 31 21 28 19 27 21 27 20 29 21 30 19
Day
Dec-17 Dec-18 Dec-19 Dec-20 Dec-21 Dec-22 Dec-23 Dec-24 Dec-25 Dec-26 Dec-27 Dec-28 Dec-29 Dec-30 Dec-31
Actual temperature in Celsius High Low 31 22 31 21 33 20 29 21 32 22 33 22 33 20 32 19 32 20 33 20 33 20 30 23 31 20 30 19 31 18
Measured temperature in Celsius High Low 30 21 29 20 31 19 28 20 30 21 31 20 32 19 30 18 31 19 31 19 31 19 28 22 29 19 29 18 30 17
The temperature values are measured for the month of November and December 2016. It is observed that the maximum temperature value in November is 33° Celsius and minimum is 15° Celsius. The temperature in the month of December alone is tabulated in Table 3 and it is seen that the actual value from the web data varies from 18 to 33° Celsius and the measured is from 17 to 32° Celsius. The temperature values are plotted in a graph and are shown in Figure 7. The graph consists of the actual high and low temperatures and the measured high and low temperatures and the difference in estimation of temperatures. It is noted that there is a
minimum difference between the values obtained using the proposed prototype model and the web data. It is obvious from the graph that the difference lies between 0 and 3° Celsius for the temperature high and between 1 and 2° Celsius for low.
Figure 7. Temperature value in December 2016
The humidity and soil moisture values for the month of November and December 2016 are measured. The December month Humidity values in percentage are tabulated in Table 4. It is observed from the table that the actual web data ranges from 29 to 96 % and the proposed hardware setup yields 28 to 95%. Table 4 Humidity in December 2016 Day Dec-01 Dec-02 Dec-03 Dec-04 Dec-05 Dec-06
Humidity in % Actual Measured 66 65 90 89 96 94 95 94 88 87 83 82
Day Dec-17 Dec-18 Dec-19 Dec-20 Dec-21 Dec-22
Humidity in % Actual Measured 87 86 83 82 29 28 76 75 89 87 83 82
Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12 Dec-13 Dec-14 Dec-15 Dec-16
79 77 88 91 96 82 83 96 95 87
78 76 87 90 95 81 82 95 94 86
Dec-23 Dec-24 Dec-25 Dec-26 Dec-27 Dec-28 Dec-29 Dec-30 Dec-31
93 82 75 87 77 80 90 84 46
92 81 74 86 76 79 88 82 44
The tabulated values are shown in Figure 8 along with the difference in estimation of humidity in percentage and it is observed that it is between 1 and 2.
Figure 8. Humidity in Dec 2016 The soil moisture values taken from the proposed design are tabulated in Table 5 and the same is shown diagrammatically in Figure 9. The volumetric water content in % denotes the minimum and maximum soil moisture on that particular day.
Table 5: Soil moisture in December 2016 Days Dec-01 Dec-02 Dec-03 Dec-04 Dec-05 Dec-06 Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12 Dec-13 Dec-14 Dec-15 Dec-16
Restored Un-Restored value in % value in % 1.03 17.6 3.6 18.4 1.18 18 2.36 16.4 3.66 18.2 2.85 19.1 6.33 18.5 6.68 16.7 4.38 16.2 3.8 18.4 7.48 18.3 3.11 16.9 9.81 16 7.19 17.7 8.68 14.9 4.05 15.2
Days Dec-17 Dec-18 Dec-19 Dec-20 Dec-21 Dec-22 Dec-23 Dec-24 Dec-25 Dec-26 Dec-27 Dec-28 Dec-29 Dec-30 Dec-31
Restored Un-Restored value in % value in % 4.54 15.9 10.8 14.5 3.32 14.6 3.12 17.4 1.76 17.4 4.37 17 3.69 17.3 3.4 17.1 2.8 16.5 3.62 17.1 3.76 16.7 1.5 16.1 1.93 16.2 2.04 16.2 2.86 17.2
Figure 9. Soil moisture in Dec 2016
3.4 Comparison Results The proposed automatic irrigation system is tested in one acre of drip irrigated paddy field. The amount of water required at different stages is noted. The estimated amount of water for the proposed irrigation control system is compared with the existing systems [24]. The comparison is shown as a graph in Figure 10. From the Figure, it is observed that the proposed system consumes approximately 42% and 14% lesser amount of water than the manual flood irrigation and drip irrigation respectively. The amount of water saved in the proposed system is achieved by taking rainfall as one of the factor for irrigation control.
Figure 10. Water requirements for different Irrigation methods 3.5 Statistical Analysis
The values obtained using the proposed hardware prototype model and the actual values are statistically analyzed and the statistical parameters such as mean, standard deviation, variance, Skewness and Kurtosis are found out. It is tabulated in Table 6. From the statistical mean, standard deviation and variance, it is noted that there is a minimum difference in the values. The terms Skewness and Kurtosis are mainly to find the distribution of the data to test for its normality. Table 6 Comparison of statistical parameters Statistical Parameters
Rain Fall
Temperature in Celsius - High
Temperature in Celsius - Low
Humidity
Actual
Evaluated
Actual
Measured
Actual
Measured
Actual
Evaluated
Mean
0.35
0.27
30.68
29.13
20.74
19.71
82.35
81.19
Standard Deviation
0.49
0.40
1.83
2.05
1.50
1.49
14.10
14.12
Variance
0.24
0.16
3.36
4.18
2.26
2.21
198.8 4
199.43
Skewness
1.12
1.12
-1.08
-1.64
-0.28
-0.24
-2.41
-2.43
Kurtosis
-0.24
-0.43
1.84
3.89
-0.92
-0.85
7.09
7.10
The standard deviation here is used to measure the spread from the average or mean value. If the difference between the measured and actual is low, then it reveals that the predicted results are very close to the average. A high standard deviation means that the predicted values are spread out. It is observed from the Table 6 that the difference in standard deviation of the actual and evaluated i.e., the result obtained from the prototype model is low and it can be said that the results obtained are close to the average of the actual value. From these, it can be concluded that the proposed model gives almost the same result as that of the actual values. Skewness is to measure the asymmetry of the probability distribution of a random variable with respect to its mean. It is stated that If Skewness is less than -1 or greater
than 1, the distribution is highly skewed. If Skewness is between -1 and -0.5 or between 0.5 and 1, the distribution is moderately skewed. In general, Kurtosis is measured against the normal distribution. If the Kurtosis is nearer to zero, it can be treated as a normal distribution and it is called mesokurtic distribution. If the Kurtosis is less than zero, then the distribution has light tails and it is called platykurtic distribution. If the Kurtosis value becomes greater than zero, the distribution will have heavier tails and it is called leptokurtic distribution. The analysis of skewness and kurtosis for the considered environmental parameters is detailed in Table.7. Table 7 Analysis of Skewness and Kurtosis for the environmental parameters Statistical parameters
Temperature High
Temperature Low
Humidity
Rain fall
Skewness
Moderately skewed
Highly skewed
Highly skewed
Highly skewed
Kurtosis
Leptokurtic distribution
Platykurtic distribution
Leptokurtic distribution
Platykurtic distribution
3.6 Implementation and Testing The developed prototype model is tested in an area in tomato cultivated field in Coimbatore. Tomato is an important protective and versatile vegetable because of its nutritive value. It is a widely used in Indian culinary tradition. Tomatoes need even moisture content and it can’t withstand to both excess water as well as very little water. The challenge in tomato cultivation lies in the moisture content of the soil. It is necessary to irrigate the plant once a week during the summer, while it is enough to irrigate it once in every two weeks. Hence, it is decided to implement the prototype model for tomato cultivation to utilize the water resource effectively. It requires irrigation once in 7 to 10 days. Earlier surface irrigation and sprinkler are used. Due to the specific crop requirements for high soil water content, drip irrigation is found to be a suitable choice. The requirements of water will vary from stage to stage. There are
five growing stages such as germination or establishment stage, flowering, fruit set, yield formation, ripening. The water requirement analysis is tabulated in the following table 8 based on the water need in each and every stage. The water requirement for the crop is taken from the web sources [27, 28]. The prototype model is implanted to analyze the rainfall and based on which the motor is switched on and off. The water saving is calculated. The report is the observation done in the period of June 2018 to September 2018. Table 8. Analysis of water requirement for Tomato cultivation using proposed system Stages of Tomato Crop
Irrigation Schedule
Average Water required in cm 0.4
Average Rainfall in cm
Motor status
Water Saving (%)
0.2
Average Water supplied in cm 0.2
Germination or plant establishment stage Flower initiation to flowering
10 to 15 days
ON
50
20 to 30 days
0.7
0.1
0.6
ON
15
Flowering to fruit set
30 to 40 days
1.05
0.0
1.05
ON
0
Yield formation
30 to 40 days
0.8
0.5
0.3
ON
37
Ripening
15 to 20 days
0.6
0.4
0.2
ON
33
It is observed from the table that the water utilization with the proposed hardware prototype model is very much reduced. It is because of the switching ON and OFF based on the rainfall level. And also, it can be claimed that the crop yield will not be affected due to this and the farmer can effectively plan to treat the water resource for other crops.
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Barkunan.S.R.
Credit author statement Barkunan.S.R.: Conceptualization, Methodology, Software, Data curation, WritingOriginal draft preparation. Bhanumathi.V. : Visualization, Investigation, Supervision Writing- Reviewing and Editing. Balakrishnan.V.: Software, Validation.
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Highlights Efficient prototype model for the Irrigation System by detecting rain fall. Rainfall, temperature, humidity and soil moisture are used to control the motor. Periodically updating the environmental status to the farmer through mobile phone. Amount of water utilization is less compared to existing traditional methods.