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Current and potential capabilities of UAS for crop water productivity in precision agriculture
Agricultural Water Management 218 (2019) 158–164
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Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat
Review
Current and potential capabilities of UAS for crop water productivity in precision agriculture
T
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G.I. Ezennea, , Louise Juppb, S.K. Mantela, J.L. Tannera a b
Institute for Water Research, Rhodes University, South Africa Terreco Aviation, 123 Western Avenue, Vincent, East London, South Africa
A R T I C LE I N FO
A B S T R A C T
Keywords: Agricultural water use Thermal imaging Real-time irrigation scheduling
In order to feed growing populations under scare water resources, a suitable technology that improves crop water productivity (CWP) is crucial. Precision agriculture that utilizes digital techniques such as unmanned aerial systems (UAS) can play a significant role in improving CWP. CWP is an important indicator that quantifies the effect of agricultural water management. To improve CWP, implementation of suitable methods for early detection of crop water stress before irreversible damage on crops occurs, is vital. Conventionally, farmers have relied on in situ measurements of soil moisture and weather variables for detecting crop water status for irrigation scheduling. This method is time consuming and does not account for spatial and temporal variability associated with crop water status. Hence, the aim of this study is to give an overview of the current and potential capabilities of UAS for crop water productivity in precision agriculture. Identified in this study are the factors as well as the technology that can improve CWP. UAS thermal remote sensing is found to be the most suitable technology for monitoring and assessing crop water status using certain indices. Determining a crop water stress index (CWSI) from thermal imagery has the potential to detect instantaneous variations of water status. CWSI obtained from UAS thermal imaging camera can be adapted for real-time irrigation scheduling for maximum crop water productivity.
1. Introduction By 2050 agricultural production must double to meet the food demand of the growing population (Martínez et al., 2017). The agricultural industry must therefore, maximize the use of available resources in order to cope with the present trajectory of population growth and, precision agriculture can play a significant role in reaching this goal. Precision agriculture is a farming management concept where a practice is performed at the right time, right place and appropriate intensity (Maes and Steppe, 2019). It often utilizes digital techniques (satellite, UAS, sensors, etc.) to optimize agricultural production processes while minimizing adverse environmental impacts (Schrijver, 2016; Abdullahi et al., 2015). Sensors commonly used in precision irrigation systems such as drip irrigation reduce crop water use but provide only point estimates. When digital techniques such as UAS are utilized in precision agriculture, the spatial and temporal variability of the crop conditions are accounted for in greater detail than commonly used sensor technologies. Precision agriculture generally aims to increase the quality and quantity of agricultural output while using less input (e.g. water, pesticides, fertilizer, energy, herbicides etc.).
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Agricultural water use is one of the main factors behind increasing water scarcity, with irrigated agriculture accounting for about 70% of the freshwater withdrawals in the world (Tshwene and Oladele, 2016). With water being a significant limiting factor in agricultural management, this high agricultural water use can be better managed using the crop water productivity concept. CWP also referred to as water use efficiency, can be defined as the relationship between crop output and the amount of water used in crop production. It is mathematically expressed as the quantity of yield (kg/hectare) per unit amount of water (mm/day) consumed (i.e. crop evapotranspiration) (Jin et al., 2016; Araya et al., 2018; Kukal and Irmak, 2017; Brauman et al., 2013; Dogaru, 2016; Humphreys et al., 2006; Nhamo et al., 2016; Wichelns, 2014). CWP is an indicator that quantifies the effect of agricultural water management (Sakthivadivel et al., 1999; Sun et al., 2017). The use of CWP helps to meet the rising demand of water by irrigation amidst other sectors and also provide food for the growing population. According to Igbadun et al. (2006), there are many expressions and definitions for quantifying CWP, such as water use (technical) efficiency, which is defined as quantity of agricultural output per unit amount of water used in the production; water use (economic) efficiency, which is
Corresponding author. E-mail address: [email protected] (G.I. Ezenne).
https://doi.org/10.1016/j.agwat.2019.03.034 Received 22 November 2018; Received in revised form 7 March 2019; Accepted 25 March 2019 0378-3774/ © 2019 Elsevier B.V. All rights reserved.
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platforms have some limitations and advantages. To mention a few limitations and advantages, unmanned helicopters are able to travel in any direction while fixed wing airplanes travel in a more linear and restricted fashion. Fixed-wing aircrafts have simple flight systems and can fly for longer duration compared to unmanned helicopters. Unmanned helicopters have more complex flight systems and can offer lower flight altitudes with low-speed. Fixed-wing aircrafts therefore, have capacity to cover wider areas but their flight altitude is higher which reduces the image resolution. Also, fixed-wing UAS aerial platforms require specific runways or at least sufficient open space for landing and take-off. Because of these limitations, recently a new aircraft platform emerged, multi-copter (multi-rotor), which does not require special take-off or landing runway. The multi-copter is more userfriendly which makes it possible to be operated by farmers in order to obtain real-time data and apply adapted water management. Generally, the type of UAS required depends on the aim of the study as well as the quality of the desired output/result. In addition to UAS aerial platforms, the camera sensors are key to the quality of images produced. Selection of camera type also depends on the aim of the project. The most commonly used UAS cameras in agriculture include: thermal, multispectral, hyperspectral and redgreen-blue (RGB). Gago et al. (2015) recommended different camera types depending on the type of crop trait/status of interest. For biotic and abiotic stress, thermal and hyperspectral cameras are recommended; while multispectral and red-green-blue (RGB) cameras are recommended for growth/biomass assessment. RGB cameras are one of the traditional imaging systems used in agroforestry applications, however they lack the precision and spectral range to profile materials that hyperspectral and multispectral cameras can provide (Adão et al., 2017).
the value of the output produced per quantity of water consumed, and; water use (hydraulic) efficiency, which is defined as the ratio of water consumed by irrigated agriculture to the volume of water supplied. These three CWP definitions are centred on minimizing water input while maximizing the crop yield/output. Deficit irrigation techniques have been found to improve CWP by more than 200% (Zwart and Bastiaanssen, 2004; Hirich et al., 2014; Adeboye et al., 2015) if irrigation is timeously managed. However, deficit irrigation must be carefully managed as Igbadun et al. (2006) described an attempt to maximize CWP by withholding irrigation in multiple crop growth stages which resulted in significant reduction in crop yield. Zwart and Bastiaanssen (2004), however, found that plants are more efficient with water when they are stressed and agricultural production can be sustained with 20–40% less water resources as long as new water management techniques are adopted. The agricultural sector is currently under pressure to produce high quantity of food while using less water per unit of output thereby increasing CWP (Tshwene and Oladele, 2016). This is achieved by either producing the same quantity of crop with less water resources, or a higher quantity of crop with the same quantity of water resources (Zwart and Bastiaanssen, 2004). Either way, CWP is generally derived from the relationship between the water consumption and yield of a particular crop, both of which are highly variable in space and time (Kukal and Irmak, 2017). CWP can exhibit high spatial and temporal variability due to climatic and agricultural factors (Mdemu et al., 2009). Among the climatic factors, CWP is more sensitive to wind speed and relative humidity compared to sunshine hours while for agricultural factors, it is more sensitive to irrigation efficiency than the quantity of fertilizer used (Sun et al., 2017). Therefore, to improve CWP emphasis should focus on the climatic factors and on irrigation efficiency. Existing techniques for improving CWP such as partial root zone drying (Barrios-Masias and Jackson, 2016), optimized regulated deficit irrigation (Léllis et al., 2017; Gendron et al., 2018; Adeboye et al., 2015) etc. do not currently account for the integrated effects of climatic and irrigation efficiency factors as well as spatial variability of CWP. Unmanned aerial systems (UAS) technology offers high spatial resolution and rapid collection of data over large areas, potentially enabling farmers to increase the precision of irrigation both spatially and temporally. Therefore, the aim of this study is to give a general overview of the current and potential capabilities of UAS to improve crop water productivity under precision agriculture.
3. Commonly used UAS imaging cameras for water status 3.1. Hyperspectral Hyperspectral imaging techniques measure the reflectance from the vegetation canopy. The recorded reflectance cannot be applied directly as a metric of leaf water content therefore reflectance indices are used. Indices for hyperspectral data include Simple Ratio Index (SRI), Normalized Difference (e.g. Photochemical Reflectance Index (PRI)) etc. (Elvanidi et al., 2018; Pôças et al., 2017; Rapaport et al., 2015). Hyperspectral technology integrates spectroscopy with the benefits of digital imagery (Loggenberg et al., 2018). The capability of hyperspectral imaging ranging from 400 to 2500 nm in assessing the water deficiency in tomato plants was demonstrated by Susič et al. (2018). Zarco-Tejada et al. (2012) demonstrated the ability of UAV based hyperspectral and thermal cameras to assess water stress levels in a citrus crop and confirmed that there is link between PRI and canopy temperature. Recently, Loggenberg et al. (2018) combined hyperspectral RS technologies with machine learning to discriminate between stressed and non-stressed grape vines from hyperspectral imaging. The results obtained showed that machine (ensemble) learners effectively analysed the hyperspectral data. Generally, hyperspectral imaging cameras capture more detailed data in both spatial and spectral ranges compared to other cameras. It can measure hundreds of bands which raises complexity when considering the quantity of data acquired. Loggenberg et al. (2018) noted that the major limiting factor in applying hyperspectral data is the inherent ‘curse of dimensionality’ which results in reduced classification accuracies. In addition, hyperspectral cameras are very expensive and complex (Elvanidi et al., 2018), which limit their wide-spread application especially in commercial agriculture.
2. UAS in precision agriculture UAS are platforms for remote sensing (RS) used in precision agriculture and examples of their use are found in (Hunt et al., 2018; Adão et al., 2017; Bendig et al., 2012; Santesteban et al., 2017; Poblete et al., 2017; Muchiri and Kimathi, 2016; Abdullahi et al., 2015; Simelli and Apostolos, 2015; Whitehead et al., 2014; Whitehead and Hugenholtz, 2014; Manfreda et al., 2018). These platforms are evolving rapidly technically and with regard to regulations (Ballesteros et al., 2014). UASs recently have become a common remote sensing technology comprising aerial platforms suitable for carrying small and lightweight sensors. There are different classes of UAS for instance according to Whitehead and Hugenholtz (2014), in United States it ranges from micro (< 0.9 kg), mini (0.9–13.6 kg), tactical (13.6–454.5 kg), medium altitude long endurance (454.5 – 13,636.4 kg), and high altitude long endurance (> 13,636.4 kg). They noted that commercial and remote sensing applications prefer UAS weighing less than 5 kg because of cost advantages and reduced risk associated with blunt force impact. MiniUAS weighing less than 5 kg can carry 0.2 kg to 1.5 kg of sensor payload (Bendig et al., 2012). According to Gago et al. (2015), four component tasks to be considered before selecting the appropriate UAS for precision agriculture are: design of experiment, data acquisition, data processing and results. For agricultural management, fixed-wing airplanes and unmanned helicopters UASs are mainly used. These two aerial
3.2. Multispectral Multispectral imaging techniques are based on the theory that each 159
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evaporative demand (Khanal et al., 2017). Therefore, thermal RS determines the crop canopy surface temperature as a way of quantifying crop water stress which is useful in irrigation scheduling. Additionally, thermal RS can also quantify soil moisture and evapotranspiration status. Many studies have used thermal RS to assess crop water stress for several crop species including citrus orchards (Zarco-Tejada et al., 2012), pinto beans (Zhou et al., 2018), sugar beet plants (Quebrajo et al., 2018), almond trees (García-Tejero et al., 2018a & 2018b), and grapevines (Bellvert et al., 2014; Bellvert et al., 2016; Espinoza et al., 2017; Matese et al., 2018; García-Tejero et al., 2016). Notwithstanding that thermal RS is capable of providing temporal and spatial information of crop water stress, certain issues must be considered while using thermal RS images. The issues include (1) temporal and spatial resolutions of images acquired, (2) atmospheric conditions, (3) altitude and viewing angle of thermal sensors, and (4) variation in crop species and crop growth stages (Khanal et al., 2017). Among these camera types, thermal imaging cameras are popularly used for water stress detection (Khanal et al., 2017). Some studies have used a combination of multispectral and thermal cameras in assessing crop water stress using some indicators (Zhou et al., 2018; Espinoza et al., 2017). They noted that thermal imaging cameras are useful tool for rapid detection of crop water stress. Considering cost, availability and popularity therefore, thermal imaging cameras seem to be the most suitable for detecting crop water stress (Table 1). Table 1 shows research conducted using UASs with different RS technologies for crop water stress assessment, resolution of the images obtained, altitude of the flight and area covered by UAS.
surface reflects part of the light received. Therefore, multispectral cameras measure reflectance and light absorption from the vegetation canopy (Matese and Di Gennaro, 2018). Vegetation reflectance characteristics are used to derive vegetation indices (VIs). Healthy vegetation absorbs all the red light and reflects back much of the near infrared light while stressed vegetation reflects more red light and less near infrared light. Multispectral VIs used for detecting water status include: structural indices (e.g. Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), SRI etc.); xanthophyll indices (e.g. Photochemical Reflectance Index (PRI570, PRI515)); chlorophyll indices (e.g. Transformed Chlorophyll Absorption in Reflectance Index (TCARI), TCARI/OSAVI etc.); Blue/green/red ratio indices (e.g. Green Index (GI), Red Green Ratio Index (RGRI) etc.) (Baluja et al., 2012; Rallo et al., 2014; Zarco-Tejada et al., 2013, 2012). A new index, PRInorm was proposed by Zarco-Tejada et al. (2013) in which PRI index is normalized to be sensitive to canopy structure and chlorophyll content. PRInorm performed better than standard PRI index when tested against leaf water potential and stomatal conductance. The NDVI which is the index of plant greenness is popularly used among other VIs (Matese and Di Gennaro, 2018; Zhao et al., 2017). It is expressed as the normalized difference between the red and NIR (Near-Infrared) bands hence, it manages the differences in illumination within an image due to aspect and slope as well as differences due to time images were obtained. Baluja et al. (2012) used different spectral indices to assess water status of a vineyard and noted that NDVI and TCARI/OSAVI gave the highest coefficient of determination with stem water potential (Ѱstem) and leaf stomatal conductance (gs). Therefore, the two indices reflect the cumulative water deficit hence long-term response to water status unlike thermal that gives short-term response. Zhao et al. (2017) established a relationship between canopy NDVI and Ѱstem at different growing stages. Multispectral RS imaging technology uses bands such as Green (500–600 nm spectral band), Red (600–700 nm spectral band), RedEdge (700–730 nm) and NIR (700 nm to 1.3 μm) wavebands to capture visible and invisible images of vegetation. According to Poblete et al. (2017), plant water status is not accurately predicted with multispectral indices between 500–800 nm spectral band because of their non-sensitivity to water content however, wavelengths greater than 800 nm may be better. Rallo et al. (2014) noted that satisfactory estimation of leaf water potentials at leaf/canopy levels can be derived using VIs based on the NIR-shortwave infrared domain with specific optimization of the “centre-bands”. Recently, some researchers have developed artificial neural network (ANN) models derived from multispectral images to forecast spatial variability of Ѱstem of crops. Romero et al. (2018) classified the water status level of the grapevine based on ten vegetation indices using Red, Green, Red-Edge and NIR wavebands and found that there were no significant relationships between VIs individually and Ѱstem however with lower correlation values of less than 0.3 for almost all the indices studied. They went further to develop an ANN model (model 1) using all VIs calculated as inputs and compared the output with actual measured Ѱstem and got higher correlation values. Similarly, Poblete et al. (2017) compared ANN model outputs with ground-truth measurements of Ѱstem and found the best performance of the model was with simulations that included the spectral bands of 550, 570, 670, 700 and 800 nm.
3.3.1. Indices for water stress in thermal imaging Several indices exist for monitoring and quantifying water stress based on the crop canopy temperature. These indices include Crop Water Stress Index (CWSI), Degrees Above Canopy Threshold (DACT) and Degrees Above Non-Stressed (DANS) etc., and ‘canopy temperature’ is used as the main driver for evaluating water status (DeJonge et al., 2015). Kullberg et al. (2017) evaluated thermal RS indices and found that crop canopy temperature based water stress indices with less data requirements (DANS and DACT), are sensitive to crop water stress and this is comparable to more data intensive methods, such as CWSI. However, DANS and DACT have not been widely tested like CWSI. A new indicator also based on canopy temperature, CTSD (standard deviation of canopy temperature) was developed by Han et al. (2016). This index was tested using maize crop and found to be non-sensitive to small changes in water stress. Among the indices, CWSI is used as the standard index for quantifying water stress however; it requires additional data, such as vapour pressure deficit (VPD), as well as ideal weather conditions and prior computation (baselines) (Ihuoma and Madramootoo, 2017; Gerhards et al., 2016). In estimating CWSI, emissivity from crop canopy acquired with a thermal imaging camera is used in the computation of water stress related to canopy temperature (Santesteban et al., 2017). According to Quebrajo et al. (2018), CWSI is computed as:
CWSI =
ΔTi − Ci ΔTdry, i − ci
(1)
where ΔTdry, i is the maximum ΔTi , and ΔTi is difference between canopy temperature (Tc) and air temperature (Ta) at the ith measurement moment. DeJonge et al. (2015) noted that accumulation of daily midday difference between (Ta - Tc) throughout a season is linearly proportional to the crop final yield. c is a value representing the non-water-stressed baselines (NWSB). NWSB is a linear function that combines the change in difference between air temperature and canopy temperature when crop transpiration is at potential rate and vapour pressure deficit value simultaneously measures canopy temperature. NWSB denoted as ci is expressed mathematically as:
3.3. Thermal imaging Thermal RS measures the radiation emitted from an object’s surface and converts it into temperature (Khanal et al., 2017). The theory behind thermal RS is that all objects on the earth’s surface with a temperature above - 273 °C emit radiation and the quantity of radiation is a function of the emissivity of the surface temperature (Khanal et al., 2017). Crop canopy surface temperature in general is a function of transpiration rate, available soil water status and atmospheric
ci = a + bVPDi 160
(2)
Thermal
Thermal Thermal
Thermal Thermal
Thermal
Thermal and RGB
Multi-rotor
Multi-rotor Multi-rotor
Multi- copter Multi-rotor
Multi-copter
Fixed-wing
161
150
200
150
150
Thermal and multispectral
Thermal and hyperspectral
Thermal
Thermal and multispectral
Multispectral
Thermal
Fixed-wing
Fixed-wing
Unmanned helicopter –
Unmanned helicopter Unmanned helicopter
575
150
370
Thermal
250 550 250
Fixed-wing
Fixed-wing
10
36
Altitude (m)
Thermal and hyperspectral Thermal and multispectral
90
60
– –
– 90
40
15
30
12
40
20
49
20
35
Resolution (cm/ px)
32
0.04
– 0.97
1.7 7.5
10
– 13 < 9
12
Barley
Olive, peach and orange Olive
– 4
Grapevine
Almond
– 5
Orange and Mandarin
Apricot Orange Peach Almond Lemon Grape
Mandarin and Orange Olive orchards
0.6
1.4
42
7-10
0.6
CWSI
CWSI
CWSI CWSI
CWSI CWSI
CWSI
Map CWSI and canopy conductance
Detect plant water stress
Assess water stress variability
Water stress detection
Water stress detection
Water stress detection
0.73
0.72
0.69
Ѱstem
CWSI
CWSI NDVI PRI TCARI/ OSAVI
Tc–Ta
CWSI PRI PRInorm Tc–Ta PRI515 NDVI
CWSI
CWSI
CWSI
0.82
0.91
0.70 0.75 0.46 0.84
0.59 0.32 0.66
0.38 0.24 0.74 0.52 0.68 0.25 0.58
0.78
−0.4 0.68
gs
0.34
0.59-0.66
Ѱstem
Southern Spain
Southern Spain
Logrono, Spain
California, USA
Seville, Spain
California, USA
Murcia, Spain
Southern Spain
Seville, Spain
Zarco-Tejada et al. (2009) Berni et al. (2009)
Gonzalez-Dugo et al. (2012) Baluja et al. (2012)
Zarco-Tejada et al. (2012)
Zarco-Tejada et al. (2013)
Gonzalez-Dugo et al. (2013)
Gonzalez-Dugo et al. (2014) Calderón et al. (2013)
Reference
Sepúlveda-Reyes et al. (2016) Hoffmann et al. (2016)
Ford et al. (2017) Park et al. (2017)
Mesas-Carrascosa et al. (2018) Matese et al. (2018) Santesteban et al. (2017)
Quebrajo et al. (2018)
Reference
Study location
Western Denmark
Maule region, Chile
Sardinia, Italy Traibuenas, Navarra, Spain Maryland, USA Tatura, Australia
Córdoba, Spain
Cadiz, Spain
Study location
Correlation (R2)
0.82
0.82
0.71
gs
Correlation (R2)
Some Indices used
Some Indices used
Testing the hypothesis that the CWSI is a reliable indicator Water stress detection caused by Verticillium wilt (VW) infection and severity To characterize the spatial variations in water stress of five fruit tree species
Objective
Water stress detection
Assess vine water stress conditions
To determine and analyze CWSI Water stress detection
Assessing crop water stress using thermal drift correction models Optimizing irrigation scheduling Estimation of plant water stress
To predict variations in crop water use
Purpose
Type of Crop
Corn Nectarine and peach Grape
Grape Grape
Olive trees
Sugar beet
Type of Crop
Area (Ha)
Area (Ha)
8
Resolution (cm/ px)
100 70
5, 10, 20, 30, and 40 120
Altitude (m)
Fixed-wing
Camera
Thermal
Multi-rotor
Type of UAS
Camera
Type of UAS
Table 1 Studies conducted using UASs with different RS technologies for assessment of crop water stress.
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carrying a thermal camera sensor to estimate evapotranspiration. The evapotranspiration information obtained was aggregated to irrigation valve zones that the irrigation technology supports. Reliable data and information acquired rapidly and easily are therefore essential for assessing water use productivity.
where a and b are empirical parameters obtained for each species in the study environment; VPDi is the vapour pressure deficit (kPa) at the moment of flight on the ith measurement day. According to García-Tejero et al. (2018a), thermal imagery information is better interpreted using NWSB. They evaluated the potential of NWSB obtained from thermal imagery in order to establish a practical protocol for decisions related to irrigation management in almond plantations. Some studies have evaluated CWSI indicator in some crops, sugar beet plants (Quebrajo et al., 2018), grape (Matese et al., 2018; Pou et al., 2014; García-Tejero et al., 2016; Santesteban et al., 2017), olive orchard (Egea et al., 2017), Euonymus japonica (euonymus) plants (Gómez-Bellot et al., 2015), citrus (Gonzalez-Dugo et al., 2014), maize (DeJonge et al., 2015) and found that it is a valuable indicator for assessing spatial variability of crop water status. CWSI derived from thermal imagery has also been deployed for high spatial and temporal monitoring of water stress of the corn canopy in greenhouse (Mangus et al., 2016). Results obtained indicate that CWSI estimated from thermal canopy temperature can be used to quantify spatial and temporal soil moisture variability for irrigation scheduling. In addition to water stress detection using crop temperature, thermal imaging cameras can also detect water stress using soil moisture index and evapotranspiration algorithms (Khanal et al., 2017). Evapotranspiration algorithms have been successfully applied to estimate daily evapotranspiration from soybean and corn (Khanal et al., 2017).
5. Conclusion CWP exhibit high spatial and temporal variability due to climatic and agricultural factors. To improve CWP, emphasis should be more on the climatic factors as well as on irrigation efficiency. UAS technologies with these commonly used cameras: hyperspectral, multispectral and thermal can improve CWP. Among these cameras, UAS thermal RS is identified as a valuable tool for monitoring and assessing crop water status. UAS thermal imaging cameras are used to estimate climatic factors that can improve CWP. Thermal RS determines the crop canopy temperature as a way of quantifying crop water stress. Some indices have been identified and used for quantifying crop water status using thermal RS. Among these indices, CWSI is widely used in quantifying and assessing crop water stress despite its data demand compared to other indices. CWSI is known to vary in space and time therefore, UAS thermal imaging cameras have the potential to account for spatial and temporal variability associated with CWSI. In addition to water stress detection using crop temperature, thermal imaging cameras can also detect water stress using soil moisture index and evapotranspiration algorithms. Irrigation technologies that improve CWP should be adopted in order to meet the global food demand associated with increasing global populations. Conventional irrigation systems alone are not enough to improve CWP to feed the projected human population increase, especially in the face of climate change. Recently, researchers have tried to improve CWP by developing an adaptive irrigation scheduling algorithm that relied on theoretical CWSI obtained from thermal imaging cameras mounted on the linear or centre pivot irrigation system. This method failed to account properly for the spatial and temporal variation associated with crop water stress. Similarly, UAS have also been used for quasi-time irrigation scheduling where the crop water stress information obtained is interpreted and used much later after the measurement. For maximum CWP while accounting for spatial and temporal variability of crop water status, there is need for real-time irrigation scheduling using UAS carrying thermal imaging cameras. Real-time canopy temperature, the main driver of CWSI is estimated automatically from thermal images. With the water stress information from thermal imaging irrigation controllers can be communicated for real time irrigation scheduling thereby producing maximum crop water productivity.
4. Real-time irrigation scheduling with thermal UAS Recent advances in irrigation technology have platforms for continuous transmitting of data among irrigation controllers, installed field sensors as well as equipment for variable irrigation rate (Quebrajo et al., 2018). Thermally-based techniques currently employed in evaluating water stress for automatic irrigation scheduling include crop canopy temperature, CWSI (empirical or theoretical), and time temperature threshold (O’Shaughnessy and Evett, 2010). The CWSI algorithm is mostly used for automatic irrigation control systems in order to improve crop water productivity. O’Shaughnessy et al. (2012) developed a method that integrated CWSI and time threshold for automatic irrigation scheduling. The effectiveness of the method was investigated on short and long season grain sorghum hybrids. They noted that the method can effectively trigger the irrigation system for automatic irrigation scheduling. Also, Osroosh et al. (2015) developed an adaptive scheduling algorithm that relied on theoretical CWSI to automatically irrigate apple trees. Most of these studies utilized the mobility of linear or center pivot irrigation systems to mount thermal imaging cameras thereby getting a dynamic scan of the effects of canopy temperature (DeJonge et al., 2015). CWSI obtained from UAS thermal imaging cameras can be adapted for real-time irrigation scheduling for maximum crop water productivity. Real-time canopy temperature, the main driver of CWSI, is better estimated automatically from thermal images. Jiménez-Bello et al. (2011) developed and validated an automatic UAS thermal imaging process for assessing plant water status that requires no operator participation. With this, the UAS thermal imaging techniques, coupled with software platforms, will not only estimate the spatial and temporal crop water stress variation, but drastically reduce the time needed between image processing, analysis and taking action. With the water stress information from thermal imaging either from canopy temperature, soil moisture index or evapotranspiration algorithms, irrigation controllers can be communicated for real time irrigation scheduling to improve water use productivity. CWSI from thermal imagery has great potential to detect instantaneous variations of water status (Santesteban et al., 2017; Osroosh et al., 2016). Bellvert et al. (2016) used a UAS with a thermal sensor to fly over grape vineyards and obtained water stress information for real-time irrigation scheduling. Leaf water potential obtained from the CWSI was successfully used as an irrigation trigger and there was no negative effect on yield and composition of the crop. Torres-Rua (2017) also used UAS
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Review
Current and potential capabilities of UAS for crop water productivity in precision agriculture
T
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G.I. Ezennea, , Louise Juppb, S.K. Mantela, J.L. Tannera a b
Institute for Water Research, Rhodes University, South Africa Terreco Aviation, 123 Western Avenue, Vincent, East London, South Africa
A R T I C LE I N FO
A B S T R A C T
Keywords: Agricultural water use Thermal imaging Real-time irrigation scheduling
In order to feed growing populations under scare water resources, a suitable technology that improves crop water productivity (CWP) is crucial. Precision agriculture that utilizes digital techniques such as unmanned aerial systems (UAS) can play a significant role in improving CWP. CWP is an important indicator that quantifies the effect of agricultural water management. To improve CWP, implementation of suitable methods for early detection of crop water stress before irreversible damage on crops occurs, is vital. Conventionally, farmers have relied on in situ measurements of soil moisture and weather variables for detecting crop water status for irrigation scheduling. This method is time consuming and does not account for spatial and temporal variability associated with crop water status. Hence, the aim of this study is to give an overview of the current and potential capabilities of UAS for crop water productivity in precision agriculture. Identified in this study are the factors as well as the technology that can improve CWP. UAS thermal remote sensing is found to be the most suitable technology for monitoring and assessing crop water status using certain indices. Determining a crop water stress index (CWSI) from thermal imagery has the potential to detect instantaneous variations of water status. CWSI obtained from UAS thermal imaging camera can be adapted for real-time irrigation scheduling for maximum crop water productivity.
1. Introduction By 2050 agricultural production must double to meet the food demand of the growing population (Martínez et al., 2017). The agricultural industry must therefore, maximize the use of available resources in order to cope with the present trajectory of population growth and, precision agriculture can play a significant role in reaching this goal. Precision agriculture is a farming management concept where a practice is performed at the right time, right place and appropriate intensity (Maes and Steppe, 2019). It often utilizes digital techniques (satellite, UAS, sensors, etc.) to optimize agricultural production processes while minimizing adverse environmental impacts (Schrijver, 2016; Abdullahi et al., 2015). Sensors commonly used in precision irrigation systems such as drip irrigation reduce crop water use but provide only point estimates. When digital techniques such as UAS are utilized in precision agriculture, the spatial and temporal variability of the crop conditions are accounted for in greater detail than commonly used sensor technologies. Precision agriculture generally aims to increase the quality and quantity of agricultural output while using less input (e.g. water, pesticides, fertilizer, energy, herbicides etc.).
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Agricultural water use is one of the main factors behind increasing water scarcity, with irrigated agriculture accounting for about 70% of the freshwater withdrawals in the world (Tshwene and Oladele, 2016). With water being a significant limiting factor in agricultural management, this high agricultural water use can be better managed using the crop water productivity concept. CWP also referred to as water use efficiency, can be defined as the relationship between crop output and the amount of water used in crop production. It is mathematically expressed as the quantity of yield (kg/hectare) per unit amount of water (mm/day) consumed (i.e. crop evapotranspiration) (Jin et al., 2016; Araya et al., 2018; Kukal and Irmak, 2017; Brauman et al., 2013; Dogaru, 2016; Humphreys et al., 2006; Nhamo et al., 2016; Wichelns, 2014). CWP is an indicator that quantifies the effect of agricultural water management (Sakthivadivel et al., 1999; Sun et al., 2017). The use of CWP helps to meet the rising demand of water by irrigation amidst other sectors and also provide food for the growing population. According to Igbadun et al. (2006), there are many expressions and definitions for quantifying CWP, such as water use (technical) efficiency, which is defined as quantity of agricultural output per unit amount of water used in the production; water use (economic) efficiency, which is
Corresponding author. E-mail address: [email protected] (G.I. Ezenne).
https://doi.org/10.1016/j.agwat.2019.03.034 Received 22 November 2018; Received in revised form 7 March 2019; Accepted 25 March 2019 0378-3774/ © 2019 Elsevier B.V. All rights reserved.
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platforms have some limitations and advantages. To mention a few limitations and advantages, unmanned helicopters are able to travel in any direction while fixed wing airplanes travel in a more linear and restricted fashion. Fixed-wing aircrafts have simple flight systems and can fly for longer duration compared to unmanned helicopters. Unmanned helicopters have more complex flight systems and can offer lower flight altitudes with low-speed. Fixed-wing aircrafts therefore, have capacity to cover wider areas but their flight altitude is higher which reduces the image resolution. Also, fixed-wing UAS aerial platforms require specific runways or at least sufficient open space for landing and take-off. Because of these limitations, recently a new aircraft platform emerged, multi-copter (multi-rotor), which does not require special take-off or landing runway. The multi-copter is more userfriendly which makes it possible to be operated by farmers in order to obtain real-time data and apply adapted water management. Generally, the type of UAS required depends on the aim of the study as well as the quality of the desired output/result. In addition to UAS aerial platforms, the camera sensors are key to the quality of images produced. Selection of camera type also depends on the aim of the project. The most commonly used UAS cameras in agriculture include: thermal, multispectral, hyperspectral and redgreen-blue (RGB). Gago et al. (2015) recommended different camera types depending on the type of crop trait/status of interest. For biotic and abiotic stress, thermal and hyperspectral cameras are recommended; while multispectral and red-green-blue (RGB) cameras are recommended for growth/biomass assessment. RGB cameras are one of the traditional imaging systems used in agroforestry applications, however they lack the precision and spectral range to profile materials that hyperspectral and multispectral cameras can provide (Adão et al., 2017).
the value of the output produced per quantity of water consumed, and; water use (hydraulic) efficiency, which is defined as the ratio of water consumed by irrigated agriculture to the volume of water supplied. These three CWP definitions are centred on minimizing water input while maximizing the crop yield/output. Deficit irrigation techniques have been found to improve CWP by more than 200% (Zwart and Bastiaanssen, 2004; Hirich et al., 2014; Adeboye et al., 2015) if irrigation is timeously managed. However, deficit irrigation must be carefully managed as Igbadun et al. (2006) described an attempt to maximize CWP by withholding irrigation in multiple crop growth stages which resulted in significant reduction in crop yield. Zwart and Bastiaanssen (2004), however, found that plants are more efficient with water when they are stressed and agricultural production can be sustained with 20–40% less water resources as long as new water management techniques are adopted. The agricultural sector is currently under pressure to produce high quantity of food while using less water per unit of output thereby increasing CWP (Tshwene and Oladele, 2016). This is achieved by either producing the same quantity of crop with less water resources, or a higher quantity of crop with the same quantity of water resources (Zwart and Bastiaanssen, 2004). Either way, CWP is generally derived from the relationship between the water consumption and yield of a particular crop, both of which are highly variable in space and time (Kukal and Irmak, 2017). CWP can exhibit high spatial and temporal variability due to climatic and agricultural factors (Mdemu et al., 2009). Among the climatic factors, CWP is more sensitive to wind speed and relative humidity compared to sunshine hours while for agricultural factors, it is more sensitive to irrigation efficiency than the quantity of fertilizer used (Sun et al., 2017). Therefore, to improve CWP emphasis should focus on the climatic factors and on irrigation efficiency. Existing techniques for improving CWP such as partial root zone drying (Barrios-Masias and Jackson, 2016), optimized regulated deficit irrigation (Léllis et al., 2017; Gendron et al., 2018; Adeboye et al., 2015) etc. do not currently account for the integrated effects of climatic and irrigation efficiency factors as well as spatial variability of CWP. Unmanned aerial systems (UAS) technology offers high spatial resolution and rapid collection of data over large areas, potentially enabling farmers to increase the precision of irrigation both spatially and temporally. Therefore, the aim of this study is to give a general overview of the current and potential capabilities of UAS to improve crop water productivity under precision agriculture.
3. Commonly used UAS imaging cameras for water status 3.1. Hyperspectral Hyperspectral imaging techniques measure the reflectance from the vegetation canopy. The recorded reflectance cannot be applied directly as a metric of leaf water content therefore reflectance indices are used. Indices for hyperspectral data include Simple Ratio Index (SRI), Normalized Difference (e.g. Photochemical Reflectance Index (PRI)) etc. (Elvanidi et al., 2018; Pôças et al., 2017; Rapaport et al., 2015). Hyperspectral technology integrates spectroscopy with the benefits of digital imagery (Loggenberg et al., 2018). The capability of hyperspectral imaging ranging from 400 to 2500 nm in assessing the water deficiency in tomato plants was demonstrated by Susič et al. (2018). Zarco-Tejada et al. (2012) demonstrated the ability of UAV based hyperspectral and thermal cameras to assess water stress levels in a citrus crop and confirmed that there is link between PRI and canopy temperature. Recently, Loggenberg et al. (2018) combined hyperspectral RS technologies with machine learning to discriminate between stressed and non-stressed grape vines from hyperspectral imaging. The results obtained showed that machine (ensemble) learners effectively analysed the hyperspectral data. Generally, hyperspectral imaging cameras capture more detailed data in both spatial and spectral ranges compared to other cameras. It can measure hundreds of bands which raises complexity when considering the quantity of data acquired. Loggenberg et al. (2018) noted that the major limiting factor in applying hyperspectral data is the inherent ‘curse of dimensionality’ which results in reduced classification accuracies. In addition, hyperspectral cameras are very expensive and complex (Elvanidi et al., 2018), which limit their wide-spread application especially in commercial agriculture.
2. UAS in precision agriculture UAS are platforms for remote sensing (RS) used in precision agriculture and examples of their use are found in (Hunt et al., 2018; Adão et al., 2017; Bendig et al., 2012; Santesteban et al., 2017; Poblete et al., 2017; Muchiri and Kimathi, 2016; Abdullahi et al., 2015; Simelli and Apostolos, 2015; Whitehead et al., 2014; Whitehead and Hugenholtz, 2014; Manfreda et al., 2018). These platforms are evolving rapidly technically and with regard to regulations (Ballesteros et al., 2014). UASs recently have become a common remote sensing technology comprising aerial platforms suitable for carrying small and lightweight sensors. There are different classes of UAS for instance according to Whitehead and Hugenholtz (2014), in United States it ranges from micro (< 0.9 kg), mini (0.9–13.6 kg), tactical (13.6–454.5 kg), medium altitude long endurance (454.5 – 13,636.4 kg), and high altitude long endurance (> 13,636.4 kg). They noted that commercial and remote sensing applications prefer UAS weighing less than 5 kg because of cost advantages and reduced risk associated with blunt force impact. MiniUAS weighing less than 5 kg can carry 0.2 kg to 1.5 kg of sensor payload (Bendig et al., 2012). According to Gago et al. (2015), four component tasks to be considered before selecting the appropriate UAS for precision agriculture are: design of experiment, data acquisition, data processing and results. For agricultural management, fixed-wing airplanes and unmanned helicopters UASs are mainly used. These two aerial
3.2. Multispectral Multispectral imaging techniques are based on the theory that each 159
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evaporative demand (Khanal et al., 2017). Therefore, thermal RS determines the crop canopy surface temperature as a way of quantifying crop water stress which is useful in irrigation scheduling. Additionally, thermal RS can also quantify soil moisture and evapotranspiration status. Many studies have used thermal RS to assess crop water stress for several crop species including citrus orchards (Zarco-Tejada et al., 2012), pinto beans (Zhou et al., 2018), sugar beet plants (Quebrajo et al., 2018), almond trees (García-Tejero et al., 2018a & 2018b), and grapevines (Bellvert et al., 2014; Bellvert et al., 2016; Espinoza et al., 2017; Matese et al., 2018; García-Tejero et al., 2016). Notwithstanding that thermal RS is capable of providing temporal and spatial information of crop water stress, certain issues must be considered while using thermal RS images. The issues include (1) temporal and spatial resolutions of images acquired, (2) atmospheric conditions, (3) altitude and viewing angle of thermal sensors, and (4) variation in crop species and crop growth stages (Khanal et al., 2017). Among these camera types, thermal imaging cameras are popularly used for water stress detection (Khanal et al., 2017). Some studies have used a combination of multispectral and thermal cameras in assessing crop water stress using some indicators (Zhou et al., 2018; Espinoza et al., 2017). They noted that thermal imaging cameras are useful tool for rapid detection of crop water stress. Considering cost, availability and popularity therefore, thermal imaging cameras seem to be the most suitable for detecting crop water stress (Table 1). Table 1 shows research conducted using UASs with different RS technologies for crop water stress assessment, resolution of the images obtained, altitude of the flight and area covered by UAS.
surface reflects part of the light received. Therefore, multispectral cameras measure reflectance and light absorption from the vegetation canopy (Matese and Di Gennaro, 2018). Vegetation reflectance characteristics are used to derive vegetation indices (VIs). Healthy vegetation absorbs all the red light and reflects back much of the near infrared light while stressed vegetation reflects more red light and less near infrared light. Multispectral VIs used for detecting water status include: structural indices (e.g. Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), SRI etc.); xanthophyll indices (e.g. Photochemical Reflectance Index (PRI570, PRI515)); chlorophyll indices (e.g. Transformed Chlorophyll Absorption in Reflectance Index (TCARI), TCARI/OSAVI etc.); Blue/green/red ratio indices (e.g. Green Index (GI), Red Green Ratio Index (RGRI) etc.) (Baluja et al., 2012; Rallo et al., 2014; Zarco-Tejada et al., 2013, 2012). A new index, PRInorm was proposed by Zarco-Tejada et al. (2013) in which PRI index is normalized to be sensitive to canopy structure and chlorophyll content. PRInorm performed better than standard PRI index when tested against leaf water potential and stomatal conductance. The NDVI which is the index of plant greenness is popularly used among other VIs (Matese and Di Gennaro, 2018; Zhao et al., 2017). It is expressed as the normalized difference between the red and NIR (Near-Infrared) bands hence, it manages the differences in illumination within an image due to aspect and slope as well as differences due to time images were obtained. Baluja et al. (2012) used different spectral indices to assess water status of a vineyard and noted that NDVI and TCARI/OSAVI gave the highest coefficient of determination with stem water potential (Ѱstem) and leaf stomatal conductance (gs). Therefore, the two indices reflect the cumulative water deficit hence long-term response to water status unlike thermal that gives short-term response. Zhao et al. (2017) established a relationship between canopy NDVI and Ѱstem at different growing stages. Multispectral RS imaging technology uses bands such as Green (500–600 nm spectral band), Red (600–700 nm spectral band), RedEdge (700–730 nm) and NIR (700 nm to 1.3 μm) wavebands to capture visible and invisible images of vegetation. According to Poblete et al. (2017), plant water status is not accurately predicted with multispectral indices between 500–800 nm spectral band because of their non-sensitivity to water content however, wavelengths greater than 800 nm may be better. Rallo et al. (2014) noted that satisfactory estimation of leaf water potentials at leaf/canopy levels can be derived using VIs based on the NIR-shortwave infrared domain with specific optimization of the “centre-bands”. Recently, some researchers have developed artificial neural network (ANN) models derived from multispectral images to forecast spatial variability of Ѱstem of crops. Romero et al. (2018) classified the water status level of the grapevine based on ten vegetation indices using Red, Green, Red-Edge and NIR wavebands and found that there were no significant relationships between VIs individually and Ѱstem however with lower correlation values of less than 0.3 for almost all the indices studied. They went further to develop an ANN model (model 1) using all VIs calculated as inputs and compared the output with actual measured Ѱstem and got higher correlation values. Similarly, Poblete et al. (2017) compared ANN model outputs with ground-truth measurements of Ѱstem and found the best performance of the model was with simulations that included the spectral bands of 550, 570, 670, 700 and 800 nm.
3.3.1. Indices for water stress in thermal imaging Several indices exist for monitoring and quantifying water stress based on the crop canopy temperature. These indices include Crop Water Stress Index (CWSI), Degrees Above Canopy Threshold (DACT) and Degrees Above Non-Stressed (DANS) etc., and ‘canopy temperature’ is used as the main driver for evaluating water status (DeJonge et al., 2015). Kullberg et al. (2017) evaluated thermal RS indices and found that crop canopy temperature based water stress indices with less data requirements (DANS and DACT), are sensitive to crop water stress and this is comparable to more data intensive methods, such as CWSI. However, DANS and DACT have not been widely tested like CWSI. A new indicator also based on canopy temperature, CTSD (standard deviation of canopy temperature) was developed by Han et al. (2016). This index was tested using maize crop and found to be non-sensitive to small changes in water stress. Among the indices, CWSI is used as the standard index for quantifying water stress however; it requires additional data, such as vapour pressure deficit (VPD), as well as ideal weather conditions and prior computation (baselines) (Ihuoma and Madramootoo, 2017; Gerhards et al., 2016). In estimating CWSI, emissivity from crop canopy acquired with a thermal imaging camera is used in the computation of water stress related to canopy temperature (Santesteban et al., 2017). According to Quebrajo et al. (2018), CWSI is computed as:
CWSI =
ΔTi − Ci ΔTdry, i − ci
(1)
where ΔTdry, i is the maximum ΔTi , and ΔTi is difference between canopy temperature (Tc) and air temperature (Ta) at the ith measurement moment. DeJonge et al. (2015) noted that accumulation of daily midday difference between (Ta - Tc) throughout a season is linearly proportional to the crop final yield. c is a value representing the non-water-stressed baselines (NWSB). NWSB is a linear function that combines the change in difference between air temperature and canopy temperature when crop transpiration is at potential rate and vapour pressure deficit value simultaneously measures canopy temperature. NWSB denoted as ci is expressed mathematically as:
3.3. Thermal imaging Thermal RS measures the radiation emitted from an object’s surface and converts it into temperature (Khanal et al., 2017). The theory behind thermal RS is that all objects on the earth’s surface with a temperature above - 273 °C emit radiation and the quantity of radiation is a function of the emissivity of the surface temperature (Khanal et al., 2017). Crop canopy surface temperature in general is a function of transpiration rate, available soil water status and atmospheric
ci = a + bVPDi 160
(2)
Thermal
Thermal Thermal
Thermal Thermal
Thermal
Thermal and RGB
Multi-rotor
Multi-rotor Multi-rotor
Multi- copter Multi-rotor
Multi-copter
Fixed-wing
161
150
200
150
150
Thermal and multispectral
Thermal and hyperspectral
Thermal
Thermal and multispectral
Multispectral
Thermal
Fixed-wing
Fixed-wing
Unmanned helicopter –
Unmanned helicopter Unmanned helicopter
575
150
370
Thermal
250 550 250
Fixed-wing
Fixed-wing
10
36
Altitude (m)
Thermal and hyperspectral Thermal and multispectral
90
60
– –
– 90
40
15
30
12
40
20
49
20
35
Resolution (cm/ px)
32
0.04
– 0.97
1.7 7.5
10
– 13 < 9
12
Barley
Olive, peach and orange Olive
– 4
Grapevine
Almond
– 5
Orange and Mandarin
Apricot Orange Peach Almond Lemon Grape
Mandarin and Orange Olive orchards
0.6
1.4
42
7-10
0.6
CWSI
CWSI
CWSI CWSI
CWSI CWSI
CWSI
Map CWSI and canopy conductance
Detect plant water stress
Assess water stress variability
Water stress detection
Water stress detection
Water stress detection
0.73
0.72
0.69
Ѱstem
CWSI
CWSI NDVI PRI TCARI/ OSAVI
Tc–Ta
CWSI PRI PRInorm Tc–Ta PRI515 NDVI
CWSI
CWSI
CWSI
0.82
0.91
0.70 0.75 0.46 0.84
0.59 0.32 0.66
0.38 0.24 0.74 0.52 0.68 0.25 0.58
0.78
−0.4 0.68
gs
0.34
0.59-0.66
Ѱstem
Southern Spain
Southern Spain
Logrono, Spain
California, USA
Seville, Spain
California, USA
Murcia, Spain
Southern Spain
Seville, Spain
Zarco-Tejada et al. (2009) Berni et al. (2009)
Gonzalez-Dugo et al. (2012) Baluja et al. (2012)
Zarco-Tejada et al. (2012)
Zarco-Tejada et al. (2013)
Gonzalez-Dugo et al. (2013)
Gonzalez-Dugo et al. (2014) Calderón et al. (2013)
Reference
Sepúlveda-Reyes et al. (2016) Hoffmann et al. (2016)
Ford et al. (2017) Park et al. (2017)
Mesas-Carrascosa et al. (2018) Matese et al. (2018) Santesteban et al. (2017)
Quebrajo et al. (2018)
Reference
Study location
Western Denmark
Maule region, Chile
Sardinia, Italy Traibuenas, Navarra, Spain Maryland, USA Tatura, Australia
Córdoba, Spain
Cadiz, Spain
Study location
Correlation (R2)
0.82
0.82
0.71
gs
Correlation (R2)
Some Indices used
Some Indices used
Testing the hypothesis that the CWSI is a reliable indicator Water stress detection caused by Verticillium wilt (VW) infection and severity To characterize the spatial variations in water stress of five fruit tree species
Objective
Water stress detection
Assess vine water stress conditions
To determine and analyze CWSI Water stress detection
Assessing crop water stress using thermal drift correction models Optimizing irrigation scheduling Estimation of plant water stress
To predict variations in crop water use
Purpose
Type of Crop
Corn Nectarine and peach Grape
Grape Grape
Olive trees
Sugar beet
Type of Crop
Area (Ha)
Area (Ha)
8
Resolution (cm/ px)
100 70
5, 10, 20, 30, and 40 120
Altitude (m)
Fixed-wing
Camera
Thermal
Multi-rotor
Type of UAS
Camera
Type of UAS
Table 1 Studies conducted using UASs with different RS technologies for assessment of crop water stress.
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carrying a thermal camera sensor to estimate evapotranspiration. The evapotranspiration information obtained was aggregated to irrigation valve zones that the irrigation technology supports. Reliable data and information acquired rapidly and easily are therefore essential for assessing water use productivity.
where a and b are empirical parameters obtained for each species in the study environment; VPDi is the vapour pressure deficit (kPa) at the moment of flight on the ith measurement day. According to García-Tejero et al. (2018a), thermal imagery information is better interpreted using NWSB. They evaluated the potential of NWSB obtained from thermal imagery in order to establish a practical protocol for decisions related to irrigation management in almond plantations. Some studies have evaluated CWSI indicator in some crops, sugar beet plants (Quebrajo et al., 2018), grape (Matese et al., 2018; Pou et al., 2014; García-Tejero et al., 2016; Santesteban et al., 2017), olive orchard (Egea et al., 2017), Euonymus japonica (euonymus) plants (Gómez-Bellot et al., 2015), citrus (Gonzalez-Dugo et al., 2014), maize (DeJonge et al., 2015) and found that it is a valuable indicator for assessing spatial variability of crop water status. CWSI derived from thermal imagery has also been deployed for high spatial and temporal monitoring of water stress of the corn canopy in greenhouse (Mangus et al., 2016). Results obtained indicate that CWSI estimated from thermal canopy temperature can be used to quantify spatial and temporal soil moisture variability for irrigation scheduling. In addition to water stress detection using crop temperature, thermal imaging cameras can also detect water stress using soil moisture index and evapotranspiration algorithms (Khanal et al., 2017). Evapotranspiration algorithms have been successfully applied to estimate daily evapotranspiration from soybean and corn (Khanal et al., 2017).
5. Conclusion CWP exhibit high spatial and temporal variability due to climatic and agricultural factors. To improve CWP, emphasis should be more on the climatic factors as well as on irrigation efficiency. UAS technologies with these commonly used cameras: hyperspectral, multispectral and thermal can improve CWP. Among these cameras, UAS thermal RS is identified as a valuable tool for monitoring and assessing crop water status. UAS thermal imaging cameras are used to estimate climatic factors that can improve CWP. Thermal RS determines the crop canopy temperature as a way of quantifying crop water stress. Some indices have been identified and used for quantifying crop water status using thermal RS. Among these indices, CWSI is widely used in quantifying and assessing crop water stress despite its data demand compared to other indices. CWSI is known to vary in space and time therefore, UAS thermal imaging cameras have the potential to account for spatial and temporal variability associated with CWSI. In addition to water stress detection using crop temperature, thermal imaging cameras can also detect water stress using soil moisture index and evapotranspiration algorithms. Irrigation technologies that improve CWP should be adopted in order to meet the global food demand associated with increasing global populations. Conventional irrigation systems alone are not enough to improve CWP to feed the projected human population increase, especially in the face of climate change. Recently, researchers have tried to improve CWP by developing an adaptive irrigation scheduling algorithm that relied on theoretical CWSI obtained from thermal imaging cameras mounted on the linear or centre pivot irrigation system. This method failed to account properly for the spatial and temporal variation associated with crop water stress. Similarly, UAS have also been used for quasi-time irrigation scheduling where the crop water stress information obtained is interpreted and used much later after the measurement. For maximum CWP while accounting for spatial and temporal variability of crop water status, there is need for real-time irrigation scheduling using UAS carrying thermal imaging cameras. Real-time canopy temperature, the main driver of CWSI is estimated automatically from thermal images. With the water stress information from thermal imaging irrigation controllers can be communicated for real time irrigation scheduling thereby producing maximum crop water productivity.
4. Real-time irrigation scheduling with thermal UAS Recent advances in irrigation technology have platforms for continuous transmitting of data among irrigation controllers, installed field sensors as well as equipment for variable irrigation rate (Quebrajo et al., 2018). Thermally-based techniques currently employed in evaluating water stress for automatic irrigation scheduling include crop canopy temperature, CWSI (empirical or theoretical), and time temperature threshold (O’Shaughnessy and Evett, 2010). The CWSI algorithm is mostly used for automatic irrigation control systems in order to improve crop water productivity. O’Shaughnessy et al. (2012) developed a method that integrated CWSI and time threshold for automatic irrigation scheduling. The effectiveness of the method was investigated on short and long season grain sorghum hybrids. They noted that the method can effectively trigger the irrigation system for automatic irrigation scheduling. Also, Osroosh et al. (2015) developed an adaptive scheduling algorithm that relied on theoretical CWSI to automatically irrigate apple trees. Most of these studies utilized the mobility of linear or center pivot irrigation systems to mount thermal imaging cameras thereby getting a dynamic scan of the effects of canopy temperature (DeJonge et al., 2015). CWSI obtained from UAS thermal imaging cameras can be adapted for real-time irrigation scheduling for maximum crop water productivity. Real-time canopy temperature, the main driver of CWSI, is better estimated automatically from thermal images. Jiménez-Bello et al. (2011) developed and validated an automatic UAS thermal imaging process for assessing plant water status that requires no operator participation. With this, the UAS thermal imaging techniques, coupled with software platforms, will not only estimate the spatial and temporal crop water stress variation, but drastically reduce the time needed between image processing, analysis and taking action. With the water stress information from thermal imaging either from canopy temperature, soil moisture index or evapotranspiration algorithms, irrigation controllers can be communicated for real time irrigation scheduling to improve water use productivity. CWSI from thermal imagery has great potential to detect instantaneous variations of water status (Santesteban et al., 2017; Osroosh et al., 2016). Bellvert et al. (2016) used a UAS with a thermal sensor to fly over grape vineyards and obtained water stress information for real-time irrigation scheduling. Leaf water potential obtained from the CWSI was successfully used as an irrigation trigger and there was no negative effect on yield and composition of the crop. Torres-Rua (2017) also used UAS
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