Remote Sensing for Irrigation of Horticultural Crops

Monitoring soil moisture in winter wheat with crop water stress index based on canopy-air temperature time lag effect

Crop water stress index (CWSI) has been widely used in soil moisture monitoring. However, the influence of the time lag effect between canopy temperature and air temperature on the accuracy of soil moisture monitoring with different CWSI models has not been further investigated. Therefore, based on the continuous record of canopy temperature and air temperature, this study explored the influence of canopy-air temperature hysteresis on the diagnosis of soil moisture with three CWSI models (CWSIT-theoretical, CWSIE-empirical, CWSIH-hybrid). The results show (1) the peak time of canopy temperature was ahead of that of air temperature, and the lag time varied under different soil moisture conditions. When the soil moisture was seriously deficient, the lag time decreased. However, from jointing-heading period to filling-ripening period, the lag time became longer. (2) The values of CWSIT, CWSIE, and CWSIH decreased when the time lag effect was considered. In jointing-heading period, heading-filling period, and filling-ripening period, CWSIT had the highest accuracy in soil moisture monitoring without the consideration of the time lag effect. When the time lag effect was considered, the monitoring accuracy of CWSIE and CWSIH was greatly improved and higher than that of CWSIT, while that of CWSIT was reduced. The findings provided a basis for further improving the accuracy of soil moisture monitoring with CWSI models.

Rice Crop Counting Using Aerial Imagery and GIS for the Assessment of Soil Health to Increase Crop Yield

Rice is one of the vital foods consumed in most countries throughout the world. To estimate the yield, crop counting is used to indicate improper growth, identification of loam land, and control of weeds. It is becoming necessary to grow crops healthy, precisely, and proficiently as the demand increases for food supplies. Traditional counting methods have numerous disadvantages, such as long delay times and high sensitivity, and they are easily disturbed by noise. In this research, the detection and counting of rice plants using an unmanned aerial vehicle (UAV) and aerial images with a geographic information system (GIS) are used. The technique is implemented in the area of forty acres of rice crop in Tando Adam, Sindh, Pakistan. To validate the performance of the proposed system, the obtained results are compared with the standard plant count techniques as well as approved by the agronomist after testing soil and monitoring the rice crop count in each acre of land of rice crops. From the results, it is found that the proposed system is precise and detects rice crops accurately, differentiates from other objects, and estimates the soil health based on plant counting data; however, in the case of clusters, the counting is performed in semi-automated mode.

A Novel Hybrid GOA-XGB Model for Estimating Wheat Aboveground Biomass Using UAV-Based Multispectral Vegetation Indices

The rapid and nondestructive determination of wheat aboveground biomass (AGB) is important for accurate and efficient agricultural management. In this study, we established a novel hybrid model, known as extreme gradient boosting (XGBoost) optimization using the grasshopper optimization algorithm (GOA-XGB), which could accurately determine an ideal combination of vegetation indices (VIs) for simulating wheat AGB. Five multispectral bands of the unmanned aerial vehicle platform and 56 types of VIs obtained based on the five bands were used to drive the new model. The GOA-XGB model was compared with many state-of-the-art models, for example, multiple linear regression (MLR), multilayer perceptron (MLP), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), random forest (RF), support vector machine (SVM), XGBoost, SVM optimization by particle swarm optimization (PSO), SVM optimization by the whale optimization algorithm (WOA), SVM optimization by the GOA (GOA-SVM), XGBoost optimization by PSO, XGBoost optimization by the WOA. The results demonstrated that MLR and GOA-MLR models had poor prediction accuracy for AGB, and the accuracy did not significantly improve when input factors were more than three. Among single-factor-driven machine learning (ML) models, the GPR model had the highest accuracy, followed by the XGBoost model. When the input combinations of multispectral bands and VIs were used, the GOA-XGB model (having 37 input factors) had the highest accuracy, with RMSE = 0.232 kg m−2, R2 = 0.847, MAE = 0.178 kg m−2, and NRMSE = 0.127. When the XGBoost feature selection was used to reduce the input factors to 16, the model accuracy improved further to RMSE = 0.226 kg m−2, R2 = 0.855, MAE = 0.172 kg m−2, and NRMSE = 0.123. Based on the developed model, the average AGB of the plot was 1.49 ± 0.34 kg.

A Review on Evapotranspiration Estimation in Agricultural Water Management: Past, Present, and Future

Evapotranspiration (ET) is a major component of the water cycle and agricultural water balance. Estimation of water consumption over agricultural areas is important for agricultural water resources planning, management, and regulation. It leads to the establishment of a sustainable water balance, mitigates the impacts of water scarcity, as well as prevents the overusing and wasting of precious water resources. As evapotranspiration is a major consumptive use of irrigation water and rainwater on agricultural lands, improvements of water use efficiency and sustainable water management in agriculture must be based on the accurate estimation of ET. Applications of precision and digital agricultural technologies, the integration of advanced techniques including remote sensing and satellite technology, and usage of machine learning algorithms will be an advantage to enhance the accuracy of the ET estimation in agricultural water management. This paper reviews and summarizes the technical development of the available methodologies and explores the advanced techniques in the estimation of ET in agricultural water management and highlights the potential improvements to enhance the accuracy of the ET estimation to achieve precise agricultural water management.

Identifying Critical Issues in the Horticulture Industry: A Delphi Analysis during the COVID-19 Pandemic

The horticulture industry provides significant contributions to society, including healthy foods, economic development, recreation and leisure activities, among others. However, there are critical issues facing the horticulture industry which should be illuminated to ensure ongoing vitality and relevance, particularly within the COVID-19 pandemic context. The present study used the Delphi Technique to identify the most critical issues facing the horticulture industry as perceived by a panel of industry experts. Data were collected from February to October 2020 and thus preceded the initial declaration of COVID-19 as a global pandemic through the lifting of stay-at-home orders in most states. The expert panel arrived at a consensus on 34 specific issues, five of which were specifically related to the COVID-19 pandemic. Using the Constant Comparative Method, the issues were thematically analyzed and grouped into five primary categories, including (alphabetically ordered): (1) disease and pest management, (2) education, research, and recruitment, (3) environmental conditions and natural resource availability, (4) labor challenges and considerations, and (5) production challenges and innovations. The results of the study provide a framework for both academic and practitioner audiences to identify critical focus areas for the industry within a COVID-19 context.

Refining Irrigation Strategies in Horticultural Production

Water, energy and food security are crucial for a sustainable long-term economy [...]

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits

Monitoring the spatial and temporal variability of yield crop traits using remote sensing techniques is the basis for the correct adoption of precision farming. Vegetation index images are mainly associated with yield and yield-related physiological traits, although quick and sound strategies for the classification of the areas with plants with homogeneous agronomic crop traits are still to be explored. A classification technique based on remote sensing spectral information analysis was performed to discriminate between wheat cultivars. The study analyzes the ability of the cluster method applied to the data of three vegetation indices (VIs) collected by high-resolution UAV at three different crop stages (seedling, tillering, and flowering), to detect the yield and yield component dynamics of seven durum wheat cultivars. Ground truth data were grouped according to the identified clusters for VI cluster validation. The yield crop variability recorded in the field at harvest showed values ranging from 2.55 to 7.90 t. The ability of the VI clusters to identify areas with similar agronomic characteristics for the parameters collected and analyzed a posteriori revealed an already important ability to detect areas with different yield potential at seedling (5.88 t ha−1 for the first cluster, 4.22 t ha−1 for the fourth). At tillering, an enormous difficulty in differentiating the less productive areas in particular was recorded (5.66 t ha−1 for cluster 1 and 4.74, 4.31, and 4.66 t ha−1 for clusters 2, 3, and 4, respectively). An excellent ability to group areas with the same yield production at flowering was recorded for the cluster 1 (6.44 t ha−1), followed by cluster 2 (5.6 t ha−1), cluster 3 (4.31 t ha−1), and cluster 4 (3.85 t ha−1). Agronomic crop traits, cultivars, and environmental variability were analyzed. The multiple uses of VIs have improved the sensitivity of k-means clustering for a new image segmentation strategy. The cluster method can be considered an effective and simple tool for the dynamic monitoring and assessment of agronomic traits in open field wheat crops.

Implementing Sustainable Irrigation in Water-Scarce Regions under the Impact of Climate Change

The sustainability of irrigated agriculture is threatening due to adverse climate change, given future projections that every one in four people on Earth might be suffering from extreme water scarcity by the year 2025. Pressurized irrigation systems and appropriate irrigation schedules can increase water productivity (i.e., product yield per unit volume of water consumed by the crop) and reduce the evaporative or system loss of water as opposed to traditional surface irrigation methods. However, in water-scarce countries, irrigation management frequently becomes a complex task. Deficit irrigation and the use of non-conventional water resources (e.g., wastewater, brackish groundwater) has been adopted in many cases as part of a climate change mitigation measures to tackle the water poverty issue. Protected cultivation systems such as greenhouses or screenhouses equipped with artificial intelligence systems present another sustainable option for improving water productivity and may help to alleviate water scarcity in these countries. This article presents a comprehensive review of the literature, which deals with sustainable irrigation for open-field and protected cultivation systems under the impact of climatic change in vulnerable areas, including the Mediterranean region.

Accuracies of Soil Moisture Estimations Using a Semi-Empirical Model over Bare Soil Agricultural Croplands from Sentinel-1 SAR Data

This study describes a semi-empirical model developed to estimate volumetric soil moisture ( ϑ v ) in bare soils during the dry season (March–May) using C-band (5.42 GHz) synthetic aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 m spatial resolution. The semi-empirical model was developed using backscatter coefficient ( σ ° dB ) and in situ soil moisture collected from Siruguppa taluk (sub-district) in the Karnataka state of India. The backscatter coefficients σ V V 0 and σ V H 0 were extracted from SAR images at 62 geo-referenced locations where ground sampling and volumetric soil moisture were measured at a 10 cm (0–10 cm) depth using a soil core sampler and a standard gravimetric method during the dry months (March–May) of 2017 and 2018. A linear equation was proposed by combining σ V V 0 and σ V H 0 to estimate soil moisture. Both localized and generalized linear models were derived. Thirty-nine localized linear models were obtained using the 13 Sentinel-1 images used in this study, considering each polarimetric channel Co-Polarization (VV) and Cross-Polarization (VH) separately, and also their linear combination of VV + VH. Furthermore, nine generalized linear models were derived using all the Sentinel-1 images acquired in 2017 and 2018; three generalized models were derived by combining the two years (2017 and 2018) for each polarimetric channel; and three more models were derived for the linear combination of σ V V 0 and σ V H 0 . The above set of equations were validated and the Root Mean Square Error (RMSE) was 0.030 and 0.030 for 2017 and 2018, respectively, and 0.02 for the combined years of 2017 and 2018. Both localized and generalized models were compared with in situ data. Both kind of models revealed that the linear combination of σ V V 0 + σ V H 0 showed a significantly higher R2 than the individual polarimetric channels.

Agronomic Traits Analysis of Ten Winter Wheat Cultivars Clustered by UAV-Derived Vegetation Indices

Timely and accurate estimation of crop yield variability before harvest is crucial in precision farming. This study is aimed to evaluate the ability of cluster analysis based on Vegetation Indices (VIs) that were obtained from UAVs to predict the spatial variability on agronomic traits of ten winter wheat cultivars. Five VIs groups were identified and the ground truth yield-related data were analyzed for clusters validation. The yield data revealed a value of 6.91 t ha−1 for the first cluster with the highest VIs value and a decrease of −12%, −21%, and −27% for the 2nd, 3rd, and 4th clusters; respectively; the 5th cluster; with the lowest VIs value showed the lower yield values (4 t ha−1). Agronomic traits, such as dry biomass, spike numbers, and weight were grouped according to VIs clusters and analyzed and showed the same trends. The analysis of spatial distribution and agronomic data of the ten cultivars within the single clusters highlighted that the most productive varieties showing a greater value of spike weight and numbers and a greater presence of areas with high values of VIs and vice versa the less productive once, though two cultivars showed productions not linked to cluster classification and high data range variability were recorded. Cluster identified by high-resolution UAV vegetation indices can be a valid strategy although its effectiveness is closely linked to the cultivar component and, therefore, requires extensive verification.

Capability of Diffuse Reflectance Spectroscopy to Predict Soil Water Retention and Related Soil Properties in an Irrigated Lowland District of Southern Italy

In this study, we examined the potential of vis-NIR reflectance spectroscopy, coupled with partial least squares regression (PLSR) analysis, for the evaluation and prediction of soil water retention at field capacity (FC) and permanent wilting point (PWP) and related basic soil properties [organic carbon (OC), sand, silt, and clay contents] in an agricultural irrigated land of southern Italy. Soil properties were determined in the laboratory with reference to the Italian Official Methods for Soil Analysis. Vis-NIR reflectance spectra were measured in the laboratory, using a high-resolution spectroradiometer. All soil variables, with the exception of silt, evidently affected some specific spectral features. Multivariate calibrations were performed to predict the soil properties from reflectance spectra. PLSR was used to calibrate the spectral data using two-thirds of samples for calibration and one-third for validation. Spectroscopic data were pre-processed [multiplicative scatter correction (MSC), standard normal variance (SNV), wavelet detrending (WD), first and second derivative transformation, and filtering] prior to multivariate calibration. The results revealed very good models (2.0 < RPD < 2.5) for the prediction of FC, PWP and sand, and excellent (RPD > 2.5) models for the prediction of clay and OC, whereas a poor (RPD < 1.4) prediction model was obtained for silt.

Detection of Spatial and Temporal Variability of Wheat Cultivars by High-Resolution Vegetation Indices

An on-farm research study was carried out on two small-plots cultivated with two cultivars of durum wheat (Odisseo and Ariosto). The paper presents a theoretical approach for investigating frequency vegetation indices (VIs) in different areas of the experimental plot for early detection of agronomic spatial variability. Four flights were carried out with an unmanned aerial vehicle (UAV) to calculate high-resolution normalized difference vegetation index (NDVI) and optimized soil-adjusted vegetation index (OSAVI) images. Ground agronomic data (biomass, leaf area index (LAI), spikes, plant height, and yield) have been linked to the vegetation indices (VIs) at different growth stages. Regression coefficients of all samplings data were highly significant for both the cultivars and VIs at anthesis and tillering stage. At harvest, the whole plot (W) data were analyzed and compared with two sub-areas characterized by high agronomic performance (H) yield 20% higher than the whole plot, and low performances (L), about 20% lower of yield related to the whole plot). The whole plot and two sub-areas were analyzed backward in time comparing the VIs frequency curves. At anthesis, more than 75% of the surface of H sub-areas showed a VIs value higher than the L sub-plot. The differences were evident also at the tillering and seedling stages, when the 75% (third percentile) of VIs H data was over the 50% (second percentile) of the W curve and over the 25% (first percentile) of L sub-plot. The use of high-resolution images for analyzing the frequency value of VIs in different areas can be a useful approach for the detection of agronomic constraints for precision agriculture purposes.

Monitoring the Effects of Water Stress in Cotton using the Green Red Vegetation Index and Red Edge Ratio

The main objective of this work was to study the feasibility of using the green red vegetation index (GRVI) and the red edge ratio (RE/R) obtained from UAS imagery for monitoring the effects of soil water deficit and for predicting fibre quality in a surface-irrigated cotton crop. The performance of these indices to track the effects of water stress on cotton was compared to that of the normalised difference vegetation index (NDVI) and crop water stress index (CWSI). The study was conducted during two consecutive seasons on a commercial farm where three irrigation frequencies and two nitrogen rates were being tested. High-resolution multispectral images of the site were acquired on four dates in 2017 and six dates in 2018, encompassing a range of matric potential values. Leaf stomatal conductance was also measured at the image acquisition times. At harvest, lint yield and fibre quality (micronaire) were determined for each treatment. Results showed that within each year, the N rates tested (> 180 kg N ha-1) did not have a statistically significant effect on the spectral indices. Larger intervals between irrigations in the less frequently irrigated treatments led to an increase (p < 0.05) in the CWSI and a reduction (p < 0.05) in the GRVI, RE/R, and to a lesser extent in the NDVI. A statistically significant and good correlation was observed between the GRVI and RE/R with soil matric potential and stomatal conductance at specific dates. The GRVI and RE/R were in accordance with the soil and plant water status when plants experienced a mild level of water stress. In most of the cases, the GRVI and RE/R displayed long-term effects of the water stress on plants, thus hampering their use for determinations of the actual soil and plant water status. The NDVI was a better predictor of lint yield than the GRVI and RE/R. However, both GRVI and RE/R correlated well (p < 0.01) with micronaire in both years of study and were better predictors of micronaire than the NDVI. This research presents the GRVI and RE/R as good predictors of fibre quality with potential to be used from satellite platforms. This would provide cotton producers the possibility of designing specific harvesting plans in the case that large fibre quality variability was expected to avoid discount prices. Further research is needed to evaluate the capability of these indices obtained from satellite platforms and to study whether these results obtained for cotton can be extrapolated to other crops.

Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling

Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a R2 value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. The predictive irrigation scheduling system was evaluated against a rule-based system that applies irrigation based on predefined thresholds. Results indicate that the predictive system achieves a water saving ranging between 20 and 46% while realizing a yield and water use efficiency similar to that of the rule-based system.

Smart System for Bicarbonate Control in Irrigation for Hydroponic Precision Farming

Improving the sustainability in agriculture is nowadays an important challenge. The automation of irrigation processes via low-cost sensors can to spread technological advances in a sector very influenced by economical costs. This article presents an auto-calibrated pH sensor able to detect and adjust the imbalances in the pH levels of the nutrient solution used in hydroponic agriculture. The sensor is composed by a pH probe and a set of micropumps that sequentially pour the different liquid solutions to maintain the sensor calibration and the water samples from the channels that contain the nutrient solution. To implement our architecture, we use an auto-calibrated pH sensor connected to a wireless node. Several nodes compose our wireless sensor networks (WSN) to control our greenhouse. The sensors periodically measure the pH level of each hydroponic support and send the information to a data base (DB) which stores and analyzes the data to warn farmers about the measures. The data can then be accessed through a user-friendly, web-based interface that can be accessed through the Internet by using desktop or mobile devices. This paper also shows the design and test bench for both the auto-calibrated pH sensor and the wireless network to check their correct operation.