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Delineation of management zones for applying Variable Rate Irrigation (VRI) in drip irrigation presents unique challenges due to the directionality of the drip lines and the fact that the water flow rate can not be varied along a drip line. This work presents a method for optimal delineation of irrigation zones under such constraints. It is assumed that historical weather records, a soil map, a simulation model that predicts the crop development in response to weather and irrigation, as well as crop and water prices, are available. The first step consists of estimating the water productivity function of the crop for each soil type if managed optimally, using multi-objective optimization. The second step consists of estimating the water productivity function of the crop on each soil if the irrigation is managed according to another soil. Optimal delineation of the irrigation zones is then obtained by solving a non-linear optimization problem in which the decision variables are the positions of the zones boundaries. This involves an intermediate step in which the area of each soil in each irrigation zone is calculated, and the soil according to which the irrigation should be managed in each zone is determined. The procedure is illustrated on two examples involving three soils and a cotton crop in Greece. In both cases, optimal management zones delineation and management was obtained using the average weather of years 1982–1991 and performance was estimated for years 1992–2016. The results show that splitting the field into several management zones increases profit slightly via yield increase and/or water savings, depending on the relative price of water.

Apple trees tend to bear an excess of young fruits (fruitlets). Chemical thinning with an application of bioregulators early in the season is currently the most common solution to adjust fruit load. However, the results of this approach are highly inconsistent between seasons. In the present study, we identified the most informative bands in the visible-near infrared range for forecasting fruitlet destiny in response to the thinner application, which could be used to support grower decisions. “Golden Delicious” apple trees were monitored during two consecutive seasons, following the application of synthetic auxins commonly used in commercial orchards. Simple bands, band differences, and band ratios were investigated as features for forecasting fruitlet destiny from the first week of monitoring. Optimal threshold values and best wavelengths combinations were determined via the receiver operating characteristic (ROC) curve. At the early stage of fruitlet development (4–6 days after treatment, DAT), applying thresholding to the band difference R 973 − R 404 was preferable (accuracies ranging from 66% to 87%), while applying thresholding to the band ratio R 693 / R 674 was preferable at 7–12 DAT (accuracies ranging from 76% to 95%). The present results indicate that the development of a simplified portable device for forecasting apple fruitlet drop seems feasible. Complimentary measurements performed on intact vs. denuded fruitlets revealed that the spectral differences observed can be explained by substantial changes in trichomes density that occur naturally during fruitlet development and affect the apparent pigment absorption.

Site-specific agricultural management (SSM) relies on identifying within-field spatial variability and is used for variable rate input of resources. Precision agricultural management commonly attempts to integrate multiple datasets to determine management zones (MZs), homogenous units within the field, based on spatial characteristics of environmental and crop properties (i.e., terrain, soil, vegetation conditions). This study compared several multivariate spatial clustering methods to determine MZs for precision nitrogen fertilization in a citrus orchard. Six variables, namely normalized difference vegetation index, crop water stress index, digital surface model, slope, elevation and aspect, were used to characterize spatial variability within four plots. Six clustering model composites were compared, each including some or all of the following components: (1) spatial representation of the data (e.g., Getis Ord Gi*); (2) variable weights based on their relative contribution; and (3) clustering methods, including different extensions of K-means and hierarchical clustering algorithms. The fuzzy K-means algorithm applied to the weighted spatial representation was found to generate MZs with similar numbers of trees, while the K-means algorithm applied over the spatial representation generated MZs that were more continuous over space, with minimum fragmentation. Spatial variability was not constant across the orchard and among the different variables. Management of the sub-units, or plots, using spatial representation rather than the measured values, is proposed as a more suitable platform for agricultural practices. SSM is dependent upon available variable rate application technologies. Future development of fertilizer application for individual trees will require adjusting the statistical approach to support tree-specific management. The suggested model composite is flexible and may be composed of different models for delineating plot-specific MZs.

Vines for wine production are commonly cultivated under regulated deficit irrigation, i.e. the vines (Vitis vinifera) are maintained in a state of mild-sever, controlled drought stress in order to enhance the quality of the yield and the wine. The plant water status can be estimated via measurements of the Stem Water Potential (SWP) using Scholander pressure chamber. The objective of the present work was to demonstrate the application of Model Predictive Control (MPC) for managing grapevine irrigation via midday Stem Water Potential at two levels: (1) MPC was used to determine SWP reference values for the whole season, and (2) MPC was used to estimate twice a week the irrigation required to achieve the desired SWP.

Real-time in-situ measurements are increasingly used to improve the estimations of simulation models via data assimilation techniques such as particle filter. However, models that describe complex processes such as water flow contain a large number of parameters while the data available is typically very limited. In such situations, applying particle filter to a large, fixed set of parameters chosen a priori can lead to unstable behavior, i.e. inconsistent adjustment of some of the parameters that have only limited impact on the states that are being measured. To prevent this, in this study correlation-based variable selection is embedded in the particle filter, so that at each data assimilation step only a subset of the parameters is adjusted. More specifically, whenever measurements become available, the most influential (i.e., the most highly correlated) parameters are determined by correlation analysis, and only these are updated by particle filter. The proposed method was applied to a water flow model (Hydrus-1D) in which states (soil water contents) and parameters (soil hydraulic parameters) were updated via data assimilation. Two simulation case studies were conducted in order to demonstrate the performance of the proposed method. Overall, the proposed method yielded parameters and states estimates that were more accurate and more consistent than those obtained when adjusting all the parameters.

A challenge in modern farming is to find a sustainable way of achieving sufficient production. Precision in dosage, timing and allocation of water, biocides, fertilizer and other inputs is essential, as are such management actions as harvesting, pruning and weeding. Despite the increasing availability of sensor and actuator technologies, decision-making is still largely left to the farmer. This is creating a strong demand for support in operational management. This paper presents an overview of methods involving the use of technology and data to develop model-based management support and automation for productive and input-efficient farming. For each method, the main advantages and drawbacks relating to typical farm characteristics are discussed and summarized. Three case studies are presented, to illustrate the design steps involved in developing a model, observer and controller. The overall design procedure is summarized in a flowchart, and serves as a basic guide for method selection and model development.

Site-specific agricultural management relies on identifying within-field spatial variability and is being used for variable rate input of resources. Precision agricultural management commonly attempts to integrate multiple datasets to determine management zones (MZs), homogenous units within the field, based on spatial characteristics of environmental and crop properties (i.e., terrain, soil, vegetation conditions). This study aims to develop a novel statistical multivariate spatial clustering approach to determine MZs for precision nitrogen fertilization in a citrus orchard along the growing season. Five variables were used to characterize spatial variability (i.e., N spectral index, crop water stress index (CWSI), tree height, elevation, and slope) within four plots based on a monthly thermal and multispectral high-resolution imagery acquired from an unmanned aerial vehicle (UAV). The UAV data was tested against leaf N samplings based on samples taken from 48 trees within the four craters plots, which were selected based on a stratified random design (SRD) model. A Support Vector Machines-Regression (SVM-R) model was applied to develop a prediction N spectral index for canopy N levels. The clustering model included the following components — spatial representation of the data based on Getis Ord Gi*. Then variable weights were assigned based on their relative contribution to principal component analysis. Fuzzy C-means algorithm was applied to the weighted spatial representation and was found to generate spatially continuous and homogeneous MZs with similar numbers of trees. In addition, we analyzed the temporal dynamics in the MZs and clustering patterns throughout the year, using information based on the monthly UAV imagery. Management of the sub-units, or plots, using spatial representation rather than the measured values, is suggested as a more suitable platform for agricultural practices. Future development of fertilization applications for individual trees will require adjusting the statistical approach to support tree-specific management. The proposed model composite is flexible and may be composed of different models and/or variables for developing optimal MZ delineation for specific plots.

This paper analyses the performance of sub-optimal irrigation schedules obtained daily by solving a multi-objective optimization problem with updated weather measurements and forecasts. The approach was tested using five crops at four European locations with contrasting weather conditions. Four- and 6-day Global Forecast System (GFS) forecasts were used at all locations, and comparison with a down-scaled locally tuned model was conducted at one location. Accurate GFS temperature forecasts were observed at all four locations, but the accuracy of the potential evapotranspiration calculated from the GFS forecasts was not as consistent. Precipitations forecasts were very poor at all locations. In Greece, the down-scaled locally tuned forecasts were only marginally better than the GFS ones. In most cases, recomputing the sub-optimal irrigation schedule daily greatly reduced the impact of the imperfect weather forecasts on the final results. Using 4- or 6-day actual forecasts did not yield results appreciably better than those obtained using only historical averages as surrogate forecasts. The main consequence of the imperfect forecasts was that the final yield differed from the target one, but the (yield, irrigation) combination remained close to optimal, unless the target yield was set too high and water availability was not the main factor limiting crop development.

Estimating pesticide spray drift, which is a part of total drift loss, is complex as airborne pesticide concentrations are low and depend on multiple factors. The aim was to measure and compare vertical profiles of spray drift generated by different sprayers using Open Path Fourier-Transform-Infra-Red (OP-FTIR) spectrometer. Field tests included three types of commercial agricultural sprayers. The OP-FTIR was placed at the edge of an apple orchard with the line of sight parallel to tree rows. The OP-FTIR and its reflector were mounted on platform lifts to allow measurements at 4 heights: 3 (canopy height), 4, 5, and 6 m above ground. The sprayers sprayed water within the three tree rows closest to the OP-FTIR as well as outside each tree row in order to estimate the spray drift as function of distance with and without tree interference. The results of the experiments showed that, under the meteorological conditions prevailing, there were substantial differences between the sprayers in terms of spray drift of droplets with diameter > 5 μm. Additionally, the results showed that spray drift can be reduced substantially (by up to 50%) by using a tree-line barrier or a buffer zone.

A procedure for identifying apples in night-time orchard images was developed and tested on two datasets totalling over 550 images of Golden Delicious trees captured on two years with different cameras and lighting systems. The analysis started by detecting specular reflection highlights and extracting sub-images (101 by 101 pixels) centred at those local maxima. Each sub-image was reduced to a 676 Upright Speeded Up Robust Features (U-SURF) vector. Close to 20,000 sub-images from one dataset were manually labelled as “apple” or “not apple”. The latter group included parts of leaves, branches and other objects which exhibited strong specular reflection. Seventy-two classifiers were trained with the number of “apple” and “not apple” training samples ranging from 500 to 2000 and from 5000 to 10,000, respectively, and with vocabulary size ranging from 500 to 10,000. Misclassifications occurred mostly in dark and low contrast regions, which led to developing alternate models based on the posterior probability that the classification result was correct taking into account the sub-image entropy or intensity. Yield models were calibrated for each dataset, using 20 random trees. For both datasets the overall yield estimate was within 10% of the actual yield, and the standard deviation was around 30% of the average tree yield. These results are similar to those reported in previous studies, but while these previous studies used procedures calibrated and tested with images from the same dataset, in the present study the classifier trained with images from one dataset was successfully applied to the second dataset.

Measurements indicative of crop development, such as leaf area index, canopy cover or biomass are typically performed only a few times throughout the season at irregular time intervals. Furthermore, due to the inherent spatial variability that exists in the field, combining measurements taken at different locations in the field usually leads to large uncertainty around the mean value. These factors, together with the fact that crop-soil models are strongly non-linear, render assimilation of measurements in crop-soil models non-trivial. This work presents procedures for performing such data assimilation, using the crop model AquaCrop as specific example. The procedures are based on Extended Kalman Filter, with some heuristic adjustments, and enable re-initialisation of state variables and/or adjustments of selected parameters of the model. The uncertainties of the measurements are taken into account explicitly in the proposed assimilation scheme. The procedures were tested with data obtained from experiments conducted with potato in Denmark and cotton in Greece. In both cases the data available consisted of canopy cover and biomass (average and standard deviation on 5–10 days), and a locally-calibrated AquaCrop model was used as starting point for the assimilation process. The results demonstrate the soundness of the approach but also emphasise the inherent limitations associated with data assimilation. In particular, assimilation of easy-to-obtain canopy cover measurements did not always improve the predictions of biomass.

Gross N mineralization is a fundamental soil process that plays an important role in determining the supply of soil inorganic N, highlighted by recent research demonstrating that plants can effectively compete with microbes for inorganic N. However, predictions of the supply of plant available N from soil have largely neglected gross N mineralization. As soil organic matter (SOM) is the substrate that microbes use in the process of N mineralization, characteristics of SOM fractions that are relatively easy to measure may hold value as predictors of gross N mineralization. To improve understanding of predictive relationships between SOM fraction properties and gross N mineralization, we assessed 32 measures of SOM quality and quantity, including physically, chemically, and biologically defined SOM fractions, for their ability to predict gross N mineralization across a wide range of soil types (Aridisols to Mollisols) and crop management systems (organic vs. inorganic based fertility) in Israel and the United States. We also assessed predictions of a commonly employed indicator of soil N availability, potentially mineralizable N (PMN, determined by 7-d anaerobic incubation). Organic fertility management systems consistently enhanced gross N mineralization and PMN compared with inorganic fertility management systems. While several SOM characteristics were significantly correlated with both gross N mineralization and PMN, other characteristics differed in their relationships with gross N mineralization and PMN, highlighting that these assays are controlled by different factors. Multiple linear regressions (MLR) were utilized to generate N mineralization predictions: five (gross N mineralization) or six (PMN) predictor models explained >80% of the variation in both gross N mineralization and PMN (R² > 0.8). The MLR models successfully predicted gross N mineralization and PMN across diverse soil types and management systems, indicating that the relationships were valid across a wide range of diverse agroecosystems. The ability to develop predictive models that apply across diverse soil types can aid soil health assessment and management efforts.

Precise movement of a rigid body with jerk as the control signal, and with friction compensation is considered. The optimal solution for the minimization of the electrical energy consumption is obtained. The optimal solution was tested numerically.

The ensemble Kalman filter (EnKF) is a widely used data assimilation method in soil hydrology. However, underestimation of the modeling errors and of the sampling errors may cause systematic reduction of state variances and rejection of the observations. Inflation methods are used to alleviate this phenomenon. Here, we suggest a novel inflation method based on confidence intervals constructed using the collected ensemble of the measurements. The proposed method is illustrated via two synthetic examples of a three‐layer soil with (i) precipitation and evaporation boundary condition and (ii) irrigation boundary condition. We present a comparison of two existing inflation methods and discuss the advantages and limitations of the proposed method. Basically, the suggested method behavior is superior to the behavior of the existing methods.

This study examines the impact of weekly and seasonal weather forecasts on the optimal irrigation scheduling problem. We compare stochastic and deterministic simulation-optimization framewo...

Maize is the dominant irrigated crop in Kansas. In recent years, as a result of declining groundwater levels in the Ogallala aquifer and diminished well capacities, farmers are turning to deficit irrigation strategies. This study demonstrates the potential of model-based optimization for determining adequate soil water depletion levels. CERES-Maize was used as surrogate crop, while the AquaCrop model was used in an optimization procedure that determined the optimal water depletion levels. A multi-objective optimization framework was used to determine several combinations of optimal water depletion levels based on ten years of historical weather, and these combinations were tested using an additional 50 years of historical weather. The results show that, although imperfect modeling and weather fluctuations caused the actual yield to be different from the target yield, the fluctuations around the multi-year averages were not significantly larger when testing the irrigation schedule with the CERES-Maize model than when testing it with the AquaCrop model that had been used to develop the irrigation schedule. Keywords: AquaCrop, Center-pivot irrigation, CERES-Maize, Multi-objective optimization.

Fungi can modify the pH in or around the infected site via alkalization or acidification, and pH monitoring may provide valuable information on host–fungus interactions. The objective of the present study was to examine the ability of two fungi, Colletotrichum coccodes and Helminthosporium solani, to modify the pH of potato tubers during artificial inoculation in situ. Both fungi cause blemishes on potato tubers, which downgrades tuber quality and yield. Direct visualization and estimation of pH changes near the inoculation area were achieved using pH indicators and image analysis. The results showed that the pH of the area infected by either fungus increased from potato native pH of approximately 6.0 to 7.4 to 8.0. By performing simple analysis of the images, it was also possible to derive the growth curve of each fungus and estimate the lag phase of the radial growth: 10 days for C. coccodes and 17 days H. solani. In addition, a distinctive halo (an edge area with increased pH) was observed only during the lag phase of H. solani infection. pH modulation is a major factor in pathogen–host interaction and the proposed method offers a simple and rapid way to monitor these changes.

In this paper, we tested the operational capacity of an interoperable model coupling system for the irrigation scheduling (IMCIS) at an experimental cotton (Gossypium hirsutum L.) field in Northern Greece. IMCIS comprises a meteorological model (TAPM), downscaled at field level, and a water-driven cultivation tool (AquaCrop), to optimize irrigation and enhance crop growth and yield. Both models were evaluated through on-site observations of meteorological variables, soil moisture levels and canopy cover progress. Based on irrigation management (deficit, precise and farmer’s practice) and method (drip and sprinkler), the field was divided into six sub-plots. Prognostic meteorological model results exhibited satisfactory agreement in most parameters affecting ETo, simulating adequately the soil water balance. Precipitation events were fairly predicted, although rainfall depths needed further adjustment. Soil water content levels computed by the crop growth model followed the trend of soil humidity measurements, while the canopy cover patterns and the seed cotton yield were well predicted, especially at the drip irrigated plots. Overall, the system exhibited robustness and good predicting ability for crop water needs, based on local evapotranspiration forecasts and crop phenological stages. The comparison of yield and irrigation levels at all sub-plots revealed that drip irrigation under IMCIS guidance could achieve the same yield levels as traditional farmer’s practice, utilizing approximately 32% less water, thus raising water productivity up to 0.96 kg/m3.

Archaeological waterlogged wood objects exposed on the Dead Sea shore exhibit little visual evidence of degradation when first exposed, and after prolonged exposure and dehydration. An investigation on the state of preservation of this material was recognised as a necessary step towards its long-term conservation. Micromorphological observations, ATR FTIR, ash content, and physical tests showed that deterioration is limited and is mostly non-biological in nature. Natural bulking and impregnation with lake minerals and salts appear to play a significant role in the physical stability of these woods when dried, and apparently inhibit microbial colonization and subsequent degradation. In contrast, archaeological wood examined from a typical Mediterranean marine environment showed advanced stages of degradation by bacteria, with the wood structure extensively compromised.

The use of pesticides is important to ensure food security around the world. Unfortunately, exposure to pesticides is harmful to human health and the environment. This study suggests using active Open Path Fourier Transform Infra-Red (OP-FTIR) spectroscopy for monitoring and characterizing pesticide spray drift, which is one of the transfer mechanisms that lead to inhalation exposure to pesticides. Experiments were conducted in a research farm with two fungicides (Impulse and Bogiron), which were sprayed in the recommended concentration of ∼0.1%w in water, using a tractor-mounted air-assisted sprayer. The ability to detect and characterize the pesticide spray drift was tested in three types of environments: fallow field, young orchard, and mature orchard. During all spraying experiments the spectral signature of the organic phase of the pesticide solution was identified. Additionally, after estimating the droplets' size distribution using water sensitive papers, the OP-FTIR measurements enabled the estimation of the droplets load in the line of sight.

A procedure for estimating the number of mature apples in orchard images captured at night-time with artificial illumination was developed and its potential for estimating yield was investigated. The procedure was tested using four datasets totaling more than 800 images taken with cameras positioned at three heights. The procedure for detecting apples was based on the observation that the light distribution on apples follows a simple pattern in which the perceived light intensity decreases with the distance from a local maximum due to specular reflection. Accordingly, apple detection was achieved by detecting concentric circles (or parts of circles) in binary images obtained via threshold operations. For each dataset, after calibration of the procedure using 12 images, the estimates of the number of apples were within a few percent of the number of apples counted by visual inspection. Yield estimations were obtained via multi-linear models that used between two and six images per tree. The results obtained using all three cameras were only slightly better than those obtained using only two cameras. Using images from only one side of the tree did not worsen the results significantly. Overall, the yield estimated by the best models was within $$\pm$$ 10 % of the actual yield. However, the standard deviation of the yield estimation errors corresponded to ~26–37 % of the average tree yield, indicating that improvements are still needed in order to achieve accurate yield estimation at the single-tree level.

Machine vision technologies hold the promise of enabling rapid and accurate fruit crop yield predictions in the field. The key to fulfilling this promise is accurate segmentation and detection of fruit in images of tree canopies. This paper proposes two new methods for automated counting of fruit in images of mango tree canopies, one using texture-based dense segmentation and one using shape-based fruit detection, and compares the use of these methods relative to existing techniques:—(i) a method based on K-nearest neighbour pixel classification and contour segmentation, and (ii) a method based on super-pixel over-segmentation and classification using support vector machines. The robustness of each algorithm was tested on multiple sets of images of mango trees acquired over a period of 3 years. These image sets were acquired under varying conditions (light and exposure), distance to the tree, average number of fruit on the tree, orchard and season. For images collected under the same conditions as the calibration images, estimated fruit numbers were within 16 % of actual fruit numbers, and the F1 measure of detection performance was above 0.68 for these methods. Results were poorer when models were used for estimating fruit numbers in trees of different canopy shape and when different imaging conditions were used. For fruit-background segmentation, K-nearest neighbour pixel classification based on colour and smoothness or pixel classification based on super-pixel over-segmentation, clustering of dense scale invariant feature transform features into visual words and bag-of-visual-word super-pixel classification using support vector machines was more effective than simple contrast and colour based segmentation. Pixel classification was best followed by fruit detection using an elliptical shape model or blob detection using colour filtering and morphological image processing techniques. Method results were also compared using precision–recall plots. Imaging at night under artificial illumination with careful attention to maintaining constant illumination conditions is highly recommended.

Green leaf area index (LAI) provides insight into the productivity, physiological and phenological status of vegetation. Measurement of spectral reflectance offers a fast and nondestructive estimation of green LAI. A number of methods have been used for the estimation of green LAI; however, the specific spectral bands employed varied widely among the methods and data used. Our objectives were (i) to find informative spectral bands retained in three types of methods, neural network (NN), partial least squares (PLS) regression and vegetation indices (VI), for estimating green LAI in maize (a C4 species) and soybean (a C3 species); (ii) to assess the accuracy of the algorithms estimating green LAI using a minimal number of bands for each crop and generic algorithms for the two crops combined. Hyperspectral reflectance and green LAI of irrigated and rainfed maize and soybean were taken during eight years of observations (altogether 24 field-years) in very different weather conditions. The bands retained in the best NN, PLS and VI methods were in close agreement. The validity of these bands was further confirmed via the uninformative variable elimination PLS technique. The red edge and the NIR bands were selected in all models and were found the most informative. Identifying informative spectral bands across all four techniques provided insight into spectral features of reflectance specific for each species as well as those that are common to species with different leaf structures, canopy architectures and photosynthetic pathways. The analyses allowed development of algorithms for estimating green LAI in soybean and maize with no re-parameterization. These findings lay a strong foundation for the development of generic algorithms which is crucial for remote sensing of vegetation biophysical parameters.