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Irrigation forecasting is essential for improving water management in agriculture. This study proposed a novel and practical framework for irrigation forecasting for paddy rice. Public weather forecasts in China were selected to forecast ETo and quantitative rainfall. For the latter, a simple deterministic model and a probabilistic model based on a Pearson-III representation of the data were considered. A modified Python version of the AquaCrop model (ACOP-Rice model) was adopted to generate the probability distribution of forecasted irrigation events and rule-based irrigation recommendations. The performance of weekly irrigation forecasting was then evaluated. The analysis of the weather forecasts revealed that the accuracy of the temperature and ETo predictions was acceptable, whereas the quality of the rainfall forecasts was poor. The performance of the scenarios that using probabilistic rainfall forecasts outperformed scenarios that relied on deterministic rainfall forecasts, despite the lower quality of the probabilistic rainfall forecasts compared to the deterministic forecasts. The irrigation recommendations exhibited a systematic bias (i.e., the inclination toward overirrigation or underirrigation) when using weather forecasts, and this bias was lower when using probabilistic forecasts. Additionally, compared to ETo forecasts, the inaccuracy of rainfall forecasts had a much greater impact on irrigation forecasts. [ABSTRACT FROM AUTHOR]

Fire blight disease causes significant losses in pear orchards. Fire blight infection is accompanied by visual symptoms that can easily be recognized by a grower or adviser, but such visual inspection is time consuming. The present work focused on the use of convolutional neural networks (CNNs) to identify one type of visual symptom (cankers on the main trunk of dormant trees) as well as autumn blooming, which in Israel plays an important role in the epidemiology of Erwinia amylovora—the bacterium responsible for fire blight. Images of dormant trees were acquired with a tripod-mounted DSLR camera while, for autumn blooming detection, the images were acquired using a small unmanned aerial vehicle flying a few meters above the trees. In both cases, several Faster R-CNNs were trained and tested with several datasets acquired at various locations and over several years. Overall, the CNNs for canker detection achieved precision and recall rates that exceeded 90% while, for autumn blooming detection, the precision and recall rates exceeded 80% in all but one case. These trained CNNs were used to analyze automatically geo-references images, hence generating infection/blooming maps. Such maps could be one of the information layers used by growers for managing the orchard, for instance to determine whether winter sanitation is needed and/or if it was carried out properly, or to decide when and where costly manual removal of autumn flowers or copper application is required. [ABSTRACT FROM AUTHOR]

Sustainability in our food and fiber agriculture systems is inherently knowledge intensive. It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience. Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies between the domains of natural systems that are key to simultaneously achieve sustainability and food security. In the quest for agricultural sustainability, some high-payoff research areas are suggested to resolve critical legal and technical barriers as well as economic and social constraints. These include: the development of holistic decision-making systems, automated animal intake measurement, low-cost environmental sensors, robot obstacle avoidance, integrating remote sensing with crop and pasture models, extension methods for data-driven agriculture, methods for exploiting naturally occurring Genotype x Environment x Management experiments, innovation in business models for data sharing and data regulation reinforcing trust. Public funding for research is needed in several critical areas identified in this paper to enable sustainable agriculture and innovation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A mobile melon robotic harvester consisting of multiple Cartesian manipulators, each with three degrees of freedom, is being developed. In order to design an optimal robot in terms of number of arms, manipulator capabilities, and robot speed, a method of allocating the fruits to be picked by each manipulator in a way that yields the maximum harvest has been developed. Such a method has already been devised for a multi-arm robot with 2DOF each. The maximum robotic harvesting problem was shown there to be an example of the maximum k-colorable subgraph problem (MKCSP) on an interval graph. However, for manipulators with 3DOF, the additional longitudinal motion results in variable intervals. To overcome this issue, we devise a new model based on the color-dependent interval graph (CDIG). This enables the harvest by multiple robotic arms to be modeled as a modified version of the MKCSP. Based on previous research, we develop a greedy algorithm that solves the problem in polynomial time, and prove its optimality using induction. As with the multi-arm 2DOF robot, when simulated numerous times on a field of randomly distributed fruits, the algorithm yields a nearly identical percentage of fruit harvested for given robot parameters. The results of the probabilistic analysis developed for the 2DOF robot was modified to yield a formula for the expected harvest ratio of the 3DOF robot. The significance of this method is that it enables selecting the most efficient actuators, number of manipulators, and robot forward velocity for maximal robotic fruit harvest. [ABSTRACT FROM AUTHOR]

Idowu et al., in this edition of Plant and Soil describe a welcome new tool that allows farmers to optimize the management of their fields. It is based on a holistic approach that integrates assays of soil physical, chemical and biological parameters into an easily understandable, low-cost and quantitative estimate of soil quality or “health”. It can facilitate tracking and understanding of the short and long term effects of different management approaches on soil (and crop) health. Most of the assays used are based on traditional, labour-intensive techniques. The tool could be improved through employment of assay technologies that are under development. For example, Idowu et al., in this edition investigated the use of near infra-red spectroscopy as a replacement for some of the traditional soil assays. Similarly, future developments in remote spectroscopic assays, using satellite, airborne or land-based platforms may facilitate further improvement. [ABSTRACT FROM AUTHOR]