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During pesticide applications to citrus trees using air-assisted (airblast) sprayers, only a proportion of the volume emitted reaches the vegetation and the rest is lost through drift, evaporation, etc. These losses can be hazardous for the environment. Knowing the characteristics of droplets within the turbulent currents around the canopy could improve the application efficiency. In a previous study, a 2D computational fluid dynamics (CFD) model to simulate the effect of a citrus canopy on the airflow from an air-assisted sprayer was developed and validated. It considered the first element of the tree canopy as a solid body instead of a porous one. The aim of the present study was to analyse the behaviour of the droplets for pesticide applications on citrus by means of an Eulerian–Lagrangian CFD model. It simulated both the air current from the sprayer fan and the wind and the behaviour of the droplets sprayed. Distance, height, velocity, Reynolds number, temperature, geometric and volumetric diameters at different times were obtained. With these parameters, new variables related to the kinetics and evaporation of droplets were calculated. Simulation results estimated that 44% of the total sprayed volume reached the target tree, 28% reached adjacent trees, 20% was deposited on the ground and 8% was lost as atmospheric drift. The results largely matched an experimental mass balance carried out under similar conditions. The proposed model appears to be an appropriate tool for simulating treatments with air-assisted sprayers operating in citrus orchards.

The computer vision systems currently used for the automatic inspection of citrus fruits are normally based on supervised methods that are capable of detecting defects on the surface of the fruit but are unable to discriminate between different types of defects. identifying the type of the defect affecting each fruit is very important in order to optimise the marketing profit and to be able to take measures to prevent such defects from occurring in the future. In this paper, we present a computer vision system that was developed for the recognition and classification of the most common external defects in citrus. in order to discriminate between 11 types of defects, images of the defects were acquired in five spectral areas, including the study of near infrared reflectance and ultraviolet induced fluorescence. The system combines spectral information about the defects with morphological estimations of them in order to classify the fruits in categories. The fruit-sorting algorithm proposed here was tested by using it to identify the defects in more than 2000 citrus fruits, including mandarins and oranges. The overall success rate reached 86%.

The purpose of this work is to present a non-chemical weed controller for vegetable crops derived from a cooperative French–Spanish project. One of the major efforts of this project is related with working in a natural, complex environment and the similarity between the weeds and the vegetable crop, which makes locating the weeds difficult. The robotic arm presents a parallel structure with six degrees of freedom, and due to its light structure, it is capable of reaching high accelerations. The end-effector has an electrode powered by a set of batteries and kills the weeds by producing an electrical discharge of 15 000 V. All the subsystems of the machine communicate through a controller area network (CAN) bus. There are two vision systems mounted on the machine. The first one is placed at the front of the robot to acquire and analyse field images, in order to detect weeds and send their coordinates to the robotic arm control. The second camera is placed close to the electrode, and its mission is to correct inertial perturbations by relocating every individual weed detected by the first vision system The successful performance of this new concept of precise non-chemical weeding has been demonstrated in a lettuce crop in Valencia, Spain.