Agrochemical application is an important tool in the agricultural industry for the protection of crops. Agrochemical application with conventional sprayers results in the waste of applied agrochemicals, which not only increases financial losses but also contaminates the environment. Targeted agrochemical sprayers using smart control systems can substantially decrease the chemical input, weed control cost, and destructive environmental contamination. A variable rate spraying system was developed using deep learning methods for the development of new models to classify weeds and to accurately spray on desired weeds target. Laboratory and field experiments were conducted to assess the sprayer performance for weed classification and precise spraying of the target weeds using three classification CNNs (Convolutional Neural Networks) models. The DCNNs models (AlexNet, VGG-16, and GoogleNet) were trained using a dataset containing a total of 12,443 images captured from the strawberry field (4200 images with spotted spurge, 4265 images with Shepherd’s purse, and 4178 strawberry plants). The VGG-16 model attained higher values of precision, recall and F1-score as compared to AlexNet and GoogleNet. Additionally VGG-16 model recorded higher percentage of completely sprayed weeds target (CS = 93%) values. Overall in all experiments, VGG-16 performed better than AlexNet and GoogleNet for real-time weeds target classification and precision spraying. The experiments results revealed that the Sprayer performance decreased with the increase of sprayer traveling speed above 3 km/h. Experimental results recommended that the sprayer with the VGG-16 model can achieve high performance that makes it more ideal for a real-time spraying application. It is concluded that the advanced variable rate spraying system has the potential for spot application of agrochemicals to control weeds in a strawberry field. It can reduce the crop input costs and the environmental pollution risks.

Berry dropping or shattering is an important factor during the harvest and post-harvest handling of fresh eating grapes until they reach the supermarkets. There are a lot of methods to measure post-harvest placing damage and the detachment force for single fruits. However, until now, there has been no objective method to observe and analyze the berry dropping mechanism of cluster fruits during robotic post-harvest handling. Therefore, in this paper, the effect of a cluster’s vibration on berry drop during vertical transportation and the impact of different packaging materials on fresh grape clusters during robotic placing were analyzed. For this purpose, a lead screw lathe, along with an attached actuator, three grape cluster samples (0.48, 0.50, 0.53 kg), three packaging materials (rigid plastic box, corrugated fiberboard box, expandable polystyrene box), four transportation speeds (0.4, 0.6, 0.8, 1.0 m/s), and four acceleration excitations (6, 8, 10, 12 m/s²) that were given in a mechanical system (actuator) were studied. In order to analyze the berry drop mechanism of grape clusters before and after the impact with packaging material, a force sensor and high-speed video camera were used. It was concluded from the vertical transportation test that with the increase in speed and acceleration excitations, the change in hanging force increased positively (R² = 0.92). Additionally, the force after the striking of the grape cluster with packaging materials decreased negatively (R² = 0.97), and the corresponding index of berry deflection increased. It was also observed from the high-speed camera images that rigid plastic boxes caused the maximum deflection of the grape berries, with the highest change in force of 8.6 N after the impact. Experimental results showed a negative correlation between the hanging force signals and the force after impact of the cluster, with a goodness of fit of R² = 0.95 at different speeds. Overall, the proposed findings can be used as a reference study for improving robotic post-harvest handling, providing a useful visual and technical understanding of the berry fall susceptibility of cluster fruits, and can be used to develop a post-harvest robotic placing tool for avoiding berry drop damage on both industrial and farm levels.