Identification of citrus pests using Unmanned Aerial Vehicles and artificial intelligence methods

Today, the implementation of precision agriculture to manage and control citrus pests can effectively optimize pesticide use, reducing adverse environmental effects and ensuring human health. But managing individual trees on a large scale is a big challenge. Therefore, using a machine vision system seems necessary to monitor and identify pests in different partsof the trees at different times. In this study, an Unmanned Aerial Vehicle (UAV) equipped with a camera was used to identify pests in other parts of the citrus orchard. For the optimal selection of the UAV linear speed, three speeds in the range of 10, 20, and 30 cm/s were considered. After framing and formatting, the recorded videos were trained in three pretrained models: AlexNet, VGG-16, and GoogleNet. Three optimization algorithms were used in the network training process: SGDm, RMSProp, and Adam. The evaluation results showed that the AlexNet model, with the help of SGDm algorithm, had the best performance in terms of detection accuracy. The highest pest detection accuracy was 96.43% at a velocity of 10 cm/s, so increasing the linear velocity to 30 cm/s reduced the detection accuracy by 13%. The results of this study show that using a combination of UAV technology and artificial intelligence methods can help professionals and farmers manage and control citrus orchard pests.