Detection of Halyomorpha Halys Using Neural Networks

The paper's goal was to create some neural networks-based models for the detection and classification of insects such as Halyomorpha Halys in ecological orchards, from acquired images in the trees. The detecting operations were performed using models from two of the most efficient deep learning families in this area: R-CNN and YOLO. Using the proposed models, (Faster R-CNN, YOLOv5-s, YOLOv5-m, and YOLOv5-1) to early detection of harmful insects, a real contribution to anticipating damage in orchards is possible. The dataset is composed of images taken from the Maryland Biodiversity dataset. All training and testing operations were performed with the help of GPU processors provided by Google, the resulting models being saved on Google Drive Cloud. The images were evaluated from the detection and the classification perspective based on specific metrics such as precision, recall, and mAP. The best results were obtained for YOLOv5-m.