Using Drones and Deep Neural Networks to Detect Halyomorpha Halys in Ecological Orchards

To reduce the damage caused by Halyomorpha Halys (HH) in ecological orchards, the first step is to detect and identify this insect species. The paper's goal was to evaluate the effectiveness of convolutional neural networks (CNNs) in detecting harmful insects such as HH in orchards. The images were acquired by a Mavic 2 Pro DJI drone. To detect HH from images, four CNNs were tested and compared: GoogLeNet, ResNet101, DenseNet201, and VGG19. Transfer learning and data augmentation were used to minimize computational effort in the learning phase. For each proposed CNN, the detection and classification performances are evaluated in the testing phase based on statistical indicators derived from confusion matrices. Also, the learning time was considered.