Disease detection and physical disorders classification for citrus fruit images using convolutional neural network.

Citrus is an agricultural product with significant added value in the world. Depending on market demands and causing possible economic losses, disease detection in citrus fruits at an early stage is important as well as for all agricultural products. Therefore, it is critical to detect both early-stage citrus diseases and physically damaged citrus fruits with technological solutions. In this study, a new convolutional neural network (CNN)-based model, CitrusNet, was proposed for the classification of defective and deformed citrus fruits. In this study, 5149 images of citrus fruits were collected from citrus orchards in Antalya, Turkey. In the experiments conducted with four different CNN models, the CitrusNet and ResNet50 models obtained the best classification results. In the other phase of the study, experiments were carried out to detect Alternaria alternata and Thrips diseases, which are common in Turkey, using five different CNN models. In the first phase of the study, a dataset of 3582 images of citrus diseases was obtained. The experimental results show that the YOLOv5 and Mask R-CNN models adequately detected the citrus diseases compared to the other models. These models achieved the best performance with an average precision (AP) of 0.99. [ABSTRACT FROM AUTHOR]