Small size CNN (CAS-CNN), and modified MobileNetV2 (CAS-MODMOBNET) to identify cashew nut and fruit diseases

Farming supplies nourishment for all humans, especially in times of rapid population growth. The cashew (Anacardium occidentale L.) tree provides nutrition, income, and health benefits through its agricultural products. However, diseases often affect cashew nuts and fruits, resulting in significant production losses. As a result, it is crucial to predict cashew diseases to ensure food supply for the entire population. This study created a database of healthy and diseased cashew nuts and fruits and divided it into two subsets: Training and Testing (Test). Data augmentation was applied to the Training dataset to enhance the images. The CAS-CNN model was developed, and the MobileNetV2 was modified. The performance of the developed CAS-CNN and CAS-MODMOBNET models was evaluated and compared with existing TL models. The CAS-MODMOBNET reached an average accuracy of 99.8% and an Area under the ROC Curve (AUC) of 1.0 on the Test subset. Furthermore, the presented system was analysed with the size, number of layers, and parameters of existing TL models.