Agry: a comprehensive framework for plant diseases classification via pretrained EfficientNet and convolutional neural networks for precision agriculture

Diagnosing plant diseases is a vital issue in maintaining and developing of agricultural products. These diseases occur with changes in the tissue of different parts of plants. While the previous researches have only been conducted on certain species of plants and specific parts, we propose a comprehensive approach that has reached a high accuracy in diagnosing and classifying the disease of offending plant species by examining their different parts, including leaves, fruits, tree trunks, and seeds. We extract features from different layers of pre-trained AlexNet, ResNet50, VGG16, EfficientNetB0, EfficientNetB3 and EfficientNetB7 deep models with a spatial attention module to classify samples with an SVM classifier with RBF kernel. In order to automate the detection of plant diseases by manned or unmanned agricultural machines, our proposed Agry requires only 0.04089 seconds for image processing and decision making in real time. Also, due to the use of transfer learning, the cost of building the proposed model, including time and resources, is minimized. The results of the tests show a significant improvement compared to the previous works, and in most cases the classification is done without errors.