AFD-Net: Apple Foliar Disease multi classification using deep learning on plant pathology dataset

Abstract

Background Plant diseases significantly affect the crop, so their identification is very important. Correct identification of these diseases is crucial for establishing a good disease control strategy to avoid time and financial losses. In general, machines can greatly reduce the possibility of human error. In particular, computer vision techniques developed through deep learning have paved a way to detect and diagnose these plant diseases on the leaf.

Methods In this work, the model AFD-Net was developed to detect and identify various leaf diseases in apple trees. The dataset is from Kaggle 2020 and 2021 and was financially supported by the Cornell Initiative for Digital Agriculture. An AFD-Net was proposed for leaf disease classification in apple trees and the results of the efficiency of the model are compared with other state-of-the-art deep learning approaches.

Results The results of the experiments in the validation dataset show that the proposed AFD-Net model achieves the highest values of 98.7% accuracy for Plant Pathology 2020 and 92.6% for Plant Pathology 2021 compared to other deep learning models in the original and extended datasets.

Discussion The results also indicate the efficiency of the proposed model in identifying leaf diseases on apple trees for major and minor classes, i.e., for multiple classification.