Apple Foliar Disease and detection using Machine learning

Apple Foliar Disease and detection using Machine learning

Babli Pal

*¹(Department of CSE, Global Nature Care Sangathan's Group of Institutions, Jabalpur)

Abstract

Apple (Malus pumila) is commercially the most important temperate fruit and is fourth among the most widely produced fruits in the world after banana, orange and grape. China is the largest apple producing country in the world. Himachal Pradesh covering the districts of Shimla, Siramour, Kullu, Mandi, Chamba and Kinnaur considering the vast potential for increasing exports. The diseases like apple scab, apple rust, and brown rot affect apple trees. Diseases significantly affect the apple, 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. In this work, the model DF-Tiny-YOLO 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. DF-Tiny-YOLO 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. The results of the experiments in the validation dataset show that the proposed DF-Tiny-YOLO model achieves the highest values compared to other deep learning models in the original and extended datasets with 98.7% accuracy for Plant Pathology 2020 and 92.6% for Plant Pathology 2021.

Keywords: Computer vision, Deep learning, Kaggle, Foliar disease, YOLO.