Bangladeshi jujube leaf disease classification using deep learning techniques

Abstract

This paper explores the use of deep learning algorithms in identifying diseases affecting Bangladesh jujube leaves, with the goal of improving crop yields and yields’ management. A dataset comprising 2400 images was curated, encompassing four target attributes: These are the diseases such as Jujube Sun Burn, Jujube Anthracnose, Jujube Fresh, and Jujube Brown Spot. Applying the steps of data augmentation techniques, the dataset was preprocessed to make the model more effective. Deep learning models such as Xception, VGG19, InceptionV3, MobileNetV2 and a Custom CNN was used. Hence, MobileNetV2 was identified as the model with the highest accuracy of 98.75% higher than other models for disease classification. As a result, this study has proved that transfer learning produces high accuracy and efficient models for detecting diseases to implement the models practically for disease management in agricultural fields for perpetuating sustainable farming practices and food security in Bangladesh.