Detection of Different Kinds of Bacterial and Fungal Diseases in Jackfruit Using Image Processing

Abstract

This study presents the development of an AI-driven tool for detecting bacterial and fungal diseases in jackfruit using advanced image processing and deep learning techniques. Leveraging four pretrained convolutional neural network (CNN) models VGG19, MobileNetV2, EfficientNetB0, and ResNet50, alongside an ensemble approach, the study focuses on achieving high accuracy and reliability in disease classification. The dataset, comprising jackfruit images categorized into Healthy, Bacteria Affected, and Fungus Affected classes, undergoes preprocessing steps such as normalization, resizing, and augmentation to ensure robust training and evaluation. Among the individual CNN models, VGG19 and MobileNetV2 demonstrate superior performance, with accuracies of 95.84% and 93.35% on the test set, respectively, while EfficientNetB0 exhibits the lowest performance due to instability in learning. The ensemble model significantly enhances classification performance, achieving a near-perfect accuracy of 99.83% and an AUC score of 1.00 across all classes, combining the strengths of individual models to minimize misclassifications. Furthermore, the study implements the model in a mobile application, "Jackfruit-Doctor," providing real-time disease detection with high accuracy and accessibility for end users. This application empowers farmers by enabling early intervention, reducing crop losses, and promoting sustainable agricultural practices. The findings demonstrate the efficacy of integrating ensemble learning with mobile technology, offering a reliable and scalable solution for agricultural disease management while contributing to food security and economic sustainability.