Bell Pepper Leaf Disease Classification Using Fine-Tuned Transfer Learning

Abstract

Leaf diseases of plants are common worldwide. Using image processing, farmers could spot diseases in pepper plants more rapidly and get advice from plant disease experts. In this paper, researchers developed a Transfer Learning classification model for bell pepper leaf disease, with the Transfer Learning model trained on images of healthy and diseased bell pepper leaves. Classification of healthy and diseased bell pepper leaves has been carried out, and fine-tuned Transfer Learning has been applied using several pre-trained CNN models. To achieve the best outcome, four pre-trained models, including MobileNet, VGG16, ResNetV250, and DenseNet121, and three Fully Connected (FC) layer architectures were tested. The Fully Connected (FC) layer with four Transfer Learning architectures achieved the best accuracy value of 99.33% on DenseNet121 architecture with one layer and Cohen’s Kappa value of 0.9865.