An efficient deep learning approach to detect plant diseases using image data

Abstract

Plant diseases drastically impact on the yield and quality of crops and this is a great challenge to food security and sustainable agriculture in the world and more so in countries such as Bangladesh. There is need to therefore ensure the early and accurate diagnosis of plant diseases to enhance productivity and safeguard food supplies. Plant disease classification based on images has been highly successful with the swift development of deep learning, especially Convolutional Neural Networks (CNNs).In this thesis, a lightweight hybrid CNN-Transformer model is suggested as a classification of rice leaf disease. A number of pretrained CNN models, such as ResNet50, VGG16, InceptionV3, MobileNet V3, EfficientNetB0, and DenseNet121 are tested with transfer learning to determine the baseline performance. The dataset used in the study is the 23, 265 images of rice leaf in 10 different classes including healthy and diseased. To (hopefully) bypass the shortcomings of single-architecture models, the hybrid model proposed replaces local feature extraction with EfficientNetB0 and global contextual learning with TinyViT as well as a lightweight version of MobileNet. The experimental findings indicate that the hybrid method is better than single CNN and independent Transformer models as it has an accuracy of 98.04%with high precision, recall, and F1-score and low costs of computation. The results demonstrate the usefulness of lightweight CNN-Transformer models in real-time and resource-limited agricultural disease prediction and farm health crop management.