Plant-Leaf Diseases Classification using CNN, CBAM and Vision Transformer

Detecting plant diseases is usually difficult without an experts knowledge. In this study we want to propose a new classification model based on deep learning that will be able to classify and identify different plant-leaf diseases with high accuracy that outperforms the state of the art approaches and previous works. Using only training images, CNN can automatically extract features for classification, and achieve high classification performance. We used two datasets in this study, PlantVillage dataset containing 54,303 healthy and unhealthy leaf images divided into 38 categories by species and disease, and Tomato dataset containing 11,000 healthy and unhealthy tomato leaf images with nine diseases to train the models. We propose a deep convolutional neural network architecture, with and without attention mechanism, and we tuned 4 pretrained models that have been trained on large dataset such as MobileNet, VGG-16, VGG-19 and ResNET. We also tuned 2 ViT models, the vit b32 from keras and the base patch 16 from google. Our porposed model obtained an accuracy up to 97.74%. The pretrained models gave an accuracy up to 99.52%. And the ViT models obtained an accuracy up to 99.7%. This study may aid in detecting the plant leaf diseases and improve life conditions to plants which will improve quality of humans life.