Precision Agriculture: Tomato Disease Classification via Compact Convolutional Vision Transformer

Abstract

Plant disease detection is a one of the most studied subjects in precision agriculture which aims to protect and improve agricultural crops. Commonly, intelligent systems based on CNN (Convolutional Neural Networks) are employed to identify multiple plant diseases by analyzing leaf images. In this work, we propose the use of the Compact Convolutional vision Transformer for tomato disease classification. Experiments conducted on a set of 10 tomato disease categories highlight the effectiveness of the proposed system which outperforms famous CNN models including DenseNet201, and MobileNetV2 by 1.73% in the overall classification accuracy.