Convolution Network Enlightened Transformer for Regional Crop Disease Classification

Abstract

The overarching goal of smart farming is to propose pioneering solutions for future sustainability of humankind. It is important to recognize the image captured for monitoring the growth of plants and preventing diseases and pests. Currently, the task of automatic recognition of crop diseases is to research crop diseases based on deep learning, but the existing classifiers have problems regarding, for example, accurate identification of similar disease categories. Tomato is selected as the crop of this article, and the corresponding tomato disease is the main research point. The vision transformer (VIT) method has achieved good results on image tasks. Aiming at image recognition, tomato plant images serve as this article’s data source, and their structure is improved based on global ViT and local CNN (convolutional neural network) networks, which are built to diagnose disease images. Therefore, the features of plant images can be precisely and efficiently extracted, which is more convenient than traditional artificial recognition. The proposed architecture’s efficiency was evaluated by three image sets from three tomato-growing areas and acquired by drone and camera. The results show that this article method garners an average counting accuracy of 96.30%. It provides scientific support and a reference for the decision-making process of precision agriculture.