Center of Excellence in Robotics and Control, Advanced Robotics & Automated Systems (ARAS), Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Faculty of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran.
Sci Rep. 2022 Jul 7;12(1):11554. doi: 10.1038/s41598-022-15163-0.
Plant disease can diminish a considerable portion of the agricultural products on each farm. The main goal of this work is to provide visual information for the farmers to enable them to take the necessary preventive measures. A lightweight deep learning approach is proposed based on the Vision Transformer (ViT) for real-time automated plant disease classification. In addition to the ViT, the classical convolutional neural network (CNN) methods and the combination of CNN and ViT have been implemented for the plant disease classification. The models have been trained and evaluated on multiple datasets. Based on the comparison between the obtained results, it is concluded that although attention blocks increase the accuracy, they decelerate the prediction. Combining attention blocks with CNN blocks can compensate for the speed.
植物病害会使每个农场的农产品大量减少。这项工作的主要目的是为农民提供可视化信息，使他们能够采取必要的预防措施。提出了一种基于 Vision Transformer (ViT) 的轻量级深度学习方法，用于实时自动植物病害分类。除了 ViT 之外，还实现了经典卷积神经网络 (CNN) 方法以及 CNN 和 ViT 的组合，用于植物病害分类。模型已经在多个数据集上进行了训练和评估。通过对获得的结果进行比较，可以得出结论，尽管注意力块提高了准确性，但它们会降低预测速度。将注意力块与 CNN 块结合使用可以弥补速度的不足。
