Affiliations:
Shandong Provincial University Laboratory for Protected Horticulture, Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science and Technology, Weifang, Shandong, China
Correspondence:
[*]
Corresponding author. Dugang Guo and Jun Liu, Shandong Provincial University Laboratory for Protected Horticulture, Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science and Technology, Weifang, Shandong, 262700, China. E-mails: guodugang@wfust.edu.cn and Liu_jun860116@wfust.edu.cn.
Abstract: Plant disease is one of the major threats to food security. Accurate diagnosis of plant diseases can benefit the agricultural production. For the purpose of real-time plant disease diagnostics, the deep learning models are employed. In this study, we present an accurate identification method for common diseases of tomatoes based on deep-learning methods. The devising of multi-resolution detector, in line with bounding box generating and assigning, facilitates the feature extracting process of detection. The employment of an dropout and ADAMW (Adaptive moment estimation with decoupled weight decay) optimizer further resolve the overfitting problem. Using the collected images of healthy and diseased tomatoes, our detector is trained to identify 10 different diseases. Experimental results showed that the disease identification method proposed in this study could accurately and rapidly identify common diseases of tomato with an average accuracy of 85.03%and a recognition speed of 61 frames per second, which was superior to other models under the same conditions and was beneficial for tomato disease control work.
Keywords: Plant diseases, deep learning
model, multi-resolution detection layers, bounding box, single shot multibox detector
