Design and Research of an Online Diagnosis Platform for Tomato Seedling Facilities Production Diseases

Abstract

Abstract The facility-based production method is an important stage in the development of modern agriculture, lifting natural light and temperature restrictions and helping to improve agricultural production efficiency. To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities, an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment. The diagnostic platform consists of three main parts: pest and disease information detection, clustering and decision making of detection results, and platform diagnostic display. Firstly, based on the YOLOv5 algorithm, the key information of the disease was extracted by adding attention module (CBAM), multi-scale feature fusion was performed using weighted bi-directional feature pyramid network (BiFPN), and the overall construction was designed to be compressed and lightweight; Secondly, a K-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications; Finally, a web-side pest detection platform was designed and developed using Python, including the front-end, back-end, and database of the system to realize online diagnosis and interaction of tomato plant diseases and pests. The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP of 92.7% (mAP@0.5), weights of 12.8M, inference time of 33.6ms. Compared with the current mainstream single-stage detection series algorithms, the improved algorithm model has achieved better performance; The accuracy rate of the platform diagnosis output disease information of tomato images is 91.2%, and the accuracy rate of video disease information is 95.2%.