Tomato Leaf Disease Detection based on the Improved YOLOv10n

Abstract

In view of the difficulty in distinguishing tomato leaf lesions and the poor identification efficiency, an improved disease detection algorithm CGS-YOLOv10n based on YOLOv10n network was proposed. Firstly, the C2f in the global network is replaced by a multi-scale feature fusion C2f_DBB module, which can extract features more effectively. Secondly, SIOU-SoftNMS is partially used in the neck network to accurately deal with the differences between different targets, so as to improve the classification performance. Finally, the GDetect module was used to replace the detection head part of the original head network, so as to improve the accuracy and efficiency of recognition. The results show that compared with the traditional YOLOv10n, the improved CGS-YOLOv10n algorithm has an accuracy of 93.8%, a recall rate of 91.6%, and an average accuracy of 95.8%. The improved model has higher accuracy and stability in the disease detection of tomato leaf images, which can not only locate and identify targets more accurately, but also effectively reduce the rate of missed detection and false detection. The method proposed in this study provides a reliable technical means for the timely prevention and precise management of tomato diseases in agricultural production, which is helpful to promote the healthy development and sustainable development of agricultural industry.