In order to solve the problems of low accuracy and poor effect of tomato leaf disease detection in natural environment, a tomato leaf disease detection model based on optimized YOLOv8 was proposed, namely GDDL-YOLOv8n. In this model, the original backbone network is improved by using GhostHGNetV2, C2f-DWR-DRB is used to improve the neck network feature fusion, and the Lightweight Shared-Convolutional detection head (LSCG) is innovatively introduced. The lightweight and high-precision detection effect of the model has been successfully realized. Experimental results show that the GDDL-YOLOv8n model decreases by 49.13% in the number of parameters, 37.04% in the amount of computation, and 46.67% in the memory occupation of the model, while maintaining the high-precision detection performance, with the mAP@0.5 reaching 98.4% and the mAP@0.5-0.95 reaching 92.3%. In addition, a user-friendly interface system based on PyQt5 was developed, which supports image and video detection and camera real-time tracking and recognition functions, and the intelligent management of agriculture and the identification technology of tomato leaf disease have been significantly enhanced, and the model is lighter, which greatly promotes the application of these technologies in actual production.