Abstract: The accurate detection of mature green tomato is a prerequisite of automatic picking. Owing to color similarity between the surface color of mature green tomato, leaf, and branch, as well as the existence of images with leaf, branch occlusion and fruit overlapping, the detection performance of traditional image detection was bad. To solve this problem, an improved YOLO-v3 algorithm was designed for mature green tomato image detection, where the original DarkNet-53 backbone was replaced by the lightweight Mobilenet-v1. Compared with the original YOLO-v3 algorithm, the lightweight algorithm reduced the model size by 39.38%, increased the train speed by 3.88 times, and the mean average precision of the validation set and test set was 98.69% and 98.28%, respectively.Therefore, the proposed lightweight YOLO-v3 could successfully realize real-time detection of mature green tomato and is more suitable to be implement in mobile and embedded devices, which will make automatic picking of tomato more efficient.