DETECTION OF CUCURBITS’ FRUITS BASED ON DEEP LEARNING

Abstract

Cucurbitaceae is widely planted and its fruits have great economic value. Object detection is one of the key aspects of cucurbit harvesting. In this paper, four models, YOLOv3, YOLOv4, YOLOv5s and improved Resnet_YOLO, were used to detect mixed bitter melon, cucumber, white melon, and "Boyang 9" melon fruits. Fruit images of bitter melon, cucumber, white melon and "Boyang 9" melon were collected under different natural conditions for model training. The results showed that "Boyang 9" melon had the best overall detection results among the four cucurbit species, with the highest AP and F1, 0.99 and 0.94 respectively. The YOLOv5s model performed best among the four models: the best weights size was the smallest at 14 MB; the better mAP value of 0.971 for all classes of cucurbits; and the fastest detection speed with fps of 90.9. This paper shows that four types of cucurbit fruit images, bitter melon, cucumber, white melon, and "Boyang 9" melon, can be detected based on deep learning methods for hybrid detection.