Resumen
Rising labor costs and a workforce shortage have impeded the development and economic benefits of the global grape industry. Research and development of intelligent grape harvesting technologies is desperately needed. Therefore, rapid and accurate identification of grapes is crucial for intelligent grape harvesting. However, object detection algorithms encounter multiple challenges in unstructured vineyards, such as similar background colors, light obstruction from greenhouses and leaves, and fruit occlusion. All of these factors contribute to the difficulty of correctly identifying grapes. The GrapeDetectNet (GDN), based on the YOLO (You Only Look Once) v5s, is proposed to improve grape detection accuracy and recall in unstructured vineyards. dual-channel feature extraction attention (DCFE) is a new attention structure introduced in GDN. We also use dynamic snake convolution (DS-Conv) in the backbone network. We collected an independent dataset of 1280 images after a strict selection process to evaluate GDN?s performance. The dataset encompasses examples of Shine Muscat and unripe Kyoho grapes, covering a range of complex outdoor situations. The results of the experiment demonstrate that GDN performed outstandingly on this dataset. Compared to YOLOv5s, this model increased metrics such as 2.02% of mAP0.5:0.95, 2.5% of mAP0.5, 1.4% of precision, 1.6% of recall, and 1.5% of F1 score. Finally, we test the method on a grape-picking robot, and the results show that our algorithm works remarkably well in harvesting experiments. The results indicate that the GDN grape detection model in this study exhibits high detection accuracy. It is proficient in identifying grapes and demonstrates good robustness in unstructured vineyards, providing a valuable empirical reference for the practical application of intelligent grape harvesting technology.