RF-YOLOv7: A Model for the Detection of Poor-Quality Grapes in Natural Environments

Changyong Li (), Shunchun Zhang and Zhijie Ma
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Changyong Li: College of Mechanical Engineering, Xinjiang University, Urumqi 830047, China
Shunchun Zhang: College of Mechanical Engineering, Xinjiang University, Urumqi 830047, China
Zhijie Ma: College of Mechanical Engineering, Xinjiang University, Urumqi 830047, China

Agriculture, 2025, vol. 15, issue 4, 1-16

Abstract: This study addresses the challenges of detecting inferior fruits in table grapes in natural environments, focusing on subtle appearance differences, occlusions, and fruit overlaps. We propose an enhanced green grape fruit disease detection model named RF-YOLOv7. The model is trained on a dataset comprising images of small fruits, sunburn, excess grapes, fruit fractures, and poor-quality grape bunches. RF-YOLOv7 builds upon the YOLOv7 architecture by integrating four Contextual Transformer (CoT) modules to improve target-detection accuracy, employing the Wise-IoU (WIoU) loss function to enhance generalization and overall performance, and introducing the Bi-Former attention mechanism for dynamic query awareness sparsity. The experimental results demonstrate that RF-YOLOv7 achieves a detection accuracy of 83.5%, recall rate of 76.4%, mean average precision (mAP) of 80.1%, and detection speed of 58.8 ms. Compared to the original YOLOv7, RF-YOLOv7 exhibits a 3.5% increase in mAP, with only an 8.3 ms increase in detection time. This study lays a solid foundation for the development of automatic detection equipment for intelligent grape pruning.

Keywords: image processing; YOLOv7; deep learning; attention mechanism; disease detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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