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Open AccessArticle
CR-YOLOv9: Improved YOLOv9 Multi-Stage Strawberry Fruit Maturity Detection Application Integrated with CRNET
by
Rong Ye
Rong Ye 1,†,
Guoqi Shao
Guoqi Shao 2,†
,
Quan Gao
Quan Gao 3
,
Hongrui Zhang
Hongrui Zhang 4
and
Tong Li
Tong Li 2,3,*
1
College of Food Science and Technology, Yunnan Agricultural University, Kunming 650201, China
2
The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Yunnan Agricultural University, Kunming 650201, China
3
College of Big Data, Yunnan Agricultural University, Kunming 650201, China
4
College of Plant Protection, Yunnan Agricultural University, Kunming 650201, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Foods 2024, 13(16), 2571; https://doi.org/10.3390/foods13162571 (registering DOI)
Submission received: 27 June 2024
/
Revised: 13 August 2024
/
Accepted: 14 August 2024
/
Published: 17 August 2024
Abstract
Strawberries are a commonly used agricultural product in the food industry. In the traditional production model, labor costs are high, and extensive picking techniques can result in food safety issues, like poor taste and fruit rot. In response to the existing challenges of low detection accuracy and slow detection speed in the assessment of strawberry fruit maturity in orchards, a CR-YOLOv9 multi-stage method for strawberry fruit maturity detection was introduced. The composite thinning network, CRNet, is utilized for target fusion, employing multi-branch blocks to enhance images by restoring high-frequency details. To address the issue of low computational efficiency in the multi-head self-attention (MHSA) model due to redundant attention heads, the design concept of CGA is introduced. This concept aligns input feature grouping with the number of attention heads, offering the distinct segmentation of complete features for each attention head, thereby reducing computational redundancy. A hybrid operator, ACmix, is proposed to enhance the efficiency of image classification and target detection. Additionally, the Inner-IoU concept, in conjunction with Shape-IoU, is introduced to replace the original loss function, thereby enhancing the accuracy of detecting small targets in complex scenes. The experimental results demonstrate that CR-YOLOv9 achieves a precision rate of 97.52%, a recall rate of 95.34%, and an mAP@50 of 97.95%. These values are notably higher than those of YOLOv9 by 4.2%, 5.07%, and 3.34%. Furthermore, the detection speed of CR-YOLOv9 is 84, making it suitable for the real-time detection of strawberry ripeness in orchards. The results demonstrate that the CR-YOLOv9 algorithm discussed in this study exhibits high detection accuracy and rapid detection speed. This enables more efficient and automated strawberry picking, meeting the public’s requirements for food safety.
Share and Cite
MDPI and ACS Style
Ye, R.; Shao, G.; Gao, Q.; Zhang, H.; Li, T.
CR-YOLOv9: Improved YOLOv9 Multi-Stage Strawberry Fruit Maturity Detection Application Integrated with CRNET. Foods 2024, 13, 2571.
https://doi.org/10.3390/foods13162571
AMA Style
Ye R, Shao G, Gao Q, Zhang H, Li T.
CR-YOLOv9: Improved YOLOv9 Multi-Stage Strawberry Fruit Maturity Detection Application Integrated with CRNET. Foods. 2024; 13(16):2571.
https://doi.org/10.3390/foods13162571
Chicago/Turabian Style
Ye, Rong, Guoqi Shao, Quan Gao, Hongrui Zhang, and Tong Li.
2024. "CR-YOLOv9: Improved YOLOv9 Multi-Stage Strawberry Fruit Maturity Detection Application Integrated with CRNET" Foods 13, no. 16: 2571.
https://doi.org/10.3390/foods13162571
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