ASD-YOLO: a lightweight network for coffee fruit ripening detection in complex scenarios
Coffee is one of the most popular and widely used drinks worldwide. At present, how to judge the maturity of coffee fruit mainly depends on the visual inspection of human eyes, which is both time-consuming and labor-intensive. Moreover, the occlusion between leaves and fruits is also one of the chal...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1484784/full |
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| author | Baofeng Ye Baofeng Ye Renzheng Xue Renzheng Xue Haiqiang Xu Haiqiang Xu |
| author_facet | Baofeng Ye Baofeng Ye Renzheng Xue Renzheng Xue Haiqiang Xu Haiqiang Xu |
| author_sort | Baofeng Ye |
| collection | DOAJ |
| description | Coffee is one of the most popular and widely used drinks worldwide. At present, how to judge the maturity of coffee fruit mainly depends on the visual inspection of human eyes, which is both time-consuming and labor-intensive. Moreover, the occlusion between leaves and fruits is also one of the challenges. In order to improve the detection efficiency of coffee fruit maturity, this paper proposes an improved detection method based on YOLOV7 to efficiently identify the maturity of coffee fruits, called ASD-YOLO. Firstly, a new dot product attention mechanism (L-Norm Attention) is designed to embed attention into the head structure, which enhances the ability of the model to extract coffee fruit features. In addition, we introduce SPD-Conv into backbone and head to enhance the detection of occluded small objects and low-resolution images. Finally, we replaced upsampling in our model with DySample, which requires less computational resources and is able to achieve image resolution improvements without additional burden. We tested our approach on the coffee dataset provided by Roboflow. The results show that ASD-YOLO has a good detection ability for coffee fruits with dense distribution and mutual occlusion under complex background, with a recall rate of 78.4%, a precision rate of 69.8%, and a mAP rate of 80.1%. Compared with the recall rate, accuracy rate and mAP of YOLOv7 model, these results are increased by 2.0%, 1.1% and 2.1%, respectively. The enhanced model can identify coffee fruits at all stages more efficiently and accurately, and provide technical reference for intelligent coffee fruit harvesting. |
| format | Article |
| id | doaj-art-8a5f97abfc3144b7bf27d2fb83ef806b |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-8a5f97abfc3144b7bf27d2fb83ef806b2025-02-10T06:48:50ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-02-011610.3389/fpls.2025.14847841484784ASD-YOLO: a lightweight network for coffee fruit ripening detection in complex scenariosBaofeng Ye0Baofeng Ye1Renzheng Xue2Renzheng Xue3Haiqiang Xu4Haiqiang Xu5School of Computer and Control Engineering, Qiqihar University, Qiqihar, ChinaHeilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihaer University, Qiqihar, ChinaSchool of Computer and Control Engineering, Qiqihar University, Qiqihar, ChinaHeilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihaer University, Qiqihar, ChinaSchool of Computer and Control Engineering, Qiqihar University, Qiqihar, ChinaHeilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihaer University, Qiqihar, ChinaCoffee is one of the most popular and widely used drinks worldwide. At present, how to judge the maturity of coffee fruit mainly depends on the visual inspection of human eyes, which is both time-consuming and labor-intensive. Moreover, the occlusion between leaves and fruits is also one of the challenges. In order to improve the detection efficiency of coffee fruit maturity, this paper proposes an improved detection method based on YOLOV7 to efficiently identify the maturity of coffee fruits, called ASD-YOLO. Firstly, a new dot product attention mechanism (L-Norm Attention) is designed to embed attention into the head structure, which enhances the ability of the model to extract coffee fruit features. In addition, we introduce SPD-Conv into backbone and head to enhance the detection of occluded small objects and low-resolution images. Finally, we replaced upsampling in our model with DySample, which requires less computational resources and is able to achieve image resolution improvements without additional burden. We tested our approach on the coffee dataset provided by Roboflow. The results show that ASD-YOLO has a good detection ability for coffee fruits with dense distribution and mutual occlusion under complex background, with a recall rate of 78.4%, a precision rate of 69.8%, and a mAP rate of 80.1%. Compared with the recall rate, accuracy rate and mAP of YOLOv7 model, these results are increased by 2.0%, 1.1% and 2.1%, respectively. The enhanced model can identify coffee fruits at all stages more efficiently and accurately, and provide technical reference for intelligent coffee fruit harvesting.https://www.frontiersin.org/articles/10.3389/fpls.2025.1484784/fullobject detectionYOLOv7attention mechanismcoffee fruitsmart agriculture |
| spellingShingle | Baofeng Ye Baofeng Ye Renzheng Xue Renzheng Xue Haiqiang Xu Haiqiang Xu ASD-YOLO: a lightweight network for coffee fruit ripening detection in complex scenarios Frontiers in Plant Science object detection YOLOv7 attention mechanism coffee fruit smart agriculture |
| title | ASD-YOLO: a lightweight network for coffee fruit ripening detection in complex scenarios |
| title_full | ASD-YOLO: a lightweight network for coffee fruit ripening detection in complex scenarios |
| title_fullStr | ASD-YOLO: a lightweight network for coffee fruit ripening detection in complex scenarios |
| title_full_unstemmed | ASD-YOLO: a lightweight network for coffee fruit ripening detection in complex scenarios |
| title_short | ASD-YOLO: a lightweight network for coffee fruit ripening detection in complex scenarios |
| title_sort | asd yolo a lightweight network for coffee fruit ripening detection in complex scenarios |
| topic | object detection YOLOv7 attention mechanism coffee fruit smart agriculture |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1484784/full |
| work_keys_str_mv | AT baofengye asdyoloalightweightnetworkforcoffeefruitripeningdetectionincomplexscenarios AT baofengye asdyoloalightweightnetworkforcoffeefruitripeningdetectionincomplexscenarios AT renzhengxue asdyoloalightweightnetworkforcoffeefruitripeningdetectionincomplexscenarios AT renzhengxue asdyoloalightweightnetworkforcoffeefruitripeningdetectionincomplexscenarios AT haiqiangxu asdyoloalightweightnetworkforcoffeefruitripeningdetectionincomplexscenarios AT haiqiangxu asdyoloalightweightnetworkforcoffeefruitripeningdetectionincomplexscenarios |