Stereo vision based broccoli recognition and attitude estimation method for field harvesting
At present, automatic broccoli harvest in field still faces some issues. It is difficult to segment broccoli in real time under complex field background, and hard to pick tilt-growing broccoli for the end-effector of robot. In this research, an improved YOLOv8n-seg model, named YOLO-Broccoli-Seg was...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-09-01
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| Series: | Artificial Intelligence in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721725000212 |
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| author | Zhenni He Fahui Yuan Yansuo Zhou Bingbo Cui Yong He Yufei Liu |
| author_facet | Zhenni He Fahui Yuan Yansuo Zhou Bingbo Cui Yong He Yufei Liu |
| author_sort | Zhenni He |
| collection | DOAJ |
| description | At present, automatic broccoli harvest in field still faces some issues. It is difficult to segment broccoli in real time under complex field background, and hard to pick tilt-growing broccoli for the end-effector of robot. In this research, an improved YOLOv8n-seg model, named YOLO-Broccoli-Seg was proposed for broccoli recognition. Through adding a triplet attention module to YOLOv8-Seg model, the feature fusion capability of the algorithm is improved significantly. The mean average precision mAP50 (Mask), mAP95 (Mask), mAP50 (Bounding Box, Bbox) and mAP95 (Bbox) of YOLO-Broccoli-Seg are 0.973, 0.683, 0.973 and 0.748 respectively. Precision P-value was improved the most, with an increment of 8.7 %. In addition, an attitude estimation method based on three-dimensional point cloud is proposed. When the tilt angle of broccoli is between −30°and 30°, the R2 between the estimated value and the true value is 0.934. It indicated that this method can well represent the growth attitude of broccoli. This research can provide the rich broccoli information and technical basis for the automated broccoli picking. |
| format | Article |
| id | doaj-art-eb9a0f1b0b064a1a87eeed15f0d20cac |
| institution | OA Journals |
| issn | 2589-7217 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj-art-eb9a0f1b0b064a1a87eeed15f0d20cac2025-08-20T02:26:09ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172025-09-0115352653610.1016/j.aiia.2025.02.002Stereo vision based broccoli recognition and attitude estimation method for field harvestingZhenni He0Fahui Yuan1Yansuo Zhou2Bingbo Cui3Yong He4Yufei Liu5College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Corresponding authors.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Corresponding authors.At present, automatic broccoli harvest in field still faces some issues. It is difficult to segment broccoli in real time under complex field background, and hard to pick tilt-growing broccoli for the end-effector of robot. In this research, an improved YOLOv8n-seg model, named YOLO-Broccoli-Seg was proposed for broccoli recognition. Through adding a triplet attention module to YOLOv8-Seg model, the feature fusion capability of the algorithm is improved significantly. The mean average precision mAP50 (Mask), mAP95 (Mask), mAP50 (Bounding Box, Bbox) and mAP95 (Bbox) of YOLO-Broccoli-Seg are 0.973, 0.683, 0.973 and 0.748 respectively. Precision P-value was improved the most, with an increment of 8.7 %. In addition, an attitude estimation method based on three-dimensional point cloud is proposed. When the tilt angle of broccoli is between −30°and 30°, the R2 between the estimated value and the true value is 0.934. It indicated that this method can well represent the growth attitude of broccoli. This research can provide the rich broccoli information and technical basis for the automated broccoli picking.http://www.sciencedirect.com/science/article/pii/S2589721725000212Broccoli recognitionInstance segmentationAttitude estimationYOLOv8n-SegAgricultural automation |
| spellingShingle | Zhenni He Fahui Yuan Yansuo Zhou Bingbo Cui Yong He Yufei Liu Stereo vision based broccoli recognition and attitude estimation method for field harvesting Artificial Intelligence in Agriculture Broccoli recognition Instance segmentation Attitude estimation YOLOv8n-Seg Agricultural automation |
| title | Stereo vision based broccoli recognition and attitude estimation method for field harvesting |
| title_full | Stereo vision based broccoli recognition and attitude estimation method for field harvesting |
| title_fullStr | Stereo vision based broccoli recognition and attitude estimation method for field harvesting |
| title_full_unstemmed | Stereo vision based broccoli recognition and attitude estimation method for field harvesting |
| title_short | Stereo vision based broccoli recognition and attitude estimation method for field harvesting |
| title_sort | stereo vision based broccoli recognition and attitude estimation method for field harvesting |
| topic | Broccoli recognition Instance segmentation Attitude estimation YOLOv8n-Seg Agricultural automation |
| url | http://www.sciencedirect.com/science/article/pii/S2589721725000212 |
| work_keys_str_mv | AT zhennihe stereovisionbasedbroccolirecognitionandattitudeestimationmethodforfieldharvesting AT fahuiyuan stereovisionbasedbroccolirecognitionandattitudeestimationmethodforfieldharvesting AT yansuozhou stereovisionbasedbroccolirecognitionandattitudeestimationmethodforfieldharvesting AT bingbocui stereovisionbasedbroccolirecognitionandattitudeestimationmethodforfieldharvesting AT yonghe stereovisionbasedbroccolirecognitionandattitudeestimationmethodforfieldharvesting AT yufeiliu stereovisionbasedbroccolirecognitionandattitudeestimationmethodforfieldharvesting |