Intelligent Fruit Localization and Grasping Method Based on YOLO VX Model and 3D Vision
Recent years have seen significant interest among agricultural researchers in using robotics and machine vision to enhance intelligent orchard harvesting efficiency. This study proposes an improved hybrid framework integrating YOLO VX deep learning, 3D object recognition, and SLAM-based navigation f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/14/1508 |
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| author | Zhimin Mei Yifan Li Rongbo Zhu Shucai Wang |
| author_facet | Zhimin Mei Yifan Li Rongbo Zhu Shucai Wang |
| author_sort | Zhimin Mei |
| collection | DOAJ |
| description | Recent years have seen significant interest among agricultural researchers in using robotics and machine vision to enhance intelligent orchard harvesting efficiency. This study proposes an improved hybrid framework integrating YOLO VX deep learning, 3D object recognition, and SLAM-based navigation for harvesting ripe fruits in greenhouse environments, achieving servo control of robotic arms with flexible end-effectors. The method comprises three key components: First, a fruit sample database containing varying maturity levels and morphological features is established, interfaced with an optimized YOLO VX model for target fruit identification. Second, a 3D camera acquires the target fruit’s spatial position and orientation data in real time, and these data are stored in the collaborative robot’s microcontroller. Finally, employing binocular calibration and triangulation, the SLAM navigation module guides the robotic arm to the designated picking location via unobstructed target positioning. Comprehensive comparative experiments between the improved YOLO v12n model and earlier versions were conducted to validate its performance. The results demonstrate that the optimized model surpasses traditional recognition and harvesting methods, offering superior target fruit identification response (minimum 30.9ms) and significantly higher accuracy (91.14%). |
| format | Article |
| id | doaj-art-6a0d5f5aa04d438c9be5bd33cee5a209 |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-6a0d5f5aa04d438c9be5bd33cee5a2092025-08-20T03:13:36ZengMDPI AGAgriculture2077-04722025-07-011514150810.3390/agriculture15141508Intelligent Fruit Localization and Grasping Method Based on YOLO VX Model and 3D VisionZhimin Mei0Yifan Li1Rongbo Zhu2Shucai Wang3School of Intelligent Manufacturing, Wuchang Institute of Technology, Wuhan 430065, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaRecent years have seen significant interest among agricultural researchers in using robotics and machine vision to enhance intelligent orchard harvesting efficiency. This study proposes an improved hybrid framework integrating YOLO VX deep learning, 3D object recognition, and SLAM-based navigation for harvesting ripe fruits in greenhouse environments, achieving servo control of robotic arms with flexible end-effectors. The method comprises three key components: First, a fruit sample database containing varying maturity levels and morphological features is established, interfaced with an optimized YOLO VX model for target fruit identification. Second, a 3D camera acquires the target fruit’s spatial position and orientation data in real time, and these data are stored in the collaborative robot’s microcontroller. Finally, employing binocular calibration and triangulation, the SLAM navigation module guides the robotic arm to the designated picking location via unobstructed target positioning. Comprehensive comparative experiments between the improved YOLO v12n model and earlier versions were conducted to validate its performance. The results demonstrate that the optimized model surpasses traditional recognition and harvesting methods, offering superior target fruit identification response (minimum 30.9ms) and significantly higher accuracy (91.14%).https://www.mdpi.com/2077-0472/15/14/1508intelligent fruit localizationthree-dimensional visionYOLO VX |
| spellingShingle | Zhimin Mei Yifan Li Rongbo Zhu Shucai Wang Intelligent Fruit Localization and Grasping Method Based on YOLO VX Model and 3D Vision Agriculture intelligent fruit localization three-dimensional vision YOLO VX |
| title | Intelligent Fruit Localization and Grasping Method Based on YOLO VX Model and 3D Vision |
| title_full | Intelligent Fruit Localization and Grasping Method Based on YOLO VX Model and 3D Vision |
| title_fullStr | Intelligent Fruit Localization and Grasping Method Based on YOLO VX Model and 3D Vision |
| title_full_unstemmed | Intelligent Fruit Localization and Grasping Method Based on YOLO VX Model and 3D Vision |
| title_short | Intelligent Fruit Localization and Grasping Method Based on YOLO VX Model and 3D Vision |
| title_sort | intelligent fruit localization and grasping method based on yolo vx model and 3d vision |
| topic | intelligent fruit localization three-dimensional vision YOLO VX |
| url | https://www.mdpi.com/2077-0472/15/14/1508 |
| work_keys_str_mv | AT zhiminmei intelligentfruitlocalizationandgraspingmethodbasedonyolovxmodeland3dvision AT yifanli intelligentfruitlocalizationandgraspingmethodbasedonyolovxmodeland3dvision AT rongbozhu intelligentfruitlocalizationandgraspingmethodbasedonyolovxmodeland3dvision AT shucaiwang intelligentfruitlocalizationandgraspingmethodbasedonyolovxmodeland3dvision |