Intelligent Fruit Localization and Grasping Method Based on YOLO VX Model and 3D Vision

Zhimin Mei, Yifan Li, Rongbo Zhu and Shucai Wang ()
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Zhimin Mei: School of Intelligent Manufacturing, Wuchang Institute of Technology, Wuhan 430065, China
Yifan Li: College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
Rongbo Zhu: College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
Shucai Wang: College of Informatics, Huazhong Agricultural University, Wuhan 430070, China

Agriculture, 2025, vol. 15, issue 14, 1-21

Abstract: 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%).

Keywords: intelligent fruit localization; three-dimensional vision; YOLO VX (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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