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Agronomy
Volume 15
Issue 5
10.3390/agronomy15051230
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards

by
Li Zhang
1,
Zhihui He
2,
Haobin Zhu
1,
Zhanhong Wei
1,
Juan Lu
1 and
Xiongkui He
3,4,*
1
College of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
2
Robotics Institute, Ningbo University of Technology, Ningbo 315211, China
3
College of Agricultural Unmanned Systems, China Agricultural University, Beijing 100193, China
4
Centre for Chemicals Application Technology, China Agricultural University, Beijing 100193, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(5), 1230; https://doi.org/10.3390/agronomy15051230 (registering DOI)
Submission received: 26 March 2025 / Revised: 8 May 2025 / Accepted: 14 May 2025 / Published: 18 May 2025
(This article belongs to the Special Issue Agricultural Environment and Intelligent Plant Protection Equipment—2nd Edition)
Versions Notes

Abstract

To address the challenge of low operational efficiency in apple harvesting robots, this study proposes an adaptive grasping sequence planning methodology that combines Self-Organizing Maps (SOMs) and genetic algorithms (GAs). The proposed adaptive SOM—GA hybrid algorithm aims to minimize cycle time by optimizing the path planning between the fruit detection and grasping phases. First of all, we propose a density-aware adaptive mechanism that dynamically adjusts planning strategies based on fruit count thresholds. In addition, the proposed grasping sequence planning framework for high-density dwarf cultivation (HDDC) orchards is validated through threshold sensitivity analysis and empirical analysis of over 500 real-world fruit distribution samples. Finally, comparative experiments demonstrate that our proposed method reduces path length in high-density scenarios. Statistical analysis reveals a bimodal fruit distribution, which aligns the algorithm’s adaptive thresholds with real-world operational demands. These advancements improve theoretical research and enhance the commercial viability in agricultural robotics.
Keywords: design harvesting robots; grasping sequence plan; traveling salesman problem design harvesting robots; grasping sequence plan; traveling salesman problem

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MDPI and ACS Style

Zhang, L.; He, Z.; Zhu, H.; Wei, Z.; Lu, J.; He, X. Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards. Agronomy 2025, 15, 1230. https://doi.org/10.3390/agronomy15051230

AMA Style

Zhang L, He Z, Zhu H, Wei Z, Lu J, He X. Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards. Agronomy. 2025; 15(5):1230. https://doi.org/10.3390/agronomy15051230

Chicago/Turabian Style

Zhang, Li, Zhihui He, Haobin Zhu, Zhanhong Wei, Juan Lu, and Xiongkui He. 2025. "Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards" Agronomy 15, no. 5: 1230. https://doi.org/10.3390/agronomy15051230

APA Style

Zhang, L., He, Z., Zhu, H., Wei, Z., Lu, J., & He, X. (2025). Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards. Agronomy, 15(5), 1230. https://doi.org/10.3390/agronomy15051230

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