Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge
We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with...
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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/2077-0472/15/14/1536 |
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| author | Shuntaro Aotake Takuya Otani Masatoshi Funabashi Atsuo Takanishi |
| author_facet | Shuntaro Aotake Takuya Otani Masatoshi Funabashi Atsuo Takanishi |
| author_sort | Shuntaro Aotake |
| collection | DOAJ |
| description | We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments. |
| format | Article |
| id | doaj-art-b348d00f83b5490bb33d840ca1f49594 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-b348d00f83b5490bb33d840ca1f495942025-08-20T03:55:49ZengMDPI AGAgriculture2077-04722025-07-011514153610.3390/agriculture15141536Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert KnowledgeShuntaro Aotake0Takuya Otani1Masatoshi Funabashi2Atsuo Takanishi3Sony Computer Science Laboratories, Inc., Tokyo 141-0022, JapanDepartment of Systems Science and Engineering, Shibaura Institute of Technology, Tokyo 135-8548, JapanSony Computer Science Laboratories, Inc., Tokyo 141-0022, JapanFaculty of Science and Engineering, Waseda University, Tokyo 169-8555, JapanWe propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments.https://www.mdpi.com/2077-0472/15/14/1536agricultural robotssowingpolycultureimage processingfew-shot learning |
| spellingShingle | Shuntaro Aotake Takuya Otani Masatoshi Funabashi Atsuo Takanishi Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge Agriculture agricultural robots sowing polyculture image processing few-shot learning |
| title | Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge |
| title_full | Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge |
| title_fullStr | Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge |
| title_full_unstemmed | Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge |
| title_short | Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge |
| title_sort | data efficient sowing position estimation for agricultural robots combining image analysis and expert knowledge |
| topic | agricultural robots sowing polyculture image processing few-shot learning |
| url | https://www.mdpi.com/2077-0472/15/14/1536 |
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