Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge

Shuntaro Aotake (), Takuya Otani, Masatoshi Funabashi and Atsuo Takanishi
Additional contact information
Shuntaro Aotake: Sony Computer Science Laboratories, Inc., Tokyo 141-0022, Japan
Takuya Otani: Department of Systems Science and Engineering, Shibaura Institute of Technology, Tokyo 135-8548, Japan
Masatoshi Funabashi: Sony Computer Science Laboratories, Inc., Tokyo 141-0022, Japan
Atsuo Takanishi: Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan

Agriculture, 2025, vol. 15, issue 14, 1-26

Abstract: 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.

Keywords: agricultural robots; sowing; polyculture; image processing; few-shot learning (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
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/14/1536/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/14/1536/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:14:p:1536-:d:1703016

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().