IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i14p1536-d1703016.html
   My bibliography  Save this article

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

Author

Listed:
  • Shuntaro Aotake

    (Sony Computer Science Laboratories, Inc., Tokyo 141-0022, Japan
    Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, 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
    Center for Social Common Capital Beyond 2050, Kyoto University, Kyoto 606-8501, Japan)

  • Atsuo Takanishi

    (Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan)

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.

Suggested Citation

  • Shuntaro Aotake & Takuya Otani & Masatoshi Funabashi & Atsuo Takanishi, 2025. "Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge," Agriculture, MDPI, vol. 15(14), pages 1-26, July.
  • Handle: RePEc:gam:jagris:v:15:y:2025:i:14:p:1536-:d:1703016
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/14/1536/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/15/14/1536/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kousaku Ohta & Tatsuya Kawaoka & Masatoshi Funabashi, 2020. "Secondary Metabolite Differences between Naturally Grown and Conventional Coarse Green Tea," Agriculture, MDPI, vol. 10(12), pages 1-23, December.
    2. Jinzhu Lu & Lijuan Tan & Huanyu Jiang, 2021. "Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification," Agriculture, MDPI, vol. 11(8), pages 1-18, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fahman Saeed & Muhammad Hussain & Hatim A. Aboalsamh, 2022. "Automatic Fingerprint Classification Using Deep Learning Technology (DeepFKTNet)," Mathematics, MDPI, vol. 10(8), pages 1-17, April.
    2. Jinzhu Lu & Kaiqian Peng & Qi Wang & Cong Sun, 2023. "Lettuce Plant Trace-Element-Deficiency Symptom Identification via Machine Vision Methods," Agriculture, MDPI, vol. 13(8), pages 1-27, August.
    3. Mosleh Hmoud Al-Adhaileh & Amit Verma & Theyazn H. H. Aldhyani & Deepika Koundal, 2023. "Potato Blight Detection Using Fine-Tuned CNN Architecture," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
    4. Zhi-Ben Yin & Fu-Yong Liu & Hui Geng & Ya-Jun Xi & De-Bin Zeng & Chun-Jing Si & Ming-Deng Shi, 2024. "A high-precision jujube disease spot detection based on SSD during the sorting process," PLOS ONE, Public Library of Science, vol. 19(1), pages 1-28, January.
    5. Xia Hao & Man Zhang & Tianru Zhou & Xuchao Guo & Federico Tomasetto & Yuxin Tong & Minjuan Wang, 2021. "An Automatic Light Stress Grading Architecture Based on Feature Optimization and Convolutional Neural Network," Agriculture, MDPI, vol. 11(11), pages 1-17, November.
    6. Mingfeng Huang & Guoqin Xu & Junyu Li & Jianping Huang, 2021. "A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++," Agriculture, MDPI, vol. 11(12), pages 1-14, December.
    7. Hieu T. T. L. Pham & Mahdi Rafieizonooz & SangUk Han & Dong-Eun Lee, 2021. "Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction," Sustainability, MDPI, vol. 13(24), pages 1-37, December.
    8. Sen Lin & Yucheng Xiu & Jianlei Kong & Chengcai Yang & Chunjiang Zhao, 2023. "An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture," Agriculture, MDPI, vol. 13(3), pages 1-20, February.
    9. Takuya Otani & Akira Itoh & Hideki Mizukami & Masatsugu Murakami & Shunya Yoshida & Kota Terae & Taiga Tanaka & Koki Masaya & Shuntaro Aotake & Masatoshi Funabashi & Atsuo Takanishi, 2022. "Agricultural Robot under Solar Panels for Sowing, Pruning, and Harvesting in a Synecoculture Environment," Agriculture, MDPI, vol. 13(1), pages 1-22, December.
    10. Xiang Zhang & Huiyi Gao & Li Wan, 2022. "Classification of Fine-Grained Crop Disease by Dilated Convolution and Improved Channel Attention Module," Agriculture, MDPI, vol. 12(10), pages 1-16, October.
    11. Li Zhang & Qun Hao & Jie Cao, 2023. "Attention-Based Fine-Grained Lightweight Architecture for Fuji Apple Maturity Classification in an Open-World Orchard Environment," Agriculture, MDPI, vol. 13(2), pages 1-20, January.
    12. Bulent Tugrul & Elhoucine Elfatimi & Recep Eryigit, 2022. "Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review," Agriculture, MDPI, vol. 12(8), pages 1-21, August.
    13. Hamed Alghamdi & Turki Turki, 2023. "PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features," Agriculture, MDPI, vol. 13(5), pages 1-19, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:15:y:2025:i:14:p:1536-:d:1703016. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.