IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i8p1251-d1445345.html
   My bibliography  Save this article

Prediction of Feed Quantity for Wheat Combine Harvester Based on Improved YOLOv5s and Weight of Single Wheat Plant without Stubble

Author

Listed:
  • Qian Zhang

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Qingshan Chen

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Wenjie Xu

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Lizhang Xu

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • En Lu

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

Abstract

In complex field environments, wheat grows densely with overlapping organs and different plant weights. It is difficult to accurately predict feed quantity for wheat combine harvester using the existing YOLOv5s and uniform weight of a single wheat plant in a whole field. This paper proposes a feed quantity prediction method based on the improved YOLOv5s and weight of a single wheat plant without stubble. The improved YOLOv5s optimizes Backbone with compact bases to enhance wheat spike detection and reduce computational redundancy. The Neck incorporates a hierarchical residual module to enhance YOLOv5s’ representation of multi-scale features. The Head enhances the detection accuracy of small, dense wheat spikes in a large field of view. In addition, the height of a single wheat plant without stubble is estimated by the depth distribution of the wheat spike region and stubble height. The relationship model between the height and weight of a single wheat plant without stubble is fitted by experiments. Then, feed quantity can be predicted using the weight of a single wheat plant without stubble estimated by the relationship model and the number of wheat plants detected by the improved YOLOv5s. The proposed method was verified through experiments with the 4LZ-6A combine harvester. Compared with the existing YOLOv5s, YOLOv7, SSD, Faster R-CNN, and other enhancements in this paper, the mAP 50 of wheat spikes detection by the improved YOLOv5s increased by over 6.8%. It achieved an average relative error of 4.19% with a prediction time of 1.34 s. The proposed method can accurately and rapidly predict feed quantity for wheat combine harvesters and further realize closed-loop control of intelligent harvesting operations.

Suggested Citation

  • Qian Zhang & Qingshan Chen & Wenjie Xu & Lizhang Xu & En Lu, 2024. "Prediction of Feed Quantity for Wheat Combine Harvester Based on Improved YOLOv5s and Weight of Single Wheat Plant without Stubble," Agriculture, MDPI, vol. 14(8), pages 1-29, July.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:8:p:1251-:d:1445345
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/8/1251/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/8/1251/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fazheng Wang & Yanbin Liu & Yaoming Li & Kuizhou Ji, 2023. "Research and Experiment on Variable-Diameter Threshing Drum with Movable Radial Plates for Combine Harvester," Agriculture, MDPI, vol. 13(8), pages 1-16, July.
    2. Xiaobo Zhuang & Yaoming Li, 2023. "Segmentation and Angle Calculation of Rice Lodging during Harvesting by a Combine Harvester," Agriculture, MDPI, vol. 13(7), pages 1-15, July.
    3. Xu Chen & Xun He & Wanzhang Wang & Zhe Qu & Yuan Liu, 2022. "Study on the Technologies of Loss Reduction in Wheat Mechanization Harvesting: A Review," Agriculture, MDPI, vol. 12(11), pages 1-18, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qian Wang & Wuchang Qin & Mengnan Liu & Junjie Zhao & Qingzhen Zhu & Yanxin Yin, 2024. "Semantic Segmentation Model-Based Boundary Line Recognition Method for Wheat Harvesting," Agriculture, MDPI, vol. 14(10), pages 1-14, October.

    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. Shiguo Wang & Bin Li & Shuren Chen & Zhong Tang & Weiwei Zhou & Xiaohu Guo, 2024. "Design and Performance Test of Soybean Profiling Header Suitable for Harvesting Bottom Pods on Film," Agriculture, MDPI, vol. 14(7), pages 1-16, June.
    2. Zhenwei Liang & Yongqi Qin & Zhan Su, 2024. "Establishment of a Feeding Rate Prediction Model for Combine Harvesters," Agriculture, MDPI, vol. 14(4), pages 1-15, April.
    3. Jie Ma & Qinghao He & Duanyang Geng & Lin Niu & Yipeng Cui & Qiming Yu & Jianning Yin & Yang Wang & Lei Ni, 2024. "Research and Experimentation on Sparse–Dense Interphase Curved-Tooth Sorghum Threshing Technology," Agriculture, MDPI, vol. 14(10), pages 1-15, October.
    4. Kibiya Abubakar Yusuf & Edwin O. Amisi & Qishuo Ding & Xinxin Chen & Gaoming Xu & Abdulaziz Nuhu Jibril & Moussita G. Gedeon & Zakariya M. Abdulhamid, 2024. "Novel Technical Parameters-Based Classification of Harvesters Using Principal Component Analysis and Q-Type Cluster Model," Agriculture, MDPI, vol. 14(6), pages 1-16, June.
    5. Bangzhui Wang & Kexin Que & Zhong Tang & Meiyan Sun & Yi Lian & Haoyang Wang, 2024. "Multivariate Structural Vibration Coupling Response of the Self-Propelled Straw Pickup Baler Under Time-Varying Loads," Agriculture, MDPI, vol. 14(11), pages 1-28, November.
    6. Xinzhong Wang & Tianyu Hong & Weiquan Fang & Xingye Chen, 2024. "Optimized Design for Vibration Reduction in a Residual Film Recovery Machine Frame Based on Modal Analysis," Agriculture, MDPI, vol. 14(4), pages 1-21, March.
    7. Shahin Ghaziani & Gholamreza Dehbozorgi & Mohammad Bakhshoodeh & Reiner Doluschitz, 2023. "Unraveling On-Farm Wheat Loss in Fars Province, Iran: A Qualitative Analysis and Exploration of Potential Solutions with Emphasis on Agricultural Cooperatives," Sustainability, MDPI, vol. 15(16), pages 1-24, August.

    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:14:y:2024:i:8:p:1251-:d:1445345. 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.