IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i9p942-d1643069.html
   My bibliography  Save this article

Crop-Free-Ridge Navigation Line Recognition Based on the Lightweight Structure Improvement of YOLOv8

Author

Listed:
  • Runyi Lv

    (Jiangsu Provincial Key Laboratory of Hi-Tech Research for Intelligent Agricultural Equipment, Jiangsu University, Zhenjiang 212013, China)

  • Jianping Hu

    (Jiangsu Provincial Key Laboratory of Hi-Tech Research for Intelligent Agricultural Equipment, Jiangsu University, Zhenjiang 212013, China)

  • Tengfei Zhang

    (Jiangsu Provincial Key Laboratory of Hi-Tech Research for Intelligent Agricultural Equipment, Jiangsu University, Zhenjiang 212013, China)

  • Xinxin Chen

    (Jiangsu Provincial Key Laboratory of Hi-Tech Research for Intelligent Agricultural Equipment, Jiangsu University, Zhenjiang 212013, China)

  • Wei Liu

    (Jiangsu Provincial Key Laboratory of Hi-Tech Research for Intelligent Agricultural Equipment, Jiangsu University, Zhenjiang 212013, China)

Abstract

This study is situated against the background of shortages in the agricultural labor force and shortages of cultivated land. In order to improve the intelligence level and operational efficiency of agricultural machinery and solve the problems of difficulties in recognizing navigation lines and a lack of real-time performance of transplanters in the crop-free ridge environment, we propose a crop-free-ridge navigation line recognition method based on an improved YOLOv8 segmentation algorithm. First, this method reduces the parameters and computational complexity of the model by replacing the YOLOv8 backbone network with MobileNetV4 and the feature extraction module C2f with ShuffleNetV2, thereby improving the real-time segmentation of crop-free ridges. Second, we use the least-squares method to fit the obtained point set to accurately obtain navigation lines. Finally, the method is applied to testing and analyzing the field experimental ridges. The results showed that the average precision of the improved neural network model using this method was 90.4%, with a Params of 1.8 M, a FLOPs of 8.8 G, and an FPS of 49.5. The results indicate that the model maintains high accuracy while significantly outperforming Mask-RCNN, YOLACT++, YOLOv8, and YOLO11 in terms of computational speed. The detection frame rate increased significantly, improving the real-time performance of detection. This method uses the least-squares method to fit the 55% ridge contour feature points under the picture, and the fitting navigation line shows no large deviation compared with the image ridge centerline; the result is better than that of the RANSAC fitting method. The research results indicate that this method significantly reduces the size of the model parameters and improves the recognition speed, providing a more efficient solution for the autonomous navigation of intelligent carrier aircraft.

Suggested Citation

  • Runyi Lv & Jianping Hu & Tengfei Zhang & Xinxin Chen & Wei Liu, 2025. "Crop-Free-Ridge Navigation Line Recognition Based on the Lightweight Structure Improvement of YOLOv8," Agriculture, MDPI, vol. 15(9), pages 1-16, April.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:9:p:942-:d:1643069
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/9/942/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/15/9/942/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Shengyi Zhao & Yun Peng & Jizhan Liu & Shuo Wu, 2021. "Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module," Agriculture, MDPI, vol. 11(7), pages 1-15, July.
    3. Ranbing Yang & Yuming Zhai & Jian Zhang & Huan Zhang & Guangbo Tian & Jian Zhang & Peichen Huang & Lin Li, 2022. "Potato Visual Navigation Line Detection Based on Deep Learning and Feature Midpoint Adaptation," Agriculture, MDPI, vol. 12(9), pages 1-17, September.
    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. Jinkang Jiao & Ying Zang & Chaowen Chen, 2024. "Key Technologies of Intelligent Weeding for Vegetables: A Review," Agriculture, MDPI, vol. 14(8), pages 1-41, August.
    2. Yang Chen & Xiaoyulong Chen & Jianwu Lin & Renyong Pan & Tengbao Cao & Jitong Cai & Dianzhi Yu & Tomislav Cernava & Xin Zhang, 2022. "DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification," Agriculture, MDPI, vol. 12(12), pages 1-22, November.
    3. 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.
    4. Zhongyang Ma & Gang Wang & Jurong Yao & Dongyan Huang & Hewen Tan & Honglei Jia & Zhaobo Zou, 2023. "An Improved U-Net Model Based on Multi-Scale Input and Attention Mechanism: Application for Recognition of Chinese Cabbage and Weed," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    5. Jianwu Lin & Xiaoyulong Chen & Renyong Pan & Tengbao Cao & Jitong Cai & Yang Chen & Xishun Peng & Tomislav Cernava & Xin Zhang, 2022. "GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases," Agriculture, MDPI, vol. 12(6), pages 1-17, June.
    6. Liangliang Yang & Sota Kamata & Yohei Hoshino & Yufei Liu & Chiaki Tomioka, 2024. "Development of EV Crawler-Type Weeding Robot for Organic Onion," Agriculture, MDPI, vol. 15(1), pages 1-21, December.
    7. Yuan-Kai Tu & Chin-En Kuo & Shih-Lun Fang & Han-Wei Chen & Ming-Kun Chi & Min-Hwi Yao & Bo-Jein Kuo, 2022. "A 1D-SP-Net to Determine Early Drought Stress Status of Tomato ( Solanum lycopersicum ) with Imbalanced Vis/NIR Spectroscopy Data," Agriculture, MDPI, vol. 12(2), pages 1-17, February.
    8. Jiayou Shi & Yuhao Bai & Jun Zhou & Baohua Zhang, 2023. "Multi-Crop Navigation Line Extraction Based on Improved YOLO-v8 and Threshold-DBSCAN under Complex Agricultural Environments," Agriculture, MDPI, vol. 14(1), pages 1-22, December.
    9. Changguang Feng & Minlan Jiang & Qi Huang & Lingguo Zeng & Changjiang Zhang & Yulong Fan, 2022. "A Lightweight Real-Time Rice Blast Disease Segmentation Method Based on DFFANet," Agriculture, MDPI, vol. 12(10), pages 1-12, September.
    10. Tao Sun & Feixiang Le & Chen Cai & Yongkui Jin & Xinyu Xue & Longfei Cui, 2025. "Soybean–Corn Seedling Crop Row Detection for Agricultural Autonomous Navigation Based on GD-YOLOv10n-Seg," Agriculture, MDPI, vol. 15(7), pages 1-21, April.
    11. J. Dhakshayani & B. Surendiran, 2023. "M2F-Net: A Deep Learning-Based Multimodal Classification with High-Throughput Phenotyping for Identification of Overabundance of Fertilizers," Agriculture, MDPI, vol. 13(6), pages 1-19, June.
    12. Manoj A. Patil & Manohar Manur, 2023. "Sensitive crop leaf disease prediction based on computer vision techniques with handcrafted features," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(6), pages 2235-2266, December.
    13. Zahid Ullah & Najah Alsubaie & Mona Jamjoom & Samah H. Alajmani & Farrukh Saleem, 2023. "EffiMob-Net: A Deep Learning-Based Hybrid Model for Detection and Identification of Tomato Diseases Using Leaf Images," Agriculture, MDPI, vol. 13(3), pages 1-13, March.
    14. 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.
    15. Caichao Liu & Feng Wu & Fengwei Gu & Mingzhu Cao & Hongguang Yang & Lili Shi & Bokai Wang & Bing Wang, 2025. "Recent Research Progress on Key Technologies and Equipment for Mechanized Potato Harvesting," Agriculture, MDPI, vol. 15(7), pages 1-28, March.
    16. 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.
    17. Bharathwaaj Sundararaman & Siddhant Jagdev & Narendra Khatri, 2023. "Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato ( Solanum lycopersicum ) Disease Management for Global Food Security: A Comprehensive Review," Sustainability, MDPI, vol. 15(15), pages 1-23, July.

    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:9:p:942-:d:1643069. 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.