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Bud-YOLO: A Real-Time Accurate Detection Method of Cotton Top Buds in Cotton Fields

Author

Listed:
  • Xuening Zhang

    (College of Information Engineering, Tarim University, Alaer 843300, China)

  • Liping Chen

    (College of Information Engineering, Tarim University, Alaer 843300, China
    Key Laboratory of Tarim Oasis Agriculture, Tarim University, Ministry of Education, Alaer 843300, China
    Key Laboratory of Modern Agricultural Engineering, Tarim University, Alaer 843300, China)

Abstract

Cotton topping plays a crucial and indispensable role in controlling excessive growth and enhancing cotton production. This study aims to improve the operational efficiency and accuracy of cotton topping robots through a real-time and accurate cotton top bud detection algorithm tailored for field operation scenarios. We propose a lightweight structure based on YOLOv8n, replacing the C2f module with the Cross-Stage Partial Networks and Partial Convolution (CSPPC) module to minimize redundant computations and memory access. The network’s neck employs an Efficient Reparameterized Generalized-FPN (Efficient RepGFPN) to achieve high-precision detection without substantially increasing computational cost. Additionally, the loss calculation of the optimized prediction frame was addressed with the Inner CIoU loss function, thereby enhancing the precision of the model’s prediction box. Comparison experiments indicate that the Bud-YOLO model is highly effective for detecting cotton top buds, with an AP50 of 99.2%. This performance surpasses that of other YOLO variants, such as YOLOv5s and YOLOv10n, as well as the conventional Faster R-CNN model. Moreover, the Bud-YOLO model exhibits robust performance across various angles, occlusion conditions, and bud morphologies. This study offers technical insights to support the migration and deployment of the model on cotton topping machinery.

Suggested Citation

  • Xuening Zhang & Liping Chen, 2024. "Bud-YOLO: A Real-Time Accurate Detection Method of Cotton Top Buds in Cotton Fields," Agriculture, MDPI, vol. 14(9), pages 1-17, September.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:9:p:1651-:d:1482341
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