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YOLO-WAS: A Lightweight Apple Target Detection Method Based on Improved YOLO11

Author

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  • Xinwu Du

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
    Longmen Laboratory, Luoyang 471000, China)

  • Xiaoxuan Zhang

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China)

  • Tingting Li

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China)

  • Xiangyu Chen

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China)

  • Xiufang Yu

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China)

  • Heng Wang

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
    Longmen Laboratory, Luoyang 471000, China)

Abstract

Target detection is the key technology of the apple-picking robot. To overcome the limitations of existing apple target detection methods, including low recognition accuracy of multi-species apples in complex orchard environments and a complex network architecture that occupies large memory, a lightweight apple recognition model based on the improved YOLO11 model was proposed, named YOLO-WAS model. The model aims to achieve efficient and accurate automatic multi-species apple identification while reducing computational resource consumption and facilitating real-time applications on low-power devices. First, the study constructed a high-quality multi-species apple dataset and improved the complexity and diversity of the dataset through various data enhancement techniques. The YOLO-WAS model replaced the ordinary convolution module of YOLO11 with the Adown module proposed in YOLOv9, the backbone C3K2 module combined with Wavelet Transform Convolution (WTConv), and the spatial and channel synergistic attention module Self-Calibrated Spatial Attention (SCSA) combined with the C2PSA attention mechanism to form the C2PSA_SCSA module was also introduced. Through these improvements, the model not only ensured lightweight but also significantly improved performance. Experimental results show that the proposed YOLO-WAS model achieves a precision (P) of 0.958, a recall (R) of 0.921, and mean average precision at IoU threshold of 0.5 (mAP@50) of 0.970 and mean average precision from IoU threshold of 0.5 to 0.95 with step 0.05 (mAP@50:95) of 0.835. Compared to the baseline model, the YOLO-WAS exhibits reduced computational complexity, with the number of parameters and floating-point operations decreased by 22.8% and 20.6%, respectively. These results demonstrate that the model performs competitively in apple detection tasks and holds potential to meet real-time detection requirements in resource-constrained environments, thereby contributing to the advancement of automated orchard management.

Suggested Citation

  • Xinwu Du & Xiaoxuan Zhang & Tingting Li & Xiangyu Chen & Xiufang Yu & Heng Wang, 2025. "YOLO-WAS: A Lightweight Apple Target Detection Method Based on Improved YOLO11," Agriculture, MDPI, vol. 15(14), pages 1-19, July.
  • Handle: RePEc:gam:jagris:v:15:y:2025:i:14:p:1521-:d:1701544
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    References listed on IDEAS

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    1. Chenglin Wang & Weiyu Pan & Tianlong Zou & Chunjiang Li & Qiyu Han & Haoming Wang & Jing Yang & Xiangjun Zou, 2024. "A Review of Perception Technologies for Berry Fruit-Picking Robots: Advantages, Disadvantages, Challenges, and Prospects," Agriculture, MDPI, vol. 14(8), pages 1-42, August.
    2. Liusong Yang & Tian Zhang & Shihan Zhou & Jingtan Guo, 2025. "AAB-YOLO: An Improved YOLOv11 Network for Apple Detection in Natural Environments," Agriculture, MDPI, vol. 15(8), pages 1-25, April.
    3. Ji Wei & Ding Yi & Xu Bo & Chen Guangyu & Zhao Dean, 2020. "Adaptive Variable Parameter Impedance Control for Apple Harvesting Robot Compliant Picking," Complexity, Hindawi, vol. 2020, pages 1-15, April.
    4. Yangtian Lin & Yujun Xia & Pengcheng Xia & Zhengyang Liu & Haodi Wang & Chengjin Qin & Liang Gong & Chengliang Liu, 2025. "YOLO11-ARAF: An Accurate and Lightweight Method for Apple Detection in Real-World Complex Orchard Environments," Agriculture, MDPI, vol. 15(10), pages 1-24, May.
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    Cited by:

    1. Zohaib Khan & Yue Shen & Hui Liu, 2025. "ObjectDetection in Agriculture: A Comprehensive Review of Methods, Applications, Challenges, and Future Directions," Agriculture, MDPI, vol. 15(13), pages 1-36, June.

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