ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Research on Cabbage Transplanting Status Detection and Operation Quality Evaluation in Complex Environments Based on Improved YOLOv10-TQ and DeepSort
Provisionally accepted
- 1Beijing Information Science and Technology University, Beijing, China
- 2National Engineering Research Center for Information Technology in Agriculture, Beijing, China
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The quality of crop transplanting is a critical factor influencing both plant survival and final yield. To address the limitations of low manual inspection efficiency, poor environmental adaptability of traditional algorithms, and insufficient detection accuracy in mechanized transplanting operations, this study proposes a "detection and tracking" - based method for recognizing and counting cabbage transplanting states in open-field scenarios. The method enables accurate identification and robust tracking of seedling transplanting conditions. Firstly, an improved YOLOv10-TQ detection network is developed by integrating a triplet attention mechanism and a combined QFocal Loss-cross entropy loss function, aiming to enhance the detection accuracy and stability for three transplanting status of cabbage: normal, soil-buried seedlings, and bare-root seedlings. Then, a lightweight MobileViT feature extraction network is incorporated into the DeepSort algorithm to improve fine-grained target representation. Combined with a line-crossing counting strategy, this approach enables identity de-duplication and robust counting performance. Experimental results demonstrate that the proposed method achieves a mean average precision (mAP) of 86.3% and an average counting accuracy of 97.8% on a self-constructed cabbage transplanting dataset. Based on the study, a visualization system for monitoring cabbage transplanting status was designed, aiming to enhance precision in agricultural practices. Compared to traditional detection and counting methods, the proposed approach exhibits significant advantages in detection accuracy, tracking stability, and counting precision. This provides a promising technical foundation for intelligent quality evaluation of cabbage transplanting operations and data-driven decision-making in agricultural machinery systems.
Keywords: YOLO, DeepSORT, Cabbage, Transplanting, quality assessment
Received: 16 Oct 2025; Accepted: 14 Nov 2025.
Copyright: © 2025 Wang, Liu, Geng, Zhu, Chen and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Wenbai Chen, chenwb@bistu.edu.cn
Chunjiang Zhao, zhaocj@nercita.org.cn
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