数字农科院2.0

YOLO-light-pruned: A lightweight model for monitoring maize seedling count and leaf age using near-ground and UAV RGB images

文献类型： 外文期刊

作者： Tiantian Jiang;Liang Li;Zhen Zhang;Xun Yu;Yanqin Zhu;Liming Li;Yadong Liu;YaliBai; Ziqian Tang ; Shuaibing Liu;Yan Zhang;Zheng Duan;Dameng Yin;Xiuliang Jin

关键词： Maize seedling monitoring; Leaf age; Plant counting; RGB images; Deep learning; UAV

期刊名称： Artificial Intelligence in Agriculture

ISSN： 2097-2113

年卷期： 2025 年

页码：

收录情况： SCIE（2025版） ; ; EI（2025版） ; ; CSCD（2025-2026年度） ; ; 农林核心（2024版）

摘要： Maize seedling count and leaf age are critical indicators of early growth status, essential for effective field management and breeding variety selection. Traditional field monitoring methods are time-consuming, labor intensive, and prone to subjective errors. Recently, deep learning-based object detection models have gained attention in crop seedling counting. However, many of these models exhibit high computational complexity and implementation costs, making field deployment challenging. Moreover, maize leaf age monitoring in field environments is barely investigated. Therefore, this study proposes two lightweight models, YOLOv8n-Light Pruned (YOLOv8n-LP) and YOLOv11n-Light-Pruned (YOLOv11n-LP), for monitoring maize seedling count and leaf age in field RGB images. Our proposed models are improved from YOLOv8n and YOLOv11n by incorporating the DAttention mechanism, an improved BiFPN, an EfficientHead, and layer-adaptive magnitude-based pruning. The improvement in model complexity and model efficiency was significant, with the number of parameters reduced by over 73 % and model efficiency upgraded by up to 42.9 % depending on the device computation power. High accuracy was achieved in seedling counting (YOLOv8n-LP/ YOLOv11n-LP: AP = 0.968/0.969, R2 = 0.91/ 0.94, rRMSE = 6.73 %/5.59 %), with significantly reduced model size (YOLOv8n-LP/ YOLOv11n-LP: parameters = 0.8 M/0.7 M, trained model size = 1.8 MB/1.7 MB). The robustness was validated across datasets with varying leaf ages (rRMSE = 4.07 % – 7.27 %), resolutions (rRMSE = 3.06 % – 6.28 %), seedling compositions (rRMSE = 1.09 % – 9.29 %), and planting densities (rRMSE = 3.38 % – 10.82 %). Finally, by integrating plant counting and leaf age estimation, the proposed models demonstrated high accuracy in leaf age detection using near-ground images (YOLOv8n-LP/ YOLOv11n-LP: rRMSE = 5.73 %/7.54 %) and UAV images (rRMSE = 9.24 %/14.44 %). The results demonstrate that the proposed models excel in detection accuracy, deployment efficiency, and adaptability to complex field environments, providing robust support for practical applications in precision agriculture.

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