智慧农业（中英文）2025，Vol.7Issue(6)：96-110,15.DOI:10.12133/j.smartag.SA202505021
LightTassel-YOLO:一种基于无人机遥感的玉米雄穗实时检测方法
LightTassel-YOLO:A Real-Time Detection Method for Maize Tassels Based on UAV Remote Sensing
摘要
Abstract
[Objective]The accurate identification of maize tassels is critical for the production of hybrid seed.Existing ob-ject detection models in complex farmland scenarios face limitations such as restricted data diversity,insufficient feature extraction,high computational load,and low detection efficiency.To address these challenges,a real-time field maize tas-sel detection model,LightTassel-YOLO(You Only Look Once)based on an improved YOLOv11n is proposed.The model is designed to quickly and accurately identify maize tassels,enabling efficient operation of detasseling unmanned aerial ve-hicles(UAVs)and reducing the impact of manual intervention.[Methods]Data was continuously collected during the tas-seling stage of maize from 2023 to 2024 using UAVs,establishing a large-scale,high-quality maize tassel dataset that cov-ered different maize tasseling stages,multiple varieties,varying altitudes,and diverse meteorological conditions.First,Ef-ficientViT(Efficient vision transformer)was applied as the backbone network to enhance the ability to perceive informa-tion across multi-scale features.Second,the C2PSA-CPCA(Convolutional block with parallel spatial attention with chan-nel prior convolutional attention)module was designed to dynamically assign attention weights to the channel and spatial dimensions of feature maps,effectively enhancing the network's capability to extract target features while reducing compu-tational complexity.Finally,the C3k2-SCConv module was constructed to facilitate representative feature learning and achieve low-cost spatial feature reconstruction,thereby improving the model's detection accuracy.[Results and Discus-sions]The results demonstrated that LightTassel-YOLO provided a reliable method for maize tassel detection.The final model achieved an accuracy of 92.6%,a recall of 89.1%,and an AP@0.5 of 94.7%,representing improvements of 2.5,3.8 and 4.0 percentage points over the baseline model YOLOv11n,respectively.The model had only 3.23 M parameters and a computational cost of 6.7 GFLOPs.In addition,LightTassel-YOLO was compared with mainstream object detection algo-rithms such as Faster R-CNN,SSD,and multiple versions of the YOLO series.The results demonstrated that the proposed method outperformed these algorithms in overall performance and exhibits excellent adaptability in typical field scenarios.[Conclusions]The proposed method provides an effective theoretical framework for precise maize tassel monitoring and holds significant potential for advancing intelligent field management practices.关键词
玉米雄穗检测/YOLOv11/EfficientViT/CPCA/SCConv/无人机Key words
maize tassel detection/YOLOv11/EfficientViT/CPCA/SCConv/UAV分类
农业科技引用本文复制引用
CAO Yuying,LIU Yinchuan,GAO Xinyue,JIA Yinjiang,DONG Shoutian..LightTassel-YOLO:一种基于无人机遥感的玉米雄穗实时检测方法[J].智慧农业（中英文）,2025,7(6):96-110,15.基金项目
Science and Technology Innovation 2030 of New Generation of Artificial Intelligence Major Project(2021ZD0110904) （2021ZD0110904）
Unveiling projects of Heilongjiang Province(20212XJ05A0201) 国家科技创新2030"新一代人工智能"重大项目(2021ZD0110904) （20212XJ05A0201）
黑龙江省"揭榜挂帅"科技攻关项目(20212XJ05A0201) （20212XJ05A0201）
