摘要
The tassel state in maize hybridization fields not only reflects the growth stage of the maize but also reflects the performance of the detasseling operation.Existing tassel detection models are primarily used to identify mature tassels with obvious features,making it difficult to accurately identify small tassels or detasseled plants.This study presents a novel approach that utilizes unmanned aerial vehicles(UAVs)and deep learning techniques to accurately identify and assess tassel states,before and after manually detasseling in maize hybridization fields.The proposed method suggests that a specific tassel annotation and data augmentation strategy is valuable for substantial enhancing the quality of the tassel training data.This study also evaluates mainstream object detection models and proposes a series of highly accurate tassel detection models based on tassel categories with strong data adaptability.In addition,a strategy for blocking large UAV images,as well as improving tassel detection accuracy,is proposed to balance UAV image acquisition and computational cost.The experimental results demonstrate that the proposed method can accurately identify and classify tassels at various stages of detasseling.The tassel detection model optimized with the enhanced data achieves an average precision of 94.5%across all categories.An optimal model combination that uses blocking strategies for different development stages can improve the tassel detection accuracy to 98%.This could be useful in addressing the issue of missed tassel detections in maize hybridization fields.The data annotation strategy and image blocking strategy may also have broad applications in object detection and recognition in other agricultural scenarios.
基金
supported by the National Key Research and Development Program(2022YFD1900701)
the Heilongjiang Province“Enlisting and Leading”Science and Technology Research Projects(20212XJ05A02)
the Construction of Colaborative Innovation Center of Beijing Academy of Agriculture and Forestry Science(KJCX20230429)
the National Natural Science Foundation of China(U21A20205).
作者简介
Address correspondence to:Jianjun Du,dujianjun18@126.com,contributed equally to this work;Address correspondence to:Xinyu Guo,guoxy73@163.com;Address correspondence to:Chunjiang Zhao,zhaocj@nercita.org.cn;Jinrui Li,contributed equally to this work
