Research Article | Open Access

Volume 2024 |Article ID 0188 | https://doi.org/10.34133/plantphenomics.0188

Detection and Identification of Tassel States at Different Maize Tasseling Stages Using UAV Imagery and Deep Learning

Jianjun Du ,^1,2,4 Jinrui Li,^1,2,3,4 Jiangchuan Fan,^1,2 Shenghao Gu,^1,2 Xinyu Guo ,^1,2 Chunjiang Zhao ^1,3

¹Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing100097, China
²Beijing Key Lab of Digital Plants, National Engineering Research Center for InformationTechnology in Agriculture, Beijing 100097, China
³College of Information Engineering, Northwest A&FUniversity, Yangling, Shanxi 712100 China
⁴These authors contributed equally to this work

Abstract

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.