A research team has developed an innovative method utilizing unmanned aerial vehicles (UAVs) and deep learning techniques to accurately identify tassel states in maize hybridization fields before and after manual detasseling. This approach significantly enhances tassel detection accuracy, achieving up to 98%, by using specific annotation and data augmentation strategies. This research holds significant value for improving tassel detection in agricultural fields, potentially reducing manual labor and increasing crop management efficiency through advanced UAV-based analysis systems.
Maize is one of China's most important crops, and monitoring the tasseling stage is crucial for maize breeding operations. Recent advances in UAV technology have made them valuable for detailed crop monitoring. However, the large amounts of image data generated pose significant processing challenges. Current methods using CNN-based deep learning frameworks for tassel detection face difficulties with data acquisition and labelling, and traditional image processing techniques have limited effectiveness.
A study (DOI: 10.34133/plantphenomics.0188) published in Plant Phenomics on 7 May 2024, aims to address these challenges by developing accurate detection models and annotated datasets for the dynamic growth stages of maize tassels.
