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Quantifying tree-level peach flowering dynamics using UAV imagery and an optimized instance segmentation model

文献类型： 外文期刊

作者： Gu, Qing 1 ; Cheng, Jiayu 1 ; Zhang, Minghao 2 ; Li, Xiongwei 2 ; Jackson, Robert 3 ; Ju, Lei 3 ; Lou, Weidong 1 ; Chen, Miaojin 4 ; Zhou, Ji 3 ; Zhang, Xiaobin 1 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Peoples R China

2.Shanghai Acad Agr Sci, Forest & Fruit Tree Inst, Peach Res Dept, Shanghai 201403, Peoples R China

3.Natl Inst Agr Bot NIAB, Crop Sci Ctr CSC, Data Sci, Cambridge CB3 0LE, England

4.Fenghua Peach Res Inst, Ningbo 315502, Peoples R China

关键词： Peach flowering; Phenotype; Unmanned aerial vehicle; Fruit breeding; Instance segmentation

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 237 卷

页码：

收录情况： SCI

摘要： A timely and accurate assessment of flowering characteristics is vital for tracking floral phenology in agricultural management and peach breeding. In this study, an unmanned aerial vehicle (UAV) equipped with a highresolution camera was integrated with deep learning techniques to monitor peach flowering across multiple varieties. An instance segmentation model, PeFloSEG, was proposed for the accurate detection of peach flowers and buds. Based on the YOLOv5-seg framework, PeFloSEG integrates an enhanced detection head for improved feature representation and a modified loss function-Focal Efficient Intersection over Union (Focal-EIoU)-to optimize bounding box regression. To boost model efficiency, a network slimming algorithm was applied, significantly reducing model size while maintaining high accuracy. PeFloSEG achieved strong results, with mean average precision (mAP@0.5) scores of 0.876 for detection and 0.825 for segmentation, outperforming state-ofthe-art algorithms by 0.5 %-24.9 % in detection and 5.7 %-28.1 % in segmentation. Three flowering indices derived from PeFloSEG outputs-flowering intensity (FI) indices (FI1 and FI2) and single-tree flowering ratio (SFR)-were evaluated. Linear correlation analysis revealed strong relationships between these indices and ground truth values, with R2 values of 0.964 (FI1), 0.961 (FI2), and 0.986 (SFR). These indices were further used to assess flowering dynamics over time and to distinguish phenological stages, achieving an overall classification accuracy of 91.7 %. They also enabled effective variety classification, facilitating the exploration of flowering characteristics across different peach varieties. Overall, the proposed approach offers a scalable and efficient solution for high-throughput phenotyping and provides valuable tools for peach breeding and germplasm resource evaluation.

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