开源无人机植株检测与性状提取工具，支持高效精准的植物表型分析。
MatchPlant: An Open-Source Pipeline for UAV-Based Single-Plant Detection and Data Extraction
- 模块化流程整合图像处理、标注、模型训练与空间投影
- 玉米试验中检测准确率90%以上，框投影匹配率达87.5%
- 适合农业育种与环境监测中的高通量表型研究
从无人机影像中精准识别单个植株对推进高通量表型分析和作物育种数据驱动决策至关重要。本文提出MatchPlant，一个模块化、基于图形界面、开源的Python流程，用于无人机植株检测与地理空间性状提取。该系统实现端到端工作流：集成无人机图像处理、用户引导标注、卷积神经网络目标检测模型训练、边界框正射投影至正射影像，并生成用于空间表型分析的shapefile。在早期玉米案例研究中，MatchPlant达到可靠检测性能（验证AP: 89.6%，测试AP: 85.9%），边界框投影覆盖89.8%的人工标注框，其中87.5%的投影交并比（IoU）大于0.5。预测框提取的性状值与人工标注高度一致（r = 0.87–0.97，IoU ≥ 0.4）。检测结果可跨时间点复用，用于提取株高与归一化植被指数，仅需少量额外标注，提升时序表型分析效率。通过模块化设计、可复现性与空间精度，MatchPlant为无人机植株级分析提供可扩展框架，适用于农业与环境监测。
原文摘要 · Abstract (English)
Accurate identification of individual plants from unmanned aerial vehicle (UAV) images is essential for advancing high-throughput phenotyping and supporting data-driven decision-making in plant breeding. This study presents MatchPlant, a modular, graphical user interface-supported, open-source Python pipeline for UAV-based single-plant detection and geospatial trait extraction. MatchPlant enables end-to-end workflows by integrating UAV image processing, user-guided annotation, Convolutional Neural Network model training for object detection, forward projection of bounding boxes onto an orthomosaic, and shapefile generation for spatial phenotypic analysis. In an early-season maize case study, MatchPlant achieved reliable detection performance (validation AP: 89.6%, test AP: 85.9%) and effectively projected bounding boxes, covering 89.8% of manually annotated boxes with 87.5% of projections achieving an Intersection over Union (IoU) greater than 0.5. Trait values extracted from predicted bounding instances showed high agreement with manual annotations (r = 0.87-0.97, IoU >= 0.4). Detection outputs were reused across time points to extract plant height and Normalized Difference Vegetation Index with minimal additional annotation, facilitating efficient temporal phenotyping. By combining modular design, reproducibility, and geospatial precision, MatchPlant offers a scalable framework for UAV-based plant-level analysis with broad applicability in agricultural and environmental monitoring.
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