用无人机影像自动数玉米苗，又快又准。
MaizeStandCounting (MaSC): Automated and Accurate Maize Stand Counting from UAV Imagery Using Image Processing and Deep Learning
- 分块或视频帧处理，用轻量YOLOv9检测玉米苗
- 视频帧模式准确率R²达0.906，接近人工水平
- 适合农业科研与生产中快速部署使用
精确的玉米苗株数对作物管理与研究至关重要，可支持产量预测、播种密度优化及出苗问题早期发现。人工计数耗时费力且易出错，尤其在大面积或地形复杂的田块。本文提出MaizeStandCounting（MaSC），一种基于低成本无人机采集的RGB影像、在廉价硬件上运行的自动化玉米苗计数算法。MaSC采用两种模式：（1）将拼接图像分割为小块，（2）对原始视频帧利用单应性矩阵对齐。两种模式均使用轻量级YOLOv9模型，训练覆盖V2-V10生长阶段的玉米苗检测。算法能区分玉米与杂草，通过检测点的空间分布进行行与区间分割，实现精准的逐行株数统计。与2024年夏季育种田实地人工计数对比，拼接图模式相关系数R²=0.616，原始帧模式R²=0.906。系统处理83张全分辨率图像仅需60.63秒，包含推理与后处理，具备实时潜力。结果表明，MaSC是一种可扩展、低成本、高精度的自动化玉米苗计数工具，适用于科研与生产场景。
原文摘要 · Abstract (English)
Accurate maize stand counts are essential for crop management and research, informing yield prediction, planting density optimization, and early detection of germination issues. Manual counting is labor-intensive, slow, and error-prone, especially across large or variable fields. We present MaizeStandCounting (MaSC), a robust algorithm for automated maize seedling stand counting from RGB imagery captured by low-cost UAVs and processed on affordable hardware. MaSC operates in two modes: (1) mosaic images divided into patches, and (2) raw video frames aligned using homography matrices. Both modes use a lightweight YOLOv9 model trained to detect maize seedlings from V2-V10 growth stages. MaSC distinguishes maize from weeds and other vegetation, then performs row and range segmentation based on the spatial distribution of detections to produce precise row-wise stand counts. Evaluation against in-field manual counts from our 2024 summer nursery showed strong agreement with ground truth (R^2= 0.616 for mosaics, R^2 = 0.906 for raw frames). MaSC processed 83 full-resolution frames in 60.63 s, including inference and post-processing, highlighting its potential for real-time operation. These results demonstrate MaSC's effectiveness as a scalable, low-cost, and accurate tool for automated maize stand counting in both research and production environments.
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