用无人机航拍数据预测甘蔗产量，准确率高达95%。
Evaluating Sugarcane Yield Variability with UAV-Derived Cane Height under Different Water and Nitrogen Conditions
- 通过无人机获取的高程数据，计算每块地的甘蔗高度。
- 不同水肥条件下，甘蔗高度与实际产量相关性达0.95。
- 适合农业遥感、精准种植研究者参考。
本研究基于无人机获取的甘蔗试验田预收获数字表面模型（DSM），分析不同水分和氮肥条件下甘蔗产量与甘蔗高度的关系。试验田分为62个地块，按三种水分水平（低、中、高）和三种氮肥水平（低、中、高）组合处理并重复。每个地块的DSM像素分布呈现双峰特征，分别代表地面间隙和冠层顶部。通过截尾均值法提取冠层平均高度与基底平均高程，两者差值生成甘蔗高度模型（DCHM）。每块地收获后记录产量（吨/英亩）。将数据按九种处理组合聚合，计算各组的DCHM与中位产量。回归分析显示，DCHM与产量间R²为0.95。结果表明，利用单次无人机航拍数据可有效预测甘蔗产量，且水肥管理对甘蔗高度和产量影响显著。
原文摘要 · Abstract (English)
This study investigates the relationship between sugarcane yield and cane height derived under different water and nitrogen conditions from pre-harvest Digital Surface Model (DSM) obtained via Unmanned Aerial Vehicle (UAV) flights over a sugarcane test farm. The farm was divided into 62 blocks based on three water levels (low, medium, and high) and three nitrogen levels (low, medium, and high), with repeated treatments. In pixel distribution of DSM for each block, it provided bimodal distribution representing two peaks, ground level (gaps within canopies) and top of the canopies respectively. Using bimodal distribution, mean cane height was extracted for each block by applying a trimmed mean to the pixel distribution, focusing on the top canopy points. Similarly, the extracted mean elevation of the base was derived from the bottom points, representing ground level. The Derived Cane Height Model (DCHM) was generated by taking the difference between the mean canopy height and mean base elevation for each block. Yield measurements (tons/acre) were recorded post-harvest for each block. By aggregating the data into nine treatment zones (e.g., high water-low nitrogen, low water-high nitrogen), the DCHM and median yield were calculated for each zone. The regression analysis between the DCHM and corresponding yields for the different treatment zones yielded an R 2 of 0.95. This study demonstrates the significant impact of water and nitrogen treatments on sugarcane height and yield, utilizing one-time UAV-derived DSM data.
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