在干旱地区使用无人机多谱成像和堆叠合奏学习来预测麦产量
Linqiang Deng1, Yaoyu Li1,2, Xifeng Liu1
1College of Software, Shanxi Agricultural University, Jinzhong, China.
Frontiers in plant science
|October 27, 2025
概括
准确的果产量预测对于粮食安全至关重要. 这项研究使用无人机多谱数据和机器学习,确定连接阶段是干旱地区精确作物管理的最佳阶段.
科学领域:
- 农业学是一种农业学.
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 干旱和气候波动对果产量稳定性构成挑战.
- 准确,空间显式的产量预测对于精准农业和粮食安全至关重要.
研究的目的:
- 开发一个"光谱-气象-空间"框架,用于果产量预测.
- 评估机器学习算法的有效性,并确定最佳监测阶段.
- 为干旱环境中精密管理提供技术支持.
主要方法:
- 利用DJI Mavic 3M无人机和气象数据的多光谱图像.
- 在的主要生长阶段收集的数据:幼苗的出现,结合,开花和成熟.
- 开发了一个3D预测框架,使用八个机器学习算法,使用SHAP值进行变量重要性分析.
主要成果:
- 集体学习模型,特别是梯度提升 (R2 = 0.9491),表现出卓越的性能.
- DVI和NDGI光谱指数是关键预测指标,连接阶段的准确性最高 (R2 = 0.9454).
- 产量预测范围为4,291至4,965公斤1,显示中度正空间自相关.
结论:
- 将无人机多谱数据与机器学习集成,可提供高效的产预测.
- 关节生长阶段是监测和预测准确性的最佳阶段.
- 这种方法支持干旱地区的精密种植和高效的管理.
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