无人机多谱传感和数据驱动建模,用于精确预测洋产量
Sagar M Wayal1, Shardul Parab2, Anusha Raj1
1ICAR-Directorate of Onion and Garlic Research, Pune, India.
Frontiers in plant science
|February 23, 2026
概括
无人机 (UAV) 多光谱图像与机器学习相结合,准确地预测洋产量. 随机森林模型在优化精准农业和作物管理方面表现最好.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 精准农业从无人机辅助遥感与物联网 (IoT) 和万物互联网 (IoE) 的整合中受益.
- 捕捉作物生长的时空变化对于优化农业实践至关重要.
- 基于无人机的多光谱图像为监测作物健康和预测产量提供了强大的工具.
研究的目的:
- 用无人机基于多光谱图像来预测雨季洋作物的灯泡产量.
- 评估各种机器学习算法用于洋产量预测的性能.
- 评估来自多光谱数据的植被指数对产量建模的有用性.
主要方法:
- 在关键增长阶段从无人机中获取天花板反射率马赛克.
- 植物指数 (VIs) 的提取,包括NDVI,NDRE,SAVI,LAI,NORM2和GNDVI.
- 使用五种机器学习算法 (线性回归,随机森林,支持矢量机器,梯度增强,弹性净回归) 开发和评估产量预测模型,并进行十倍交叉验证.
主要成果:
- 随机森林始终优于其他模型,在灯泡开发阶段实现高精度 (验证R2 = 0.755).
- 支持向量机也表现出强大的预测能力 (验证R2 = 0.716).
- 观察到模型性能的年间变化,在2024年数据上训练的模型显示出比2023年更好的结果.
结论:
- 无人机衍生的多谱传感与机器学习相结合,是一种有效和可扩展的方法,可用于可靠的洋产量预测.
- 这种方法提供了及时的决策支持,用于在不同的农业条件下管理雨季洋作物.
- 该研究强调了先进的遥感和人工智能技术在现代农业中的潜力.
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