机器学习模型的比较,用于绘制戈阿的基于Arecanut的农林系统,通过提高精度和效率来提高精度和效率
A R Uthappa1, Bappa Das2,3, S B Chavan4
1ICAR-Central Coastal Agricultural Research Institute, Ela, Old Goa, 403402, India.
Scientific reports
|November 21, 2025
概括
机器学习使用Sentinel-2数据准确地绘制了戈阿的基于果的农林业. 梯度提升机 (GBM) 显示了最高的准确性,有助于沿海地区的土地利用规划和保护.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 环境科学 环境科学
背景情况:
- 农业林业对于减缓气候变化和农村生计至关重要,特别是在沿海地区.
- 传统的方法很难绘制复杂的农林系统,比如基于坚果的系统.
- 准确的地图绘制对于有效的土地利用规划和资源管理至关重要.
研究的目的:
- 鉴定和绘制印度戈阿的基于果的传统农业林业系统.
- 为了评估机器学习模型在农林测绘中的性能.
- 在本应用程序中评估 Sentinel-2 卫星数据的效率.
主要方法:
- 利用 Sentinel-2 卫星图像绘制地图,绘制了基于果的农业林业.
- 使用的机器学习模型:随机森林 (RF),支持矢量机 (SVM) 和梯度增强机 (GBM).
- 应用Boruta算法用于特征选择,以提高模型的准确性.
主要成果:
- 梯度提升机 (GBM) 实现了最高的精度 (0.86整体,0.83卡帕).
- 博鲁塔的分析确定了NDWI2,B3和SLAVI作为映射的关键变量.
- GBM绘制了58.64平方公里的农业林业;三种模型的平均值估计为戈阿的45.1平方公里.
结论:
- 机器学习算法,特别是GBM,对于绘制农林系统非常有效.
- 准确的地图绘制支持土地利用规划,资源保护和沿海管理.
- 未来使用高光谱传感器的研究可以进一步完善这些绘图技术.
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