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使用Sentinel-2卫星图像和机器学习算法来预测热带牧场料质量,原始蛋白质和纤维含量

Marcia Helena Machado da Rocha Fernandes¹, Jalme de Souza FernandesJunior², Jordan Melissa Adams³

¹Department of Animal Science, Sao Paulo State University (UNESP), Campus Jaboticabal, Jaboticabal, 14884-900, Brazil. marcia.fernandes@unesp.br.

Scientific reports

|April 15, 2024

概括

这项研究表明,Sentinel-2卫星图像和机器学习如何估计热带牧场料质量 (FM) 和营养含量. 这些方法支持精准养和有效的牧场管理,以实现可持续的畜牧生产.

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:

草原对全球牲畜至关重要,为反动物提供必要的料.
有效的牧场管理需要对牧场数量和营养质量进行精确的监测.

研究的目的:

评估Sentinel-2卫星图像和机器学习的潜力,以估计热带牧场的料质量 (FM),原蛋白 (CP) 和纤维含量.
开发和比较支持向量回归 (SVR) 和随机森林 (RF) 模型用于牧场营养分析.

主要方法:

作为输入特征,利用了Sentinel-2卫星数据,气象信息,光谱反射率和植被指数 (VI).
开发了SVR和RF机器学习模型,使用Marandu木草牧场 (2016-2020) 的现场数据.
评估模型性能用于估计FM,CP和纤维含量.

主要成果:

一般来说,SVR模型的性能略高于射频模型.
最好的料质量 (FM) 预测结合了植被指数 (VI) 和气象数据.
最佳的原始蛋白质 (CP) 和纤维含量估计利用光谱带和气象数据,实现R2值分别为0.66和0.57.

结论:

卫星遥感与机器学习相结合,为监测热带牧场质量提供了一个有希望的方法.
这些发现可以提高精确养技术和决策支持系统,以优化放牧管理.
该研究强调了整合光谱和气象数据用于牧场评估的实用性.

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