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整合远程传感和土壤特征,用于基于机器学习的增强型玉米产量预测,在美国南部

Sayantan Sarkar¹, Javier M Osorio Leyton¹, Efrain Noa-Yarasca¹

¹Texas A&M AgriLife Blackland Research and Extension Center, Temple, TX 76502, USA.

Sensors (Basel, Switzerland)

|January 25, 2025

概括

使用机器学习模型,可以在生产领域准确预测玉米产量. 将V14/VT生长阶段的土壤特性和植被指数与随机森林模型相结合,为农民提供了最好的结果.

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:

玉米产量预测对于农业管理和品种选择至关重要.
以前的研究往往缺乏现实世界的适用性,专注于较小或受控制的领域.
这项研究涉及产量预测在生产规模,雨水养环境.

研究的目的:

为了确定玉米产量预测的最佳植被指数和非生物因素.
通过机器学习来确定玉米生长最有效的阶段,用于产量估计.
评估不同机器学习模型用于玉米产量预测的性能.

主要方法:

使用高分辨率 (6厘米) 空中多光谱图像.
在七个玉米生长阶段 (V4-V14/VT) 获得了62个预测指标,包括土壤特性,斜率,光谱带和植被指数 (例如GNDRE,NDRE,TGI).
评估了四种机器学习算法:线性回归,随机森林,极端梯度增强和梯度增强回归器.

主要成果:

在V14/VT生长阶段的随机森林模型获得了最高的准确性 (RMSE为0.52 Mg/ha).
在V6阶段估计收益率也被发现是可行的.
无生物因素 (斜率,土壤特性) 和特定植被指数 (TGI,HUE,GNDRE) 的整合显著提高了预测准确性.

结论:

机器学习模型,特别是随机森林,结合非生物因素和植被指数,可以准确地预测生产规模的玉米产量.
早期季节产量估计是可行的,帮助农民和作物顾问进行规划和决策.
这些发现支持通过改善产量预测来提高农场的利能力和可持续性.

关键词:

玉米玉米玉米玉米玉米整体方法整体方法机器学习是机器学习.玉米玉米玉米是一种植被指数植被指数收益率预测收益率预测

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