用机器学习模型评估小麦作物水应激指数
Aditi Yadav1, Likith Muni Narakala1, Hitesh Upreti1
1Department of Civil Engineering, Shiv Nadar Institution of Eminence, Greater Noida, UP, India.
Environmental monitoring and assessment
|September 23, 2024
概括
机器学习模型准确地预测小麦作物的作物水压力指数 (CWSI),帮助安排灌. 支持向量回归 (SVR) 在这个半干旱地区的研究中显示出最高的准确性.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 农作物水压力指数 (CWSI) 对于优化灌和节约农业用水至关重要.
- 准确的CWSI确定对于有效的灌计划至关重要,特别是在缺水的半干旱地区.
研究的目的:
- 评估四种机器学习 (ML) 模型在预测小麦作物水应激指数 (CWSI) 的有效性.
- 确定最佳的输入变量和ML模型,以便在半干旱环境中准确预测CWSI.
主要方法:
- 在两个季节的不同灌处理下对小麦作物进行CWSI的实证确定.
- 支持矢量回归 (SVR),随机森林回归 (RFR),人工神经网络 (ANN) 和多线性回归 (MLR) 模型的开发和评估.
- 使用R2,MAE和RMSE与气象和土壤湿度数据的各种组合评估模型性能.
主要成果:
- 支向量回归 (SVR) 的表现优于ANN,RFR和MLR,实现了最高的精度 (R2 = 0.997) 输入包括天花板温度,空气温度,蒸汽压力赤字,净太阳辐射和风速.
- 当土壤水分耗尽作为输入时,ANN和MLR模型也显示出高的预测能力.
- 随着天花板温度,空气温度,蒸汽压力赤字和风速的结合,RFR表现最佳.
结论:
- 机器学习模型,特别是SVR,显示出在小麦种植中准确预测CWSI的显著前景.
- 这些模型可以集成到灌决策支持系统 (IDSS) 中,以加强作物压力管理和促进有效的农业用水.
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