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开发使用机器学习和深度学习技术的多阶段作物产量估计模型

K S Aravind¹, Ananta Vashisth², P Krishnan¹

¹Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.

International journal of biometeorology

|December 6, 2024

概括

机器学习模型使用气象数据准确估计了旁遮普邦的小麦产量. 随机森林,支持向量回归和深度神经网络显示了区级作物预测的有希望的结果.

科学领域:

农业科学农业科学
数据科学数据科学数据科学
气象学天气学

背景情况:

准确的小麦产量估计对于粮食安全和农业规划至关重要.
气象因素显著影响作物生产率,需要数据驱动的方法.

研究的目的:

应用和比较机器学习技术用于多变量气象时间序列数据分析.
为了估计在Punjab的五个地区在不同作物生长阶段的小麦产量.

主要方法:

利用了34年的小麦产量和天气数据.
使用逐步多线性回归 (SMLR),人工神经网络 (ANN),支持向量回归 (SVR),随机森林 (RF) 和深度神经网络 (DNN) 开发和验证了模型.
包含特定周的气象变量,对应于耕种,开花和谷物填充阶段.

主要成果:

随机森林 (RF),支持向量回归 (SVR) 和深度神经网络 (DNN) 模型表现出一致和有前途的性能.
模型在验证过程中实现了总体平均绝对百分比误差 (MAPE) 和正常化根平均平方误差 (nRMSE) 低于6%.
对于法里德科特,费罗兹普尔和古尔达斯普尔地区,RF,SVR和DNN模型显示出卓越的验证性能.

结论:

射频,SVR和DNN模型对于区级小麦产量估计非常有效.
与SVR和DNN相比,随机森林模型表现出更高的准确性.
这些机器学习方法为不同作物生长阶段的农业预测提供了可靠的工具.

关键词:

人工神经网络的人工神经网络深度神经网络是一个神经网络.机器学习机器学习随机的森林随机的森林一步一步的多线性回归.支持矢量回归的支持矢量回归天气变量的天气变量.收益率估计收益率估计

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