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公开(公告)号：US20050234691A1
公开(公告)日：2005-10-20
申请号：US11108674
申请日：2005-04-19
申请人： Ramesh Singh , Anup Prasad , Vinod Tare , Menas Kafatos
发明人： Ramesh Singh , Anup Prasad , Vinod Tare , Menas Kafatos
CPC分类号： G06Q10/04 , Y02A40/232
摘要： Crop yield may be assessed and predicted using a piecewise linear regression method with break point and various weather and agricultural parameters, such as NDVI, surface parameters (soil moisture and surface temperature) and rainfall data. These parameters may help aid in estimating and predicting crop conditions. The overall crop production environment can include inherent sources of heterogeneity and their nonlinear behavior. A non-linear multivariate optimization method may be used to derive an empirical crop yield prediction equation. Quasi-Newton method may be used in optimization for minimizing inconsistencies and errors in yield prediction. Minimization of least square loss function through iterative convergence of pre-defined empirical equation can be based on piecewise linear regression method with break point. This non-linear method can achieve acceptable lower residual values with predicted values very close to the observed values. The present invention can be modified and tailored for different crops worldwide.
摘要翻译： 可以使用具有断点和各种天气和农业参数（如NDVI，表面参数（土壤水分和表面温度））和降雨数据的分段线性回归方法来评估和预测作物产量。 这些参数可能有助于估计和预测作物条件。 整个作物生产环境可能包括固有的异质性来源及其非线性行为。 可以使用非线性多变量优化方法来导出经验作物产量预测方程。 准牛顿法可用于优化，以最小化产量预测中的不一致性和错误。 通过预定义经验方程的迭代收敛最小二乘法函数的最小化可以基于具有断点的分段线性回归方法。 这种非线性方法可以实现可接受的较低残差值，预测值非常接近观测值。 本发明可以针对全世界的不同作物进行修改和定制。
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2.发明授权Crop yield prediction using piecewise linear regression with a break point and weather and agricultural parameters 有权作物产量预测采用分段线性回归与断点和天气和农业参数公开(公告)号：US07702597B2
公开(公告)日：2010-04-20
申请号：US11108674
申请日：2005-04-19
CPC分类号： G06Q10/04 , Y02A40/232
摘要： Crop yield may be assessed and predicted using a piecewise linear regression method with break point and various weather and agricultural parameters, such as NDVI, surface parameters (soil moisture and surface temperature) and rainfall data. These parameters may help aid in estimating and predicting crop conditions. The overall crop production environment can include inherent sources of heterogeneity and their nonlinear behavior. A non-linear multivariate optimization method may be used to derive an empirical crop yield prediction equation. Quasi-Newton method may be used in optimization for minimizing inconsistencies and errors in yield prediction. Minimization of least square loss function through iterative convergence of pre-defined empirical equation can be based on piecewise linear regression method with break point. This non-linear method can achieve acceptable lower residual values with predicted values very close to the observed values. The present invention can be modified and tailored for different crops worldwide.
摘要翻译： 可以使用具有断点和各种天气和农业参数（如NDVI，表面参数（土壤水分和表面温度））和降雨数据的分段线性回归方法来评估和预测作物产量。 这些参数可能有助于估计和预测作物条件。 整个作物生产环境可能包括固有的异质性来源及其非线性行为。 可以使用非线性多变量优化方法来导出经验作物产量预测方程。 准牛顿法可用于优化，以最小化产量预测中的不一致性和错误。 通过预定义经验方程的迭代收敛最小二乘法函数的最小化可以基于具有断点的分段线性回归方法。 这种非线性方法可以实现可接受的较低残差值，预测值非常接近观测值。 本发明可以针对全世界的不同作物进行修改和定制。
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