Integration of maximum crop response with machine learning regression model to timely estimate crop yield

Abstract

Timely and reliable estimation of regional crop yield is a vital component of food security assessment, especially in developing regions. The traditional crop forecasting methods need ample time and labor to collect and process field data to release official yield reports. Satellite remote sensing data is considered a cost-effective and accurate way of predicting crop yield at pixel-level. In this study, maximum Enhanced Vegetation Index (EVI) during the crop-growing season was integrated with Machine Learning Regression (MLR) models to estimate wheat and rice yields in Pakistan's Punjab province. Five MLR models were compared using a fivefold cross-validation method for their predictive accuracy. The study results revealed that the regression model based on the Gaussian process outperformed over other models. The best performing model attained coefficient of determination (R 2 ), Root Mean Square Error (RMSE, t/ ha), and Mean Absolute Error (MAE, t/ha) of 0.75, 0.281, and 0.236 for wheat; 0.68, 0.112, and 0.091 for rice, respectively. The proposed method made it feasible to predict wheat and rice 6-8 weeks before the harvest. The early prediction of crop yield and its spatial distribution in the region can help formulate efficient agricultural policies for sustainable social, environmental, and economic progress.

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination: The Bilate cropland sub watershed

“…The calculation of the cropland zone statistics for the biomass index using the EVI was calculated in the Arc GIS raster calculator [ [69] , [70] , [110] , [111] ] on the MODIS image using the following equation. Based on the cropland trough, the extraction by attributes from the cropland class value of (12) from the MODIS-IGBP land cover type [ 112 ] was converted into a shape file for cutting crop data from national livelily hood data, based on and validated through field survey data for soil truth crop information [ 113 ] MODIS/Aqua [ 57 ] Enhanced Vegetation Indices (EVI) time series [ [64] , [69] , [114] ] indicate improved agricultural information such as: B. the food security index due to nitrogen degradation of farmland [ 115 ].…”

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination index from cropland N-fertilizer in Bilate downstream

“…Zhang et al, 2015). in addition, geo data base from crop land EVI zonal statics, is used to GWM for prediction of crop canopy water and to observe crop zonal root soil water content based on rain fall data of wet season as model input the independent variable (Wardlow & Egbert, 2010;Q. Zhou & Ismaeel, 2021).…”

Spatial Downscaling Model Combined with the Geographically Weighted Regression and Multifractal Models for Monthly GPM/IMERG Precipitation in Hubei Province, China

“…The Koenker (BP) statistic can test heteroscedasticity or non-stationarity, and it is strongly recommended to apply a GWR analysis if the non-stationarity is statistically significant [42,43]. The Jarque-Bera statistic for assessing model bias indicates whether the residuals are normally distributed [44][45][46].…”