Application of Data Mining Technique for Wheat Crop Yield Forecasting for Districts of Gujarat State

2018 ◽  
Vol 8 (7) ◽  
Author(s):  
Shital H. Bhojani ◽  
Nirav Bhatt
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Wheat Crop ◽  
Data Mining Technique ◽  
Gujarat State ◽  
Mining Technique ◽  
Yield Forecasting ◽  
Crop Yield Forecasting

scholarly journals Crop Yield Forecasting using Data Mining

2021 ◽  
Author(s):  
Pallavi Kamath ◽  
Pallavi Patil ◽  
Shrilatha S ◽  
Sushma ◽  
Sowmya S
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Yield Forecasting ◽  
Using Data ◽  
Crop Yield Forecasting

scholarly journals Use of ordinal logistic regression in crop yield forecasting

MAUSAM ◽  
2021 ◽  
Vol 67 (4) ◽  
pp. 913-918
Author(s):  
VANDITA KUMARI ◽  
RANJANA AGRAWAL ◽  
AMRENDER KUMAR
Keyword(s):  
Logistic Regression ◽  
Crop Yield ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Ordinal Logistic Regression ◽  
Percentage Error ◽  
Wheat Crop ◽  
Yield Forecasting ◽  
Crop Yield Forecasting

The performance of ordinal logistic regression and discriminant function analysis has been compared in crop yield forecasting of wheat crop for Kanpur district of Uttar Pradesh. Crop years were divided into two or three groups based on the detrended yield. Crop yield forecast models have been developed using probabilities obtained through ordinal logistic regression along with year as regressors and validated using subsequent years data. In discriminant function approach two types of models were developed, one using scores and another using posterior probabilities. Performance of the models obtained at different weeks was compared using Adj R2, PRESS (Predicted error sum of square), number of misclassifications and forecasts were compared using RMSE (Root Mean Square Error) and MAPE (Mean absolute percentage error) of forecast. Ordinal logistic regression based approach was found to be better than discriminant function analysis approach.  

scholarly journals Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

2015 ◽  
Vol XL-7/W3 ◽  
pp. 39-44 ◽  
Author(s):  
A. Kolotii ◽  
N. Kussul ◽  
A. Shelestov ◽  
S. Skakun ◽  
B. Yailymov ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Biophysical Model ◽  
Wheat Crop ◽  
Biophysical Parameters ◽  
Model Based ◽  
Winter Wheat Crop ◽  
Yield Forecasting ◽  
Crop Yield Forecasting

Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

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