Wheat yield production is largely attributed by weather parameters. Model developed by multiple linear, neural network and penalised regression techniques using weather data have the potential to provide reliable, timely and cost-effective prediction of wheat yield. Wheat yield data and weather parameter during crop growing period (46th to 15th SMW) for more than 30 years were collected for study area and model was developed using stepwise multiple linear regression (SMLR), principal component analysis (PCA) in combination with SMLR, artificial neural network (ANN) alone and in combination with PCA, least absolute shrinkage and selection operator (LASSO) and elastic net (ENET) techniques.  Analysis was carried out by fixing 70% of the data for calibration and remaining dataset for validation. On examining these models, LASSO and elastic net are performing excellent having nRMSE value less than 10 % for four out of five location and good for one location, because of prevention in over fitting and reducing regression coefficient by penalization.

Wheat being highly affected by the weather, adverse weather drastically reduces the wheat yield. Model was developed for multi stage wheat yield prediction by stepwise multi linear regression (SMLR), support vector regression (SVR), least absolute shrinkage and selection operator (LASSO) and hybrid machine learning LASSO-SVR and SMLR-SVR techniques. Wheat yield data and weather parameter for generating thermal and weather indices during different growth stage for more than 30 years were collected for study area. Analysis was carried out by fixing 70 % of the data for calibration and remaining 30 % dataset for validation in R software. Results showed that LASSO performed best having nRMSE value between 1.22 % at grain filling stage for IARI, New Delhi to 8.36 % for Hisar at flowering stage. The model performance of SVR is increased if a hybrid model in combination with LASSO and SMLR is applied. The hybrid model LASSO-SVR has shown more improvement than SVR model compared with SMLR-SVR.