Multistage wheat yield prediction using hybrid machine learning techniques

Abstract

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.

Multistage sugarcane yield prediction using machine learning algorithms

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Machine learning methods for estimating reference evapotranspiration

“…Artificial neural networks (ANNs) are another method for estimating evapotranspiration adept at capturing non-linear relationships and complex patterns, making them suitable for modeling the intricate nature of evapotranspiration. Machine Learning models find applications in various fields, including yield prediction (Gupta et al, 2022;Saravanan and Bhagavathiappan, 2022). Setiya et al, (2022) used five distinct approaches-SMLR, LASSO, ELNET, Ridge regression, and ANN to explore the correlation between yield…”