Forecasting models for forewarning Soybean Yellow Mosaic Virus for Tarai Zone of Uttarakhand
DOI:
https://doi.org/10.13052/jrss0974-8024.1828Keywords:
Crop disease severity forecasting, Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN), Elastic Net (ELNET)Abstract
The emergence of soybean diseases represents a formidable obstacle to the sustainable progression of the rapidly evolving soybean industry, which endeavors to achieve elevated productivity and enhanced crop excellence. Prompt and accurate disease prognostication is imperative for efficacious management protocol, as it contributes to limiting pathogen proliferation. This investigation examined the impact of various meteorological factors on the incidence of Soybean Yellow Mosaic Virus (SYMV) in Pantnagar, Uttarakhand. Six multivariate frameworks were assessed, encompassing Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN) and Elastic Net (ELNET), employing both original climatic parameters and calculated weather indices to predict disease severity. For model construction, 80% of the dataset was allocated for training purposes while the remaining 20% was reserved for validation procedures. Among all examined frameworks, the ANN model incorporating weather indices (ANN-WI) exhibited exceptional predictive performance, attaining a Normalized Mean Square Error (nRMSE) of only 3.08% and an R2 coefficient of 0.99 during the calibration phase. Based on performance metrics, the frameworks were ranked as follows: ANN-WI ≈ ANN-W > ELNET-W > ELNET-WI > SMLR-WI > SMLR-W. The results definitively demonstrate that ANN-based frameworks, especially those incorporating weather indices, significantly exceeded alternative modeling techniques within the investigated area.
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