Predicting crop disease severity using real time weather variability through machine learning algorithms

Integrating disease severity with real-time meteorological variables and advanced machine learning techniques has provided valuable predictive insights for assessing disease severity in wheat. This study emphasizes the potential of machine learning models, particularly artificial neural networks (ANN), in predicting wheat disease severity with high accuracy. The field experiment was conducted over two consecutive rabi growing seasons (2023 And 2024) using a randomized block design with four sowing dates to investigate critical weather-disease relationships for two key wheat pathogens: Puccinia striiformis f. sp. tritici (yellow rust) and Blumeria graminis f. sp. tritici (powdery mildew). Weekly assessments of disease severity were combined with meteorological data and analyzed using ANN and regularized regression models. The ANN model demonstrated superior predictive accuracy for yellow rust and powdery mildew, achieving R-squared values (R2 of 0.96 And 0.98 for calibration And 0.93 And 0.95 for validation, respectively. Random Forest (RF) models also exhibited robust performance with R2 values of 0.97 And 0.98 for calibration And 0.93 And 0.90 for validation for yellow rust and powdery mildew, respectively. In contrast, Elastic Net, Lasso, and Ridge regression models showed comparatively moderate predictive capabilities. Principal component analysis (PCA) explained the key meteorological variables influencing disease incidence, with evapotranspiration, temperature, wind speed and humidity emerging as critical factors. Disease prediction is an important aspect of developing a decision support system, and it makes farmers make informed decisions to optimize production.