Abstract

Agricultural productivity and public health are closely influenced by climatic variability, which has significantly affected both crop yield patterns and disease outbreaks. Extreme weather conditions have created the environmental factors that have increased the spread of vector-borne and climate-sensitive diseases. At the same time, the agricultural sector has depended on the crop yield forecasting systems that have supported food security planning. However, the existing analytical systems have treated agricultural prediction and health surveillance as two isolated domains. The absence of an integrated analytical framework has limited the ability of governments and agricultural agencies to anticipate the combined impacts of weather conditions on food production and public health risks. Conventional forecasting systems have analyzed yield patterns without considering the health indicators that have emerged from climatic fluctuations. To address this issue, this study has proposed a Weather–Agriculture–Health Integrated Mining Model (WAHIMM) that has combined crop yield prediction with climate-driven disease surveillance. The model has utilized weather attributes, agricultural yield records, and epidemiological indicators that have been collected from multi-source datasets. A hybrid learning pipeline has applied Random Forest regression for yield forecasting and Bayesian pattern mining for disease risk correlation, which has enabled the discovery of weather-dependent associations. The experimental evaluation demonstrates that the proposed Weather-Agriculture-Health Intelligent Mining Model (WAHIMM) achieves 97% accuracy, 95% precision, 95% recall, and 96% F1-Score while the mean absolute error decreases to 0.16. The framework significantly improves prediction performance when compared with the Regression Yield Model, the ML Crop Prediction model, and the Climate-Health Monitoring model. The integrated mining framework which analyzes the environmental variables that influence agricultural productivity and disease emergence provides reliable forecasting and early health risk detection.

Authors

G.L. Krishna Shri
Vellore Institute of Technology, Vellore, India

Keywords

Crop Yield Forecasting, Weather Analytics, Public Health Surveillance, Data Mining Framework, Climate-Health Integration