Authors: Ambuj Kumar Misra
Abstract: The global agricultural sector faces unprecedented challenges from climate variability, resource depletion, and a rapidly growing population that demands consistent food security. This study presents the design, implementation, and evaluation of an Intelligent Agricultural Advisory System (IAAS) that leverages secondary crop datasets, multi-source meteorological records, and machine learning algorithms to deliver actionable, site-specific farming recommendations. Drawing on publicly available repositories including the USDA National Agricultural Statistics Service (NASS), NOAA Global Historical Climatology Network, and the FAO FAOSTAT database, our framework integrates data preprocessing pipelines, feature engineering modules, and ensemble predictive models comprising Random Forest classifiers and Long Short-Term Memory (LSTM) networks. Field validation across five Midwestern U.S. counties over a three-year period (2020-2023) demonstrated an average crop yield prediction accuracy of 91.4%, a 23.6% improvement in farmer decision-making efficiency, and a measurable reduction in water usage compared to conventional irrigation scheduling. The system's modular architecture supports deployment across a web dashboard and a mobile application accessible to smallholder and commercial farms alike. Our findings confirm that intelligent advisory systems built on secondary data are both technically feasible and economically significant, offering a scalable pathway toward precision agriculture for diverse agro-climatic regions.
DOI: https://doi.org/10.5281/zenodo.19479446
