Geo Intelligence: A Key to Sustainable Paddy and Maize Production in Hassan District

Agriculture, a cornerstone of human civilization, exerts a significant impact on natural resources to fulfill societal food demands. Climate change, exacerbated by anthropogenic activities and environmental consequences, poses a critical threat to agricultural productivity. While modern agronomic practices have enhanced yields, they have also resulted in detrimental consequences such as habitat loss, reduced biodiversity, and resource depletion.

This study investigates crop suitability in Hassan District, India, by integrating Artificial Intelligence (AI) with Geographic Information Systems (GIS). Eight key geo-climatic and pedological factors, relatively stable over time, were considered. Determining optimal land use for targeted crop cultivation is crucial in the face of climate change and global food security concerns.

Geospatial technologies and Sequential AI have demonstrated significant potential in addressing agricultural and environmental challenges through data-driven approaches. This research assesses the suitability of land for paddy, maize, and gram cultivation during the kharif season in Hassan District. A weighted metric approach was employed within a GIS environment, utilizing a Sequential Artificial Neural Network (ANN) model. Initially, an equal-weighted arithmetic mean was used to evaluate seven criteria encompassing soil, climate, and topographic factors. Likewise, criterion weights were derived from a sequential regression model, reflecting their relative importance in crop suitability prediction.Slope, soil depth, and rainfall emerged as the most influential factors, collectively accounting for 76% of the total weight. The results demonstrated an improvement in site suitability assessment compared to conventional methods, highlighting the efficacy of this integrated AI-GIS approach.