AI-DRIVEN PREDICTION OF KEY SOIL NUTRIENTS IN COTTON CULTIVATION FOR SUSTAINABLE AGRICULTURE

Abstract

Sustainable cotton cultivation fundamentally depends on maintaining soil fertility, yet conventional methods of nutrient assessment are often labor-intensive, expensive, and spatially inconsistent, making them unsuitable for large-scale adoption. To address this gap, the present study introduces an artificial intelligence (AI)-driven predictive framework capable of estimating key soil nutrients namely nitrogen (N), phosphorus (P), and potassium (K) with high precision and efficiency. Representative field samples were collected from major cotton-growing regions, and their physicochemical characteristics were examined using standardized laboratory protocols. These datasets were subsequently integrated with advanced machine learning algorithms, including random forest, support vector regression, gradient boosting, and deep neural networks, to model soil nutrient dynamics and forecast nutrient availability. Comparative performance analysis demonstrated that ensemble-based learning approaches consistently outperformed conventional statistical models, offering superior accuracy, robustness, and generalizability across diverse agro ecological zones. Furthermore, spatial mapping combined with AI-generated predictions provided actionable insights into site-specific nutrient deficiencies, thus enabling tailored fertilizer management strategies. This combined methodology lessens dependence on lengthy laboratory analyses while enabling quick, evidence-based choices for both farmers and agricultural planners. In essence, the study underscores how artificial intelligence is reshaping precision farming by improving cotton yield, preventing fertilizer overuse, safeguarding natural assets, and promoting lasting environmental balance.