INTEGRATING PRECISION AGRICULTURE AND ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE CROP MANAGEMENT AND FOOD SECURITY

Abstract

This paper discusses the role of artificial intelligence in enhancing precision farming to manage crops sustainably and to ensure food security.  It employed an experimental method that is mixed with such approaches as field trials, sensor-based data, and surveys of farmers to compare the AI-driven and conventional agriculture.  Quantitative studies showed AI-precision farming had an average 20-40 percent increase in crop yields, a reduction of almost 50 percent in water usage, and optimization of fertilizer and pesticides without reducing crop production.  Cost-benefit analysis revealed that operational costs were significantly lower and profit were significantly higher. Environmental analysis demonstrated that there were reduced carbon footprints, and improved crop health indices.  Predictive models proved to have high accuracy (R 2 > 0.85) in predicting yield and input efficiency, therefore justifying the reliability of AI-based decision support.  Qualitative data supported the statement that farmers recognized the ecological and economic benefits but the pace of uptake depends on access to technical aspects, training and cultural orientation.  The analysis shows that AI-based accuracy farming is associated with economic, environmental, and social benefits that can be estimated, and it is a viable solution to address the problem of worldwide food security and stimulate sustainable agricultural development.