Artificial Intelligence and Smart Farming: Leveraging Predictive Analytics and IoT Technologies for Precision Agriculture and Food Security

https://doi.org/10.5281/zenodo.17792851

Abstract

The integration of Artificial Intelligence (AI), Internet of Things (IoT), and predictive analytics has emerged as a transformative approach in modern agriculture, aiming to enhance productivity and ensure food security. This study investigated the impact of AI, IoT, and predictive analytics on agricultural performance and their contribution to sustainable food systems. A quantitative research design was employed, collecting data from 286 farmers, agronomists, and practitioners using smart farming technologies. Structured questionnaires were utilized to capture the extent of technology adoption, IoT integration, predictive analytics utilization, agricultural productivity, and perceived food security outcomes. Descriptive statistics, correlation analysis, and regression models were conducted to examine relationships among the variables. Findings revealed that IoT integration, AI adoption, and predictive analytics utilization had significant positive effects on agricultural productivity, with IoT being the strongest predictor. Agricultural productivity, in turn, significantly influenced food security, highlighting the pathway through which technological adoption translated into broader societal benefits. The study also identified challenges related to sensor calibration, data quality, digital literacy, and resource constraints, indicating that technology adoption required complementary socio-institutional support for maximum impact. The results underscored the importance of integrating digital tools, training, and policy interventions to strengthen resilience in farming systems. These insights contribute to the understanding of smart agriculture and provide evidence-based guidance for policymakers, technology developers, and farmers aiming to enhance sustainable food production.