Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/2156 2018 2018 Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador Fausto Vizcaíno Naranjo Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador Fredy Cañizares Galarza Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador Edmundo Jalón Arias The intersection of IoT technology and machine learning has ushered in a new era of precision agriculture, offering innovative solutions to the pressing challenges of food security and environmental sustainability. This paper presents a comprehensive study on the integration of IoT sensors and machine learning techniques for crop yield prediction, with a focus on the ten most consumed crops worldwide. Leveraging a wealth of historical data encompassing environmental variables, pest conditions, and crop-specific attributes collected by IoT sensors, we develop and rigorously evaluate a predictive model employing gradient-boosting regressors. Our findings reveal that the proposed model excels in capturing the intricate relationships between IoT sensor data and crop yield predictions, outperforming established ML regressors in a series of comprehensive experimental comparisons. These results underscore the potential of data-driven decision-making in agriculture, equipping farmers and policymakers with tools to optimize resource allocation, risk management, and sustainable farming practices. In the context of a growing global population and changing climate, the insights from this research hold significant promise for transforming precision agriculture and enhancing global food production. 2023 2023 106 113 10.54216/FPA.130209 https://www.americaspg.com/articleinfo/3/show/2156