2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 13 , Issue 2 , PP: 106-113, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Fausto Vizcaíno Naranjo 1 * , Fredy Cañizares Galarza 2 , Edmundo Jalón Arias 3
Doi: https://doi.org/10.54216/FPA.130209
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.
Precision Agriculture, IoT Sensors , Agriculture Technology , Sensor Data Analysis , Data-driven Farming, Smart Farming , Predictive Analytics , Agricultural IoT , Sensor Networks.
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