Precision Environmental Agriculture: Integration of IoT, GIS, Remote Sensing, and Artificial Intelligence for Sustainable Crop Production

Vollständiger Abstract

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Abstract: Background The agriculture sector is now encountering many unique problems due to climate change, resource exhaustion, food demand, and environmental damage. Traditional methods of agriculture involve homogeneous field operations, which result in inefficient utilization of water, fertilizers, and pesticides. The application of innovative technology like the Internet of Things (IoT), Geographic Information System (GIS), Remote Sensing (RS), and Artificial Intelligence (AI) has revolutionized the concept of precision agriculture. Objective: The purpose of this research is to examine the incorporation of IoT, GIS, Remote Sensing, and AI in precision environmental agriculture and assess the possible contribution these technologies could make towards enhancing agricultural productivity and sustainability. Methods A comprehensive literature-based analytical study was conducted by reviewing recent scientific publications from 2020–2025. Information regarding IoT sensors, GIS-based spatial analysis, satellite and UAV remote sensing, and AI-driven predictive models was synthesized to develop an integrated precision agriculture framework. Comparative analysis was performed to assess the contribution of each technology toward sustainable food production. Results and Conclusion IoT, GIS, Remote Sensing, and Artificial Intelligence together provide a complete digital ecosystem for precision agriculture. The use of these technologies ensures environmental monitoring, crop health analysis, disease prediction, optimum irrigation scheduling, and estimation of yield in advance. When used together, these technologies help in reducing wastage, increasing productivity, and minimizing environmental impact. This research paper ends with a conclusion stating that digital agriculture is one of the main strategies of the future towards sustainable food production systems.