American Journal of Advanced Multidisciplinary Research and Innovation

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Smart Agriculture Using IoT, Machine Learning, and Remote Sensing

Author(s) Michael C. Ferris
Country United States
Abstract The increasing demand for food production, climate change, water scarcity, soil degradation, and the need for sustainable agricultural practices have accelerated the adoption of intelligent digital technologies in modern farming. Smart Agriculture integrates Internet of Things (IoT), Machine Learning (ML), Artificial Intelligence (AI), Remote Sensing, Geographic Information Systems (GIS), Global Positioning System (GPS), Unmanned Aerial Vehicles (UAVs)/Drones, Cloud Computing, Edge Computing, Big Data Analytics, Wireless Sensor Networks (WSNs), Digital Twin Technology, Computer Vision, Blockchain, and Decision Support Systems (DSS) to enable precision farming, intelligent crop monitoring, efficient resource management, and sustainable agricultural production. These technologies provide real-time data collection, predictive analytics, automated decision-making, and continuous environmental monitoring, thereby improving agricultural productivity while reducing operational costs and environmental impacts.
This study presents a comprehensive analysis of Smart Agriculture Using IoT, Machine Learning, and Remote Sensing. A qualitative analytical research methodology based on secondary data is adopted to investigate intelligent agricultural technologies, precision farming techniques, crop health monitoring, irrigation optimisation, pest and disease prediction, and sustainable resource management. The research explores how the integration of IoT sensors, machine learning algorithms, satellite imagery, drone-based monitoring, and cloud-based analytics enhances agricultural productivity, food security, climate resilience, and sustainable rural development.
The findings indicate that IoT-based sensor networks enable continuous monitoring of soil moisture, temperature, humidity, nutrient levels, and crop growth. Machine learning models accurately predict crop yield, irrigation requirements, pest outbreaks, and disease occurrence using historical and real-time datasets. Remote sensing technologies support large-scale vegetation monitoring, drought assessment, land-use analysis, and crop classification through multispectral and hyperspectral satellite imagery. Furthermore, AI-powered decision support systems optimise fertiliser application, irrigation scheduling, harvest planning, and supply chain management.
Despite these benefits, challenges remain concerning high implementation costs, limited digital infrastructure in rural areas, data interoperability, cybersecurity risks, farmer awareness, climate uncertainty, and policy limitations. Future research should focus on AI-driven autonomous farming systems, digital twins for agricultural ecosystems, federated learning, sustainable smart irrigation, blockchain-enabled agricultural traceability, and climate-adaptive intelligent farming models.
The study concludes that smart agriculture powered by IoT, machine learning, and remote sensing provides a transformative pathway towards sustainable food production by integrating intelligent sensing, predictive analytics, digital innovation, and precision resource management to support resilient agricultural ecosystems.
Keywords Smart Agriculture, Precision Agriculture, Internet of Things, Machine Learning, Remote Sensing, Artificial Intelligence, Precision Farming, Crop Monitoring, Digital Agriculture, Sustainable Farming.
Field Engineering
Published In Volume 2, Issue 6, November-December 2020
Published On 2020-11-22
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