Smart Agriculture Using IoT and Machine Learning for Real-Time Soil Health Assessment
Keywords:
Smart Agriculture, Internet of Things (IoT), Machine Learning, Soil Health Assessment, Precision Farming, Real-Time Monitoring, Soil Sensors, Sustainable AgricultureAbstract
Agriculture plays a vital role in ensuring food security and sustainable development. Traditional methods of soil health assessment are often time-consuming, labor-intensive, and unable to provide real-time information. This paper presents a Smart Agriculture system that integrates Internet of Things (IoT) technology with Machine Learning (ML) techniques for real-time soil health assessment. The proposed system utilizes sensors to continuously monitor key soil parameters such as moisture, temperature, pH, and nutrient levels. The collected data is transmitted to a cloud platform where machine learning algorithms analyze the information and predict soil health status. Based on the analysis, farmers receive timely recommendations for irrigation, fertilization, and crop management. The integration of IoT and ML enhances decision-making, improves resource utilization, and increases agricultural productivity. The proposed approach contributes to precision farming by enabling efficient soil monitoring, reducing operational costs, and supporting sustainable agricultural practices.
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