SmartAgroCare: An IoT & ML-Based Crop Health Monitoring System

Abstract

Agriculture is undergoing a digital transformation driven by emerging technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT). This paper proposes a Crop Monitoring System that combines these technologies to enable realtime observation, analysis, and prediction of crop and soil conditions. The system utilises IoT-based sensors deployed across the field to collect key environmental and soil parameters, including temperature, humidity, soil moisture, pH level, and light intensity. These data are transmitted to a cloud platform for processing and analysis using ML algorithms. The AI component further enhances decision-making by detecting crop diseases, predicting yield, and identifying irrigation or nutrient requirements based on data patterns and image-based analysis. A web and mobile-based interface allows farmers to visualise the collected data, receive alerts, and take timely corrective actions. This intelligent integration helps in optimising water usage, reducing pesticide dependency, and improving overall crop productivity. Experimental evaluations and simulated results indicate that the system provides accurate, efficient, and scalable monitoring suitable for diverse agricultural environments