AI-POWERED MONITORING OF CROP HEALTH, SOIL CONDITION, AND PEST RISKS USING MULTISPECTRAL/HYPERSPECTRAL IMAGING AND SENSOR DATA

Abstract

Agriculture is increasingly embracing intelligent technologies to improve crop productivity while ensuring sustainable resource utilization and environmental conservation. This paper presents an AI-powered framework for monitoring crop health, soil condition, and pest risk by integrating multispectral/hyperspectral imaging with IoT-based sensor data. The proposed system combines spectral features extracted from crop images with real-time environmental parameters, including soil moisture, temperature, humidity, pH, and essential soil nutrients such as nitrogen, phosphorus, and potassium, to provide a comprehensive assessment of field conditions. A feature fusion approach integrates heterogeneous data from imaging and sensor sources, enabling machine learning models to perform crop health evaluation, nutrient status assessment, and pest risk prediction with improved reliability. To support precision agriculture, the framework introduces a Crop Health Score that summarizes the overall field condition by considering plant vigor, soil quality, and environmental stability. Based on predictive analysis, the system generates timely recommendations for irrigation, fertilizer application, and pest management, enabling preventive rather than reactive interventions. A web-based dashboard presents real-time monitoring results, risk levels, and actionable insights through an intuitive interface. The proposed framework demonstrates the effectiveness of combining artificial intelligence, spectral imaging, and sensor-driven monitoring to optimize agricultural resource utilization, reduce unnecessary chemical inputs, enhance crop productivity, and promote sustainable farming practices under dynamic environmental conditions.