Revolutionizing Agriculture with Computer Vision, IoT, and Deep Learning

A Multi-Sensor System for Wheat Crop Growth Monitoring and Management

Abstract

This research presents a comprehensive multi-sensor system that integrates computer vision, IoT, and data fusion with machine learning to enhance wheat crop yield prediction, disease detection and management, and irrigation management. The system’s architecture consists of sensor nodes, data storage, deep learning agents, and a set of decision-making agents. Sensor nodes include cameras, temperature and humidity sensors, and disease-specific sensors to collect data on wheat crop health, environmental conditions, and disease symptoms. The deep learning agents identify crop growth stages, detect diseases, and predict yield. The decision-making agents help in irrigation, fertilization, and need of agrochemicals based on crop growth stage and disease detection. The system provides real-time data to farmers and enables them to make informed decisions regarding crop management. The deep learning agents are trained to be used by decision making agents to help revolutionize agriculture by providing real-time monitoring of wheat crop health, early detection of disease symptoms, and timely intervention to prevent crop losses. Machine learning techniques also help to predict wheat crop yield and identify areas requiring irrigation, fertilization, or pest control measures; resulting overall in batter management, early warning and batter crop production