Swarm Robotics Optimization Using Deep Q-Learning for Cooperative Search and Rescue Missions

Advancements in technology, including the Internet of Things (IoT) and Artificial Intelligence (AI), greatly impact agriculture. The study investigates the statistical applications of AI-driven robotic devices based on the IoT as a basic emphasis. Using manual effort and chemical fertilizers, traditional farming can be highly attributed to inefficiency, health problems and environmental effects. The paper proposes an AgriBotIQ, a revolutionary platform that uses robotics based on IoT to monitor and analyse with accurate participation in plant management. Autonomous robots can collect information based on plants and their habitats by using imaging devices and sensors like soil moisture, humidity, temperature, and many others. Machine learning (ML) algorithms search the database for anomaly detection, threats, and crop trends. To identify the crops that are diseased or healthy, ML is integrated with computer vision. The suggested AgriBotIQ also eliminated weeds, boosting the output by neglecting unneeded waste and chemicals. The emerging IoTs have allowed better remote plant monitoring in more versatile and précised. Overall productivity and protection of crops are possible by statistical analysis and real-time notifications of the proactive decisionmaking outcomes. By combining IoT and AI, the future agricultural crop security will improve greatly.