TeamB SumvitMVSR Engineering College PSS BharadwajMVSR Engineering College Tarun JakkulaMVSR Engineering College Our project, "Autonomous Pesticide Deployment," leverages Q-learning a core RL technique—to address the complexities of autonomous movement and decision-making within agricultural environments. Through Q-learning, our virtual agent learns and refines its pesticide deployment strategies by exploring and interacting with a simulated grid-based environment.
The primary focus of our approach is to simulate diverse scenarios within controlled environments to enhance the agent's ability to make informed decisions in real-time. By utilizing Q-learning, we aim to optimize pesticide deployment strategies, minimize waste, and respond effectively to changing agricultural conditions.
The outcomes of this project have significant implications for agriculture, promising enhanced efficiency and sustainability in pesticide usage. By showcasing the potential of RL techniques in computer-simulated environments, we aim to contribute to the autonomous pesticide deployment practices, ensuring safer and more effective pest control strategies.
Our project underscores the transformative impact of Q-learning in agricultural automation, paving the way for intelligent decision-making and autonomous operations in pest management.
