Collaborative Human in the Loop Robotics Framework for Precision Agriculture and Crop Monitoring

Abstract

This paper presents a Collaborative Human-in-the-Loop (HITL) Robotics Framework of precision agriculture and crop monitoring, which tries to bring together the benefits of human decision-making and robotic efficiency. The HITL framework is developed to overcome the drawbacks of the conventional autonomous systems, where real-time human input is included to enhance flexibility, precision, and adaptability to the dynamic agricultural conditions. The technology uses autonomous robots having sensors, like RGB cameras, thermal sensors, and soil moisture sensors, to check the state of crops, pest infestation, and irrigation. The data gathered is processed by AI algorithms, and human operators are involved when complicated decisions are required, giving feedback to guide robots. Early experiments of the framework in actual farm environments demonstrated good performance with an accuracy of crop yield prediction of 92 %, pest detection of 89 %, and water use efficiency of 94 %, which were much better than the conservativeness of robotic systems used traditionally. The HITL framework also displayed the balance between autonomy and human competency by low frequency of human interventions (10% of the tasks) and high effectiveness of interventions (80%). The results also show how HITL framework can improve precision farming by enhancing the accuracy of crop monitoring, reducing the waste of resources, and increasing the productivity. The flexibility of the system to fluctuating environments renders it a useful resource to the contemporary agriculture industry. Future studies have the potential to investigate more sophisticated AI solutions like deep learning to make decisions in real-time and evaluate how the framework may be scaled to different agricultural areas and crops.