Publication

Abstract : Amidst the dynamic landscape of agriculture, the integration of robotic technologies emerges as a transformative force. This paper unveils a robotic plant health monitoring and intervention system, integrating machine learning and networking, tailored to revolutionize automation of agricultural practices. The centralized network server, in the system, utilizes Python scripts for socket communication with robots, as well as for implementing algorithms for optimized robot selection and path planning. Surveillance robots, equipped with ESP32-S with camera and Wroom modules navigate around the field and capture real-time images. The server on receiving these images does crop disease detection using Convolutional Neural Networks (CNNs), achieving high accuracy. Spray bots then target affected crops and spray pesticides and fertilizers as necessary using a 5V pump with precision. Unlike the surveillance robots, the spray bots are equipped with only ESP32 Wroom which controls both the movement and spray action. The system also features a software implementation that illustrates tasks like robot selection, networked communication, surveillance, and pesticide application autonomously executed in an agricultural field, highlighting the potential for scalable field deployment. The integrated solution ensures timely interventions, enhances yield quality, reduces environmental impact, and demonstrates potential of the concept - Internet of Robots in driving sustainable agricultural practices. Future iterations aim for full autonomy to further optimize system operation.