Integrated Robotics System for Ripeness Detection, Tomato Harvesting and Leaf Disease Identification using CNN and YOLOv8

Abstract

In contemporary agriculture, the need for innovative solutions to enhance efficiency and reduce costs are ever-growing. This project introduces a pioneering Integrated Robotics System designed to revolutionize the monitoring and harvesting processes for short-lifecycle crops, with a primary focus on tomato plants. This project uses an improved YOLOv8 based algorithm for real-time detection of plant leaf condition in improving the precision farming. The system integrates advanced technology to detect crop diseases, suggest suitable pesticides, and autonomously harvest ripe fruits by the usage of robotic system. In essence, the project envisions a paradigm shift in agriculture, optimizing resource utilization, reducing labour dependency, and promoting sustainability. The Integrated Robotics System seamlessly amalgamates technological prowess with agricultural needs. The integration of CNN for disease classification and YOLOv8 for object detection highlights a commitment to cutting-edge solutions, promising a more efficient and productive future for farmers and farming processes, effectively tackling the challenges of modern agriculture follow. To optimize the harvesting process, a precision robotic arm is employed, capable of identifying and plucking ripe fruits at the opportune moment. This not only ensures minimal wastage but also addresses the labour intensive nature of traditional harvesting methods.