Autonomous Weed Identification Model

Abstract: Weeding is a crucial but time-consuming task in agriculture that requires significant manual labor and often relies on chemical herbicides, which can negatively impact the environment and human health. An autonomous weeding robot is proposed as a solution to address these challenges. The robot employs computer vision and machine learning techniques to detect and classify weeds. The objective of this project is to develop an autonomous weeding robot that can effectively and efficiently identify weeds from crops, while reducing the reliance on chemical herbicides, minimizing labor costs, and promoting sustainability in agriculture. The methodology involves designing and building a prototype robot, training and testing its computer vision and machine learning algorithms, and evaluating its performance in real-world field conditions. The outcomes of this project can include improved weed management, reduced reliance on chemical herbicides, labor cost savings, increased efficiency and productivity, enhanced sustainability, and advancements in agricultural technologies. This project proposes the development of an autonomous weeding robot using computer vision and machine learning techniques, specifically the YOLO (You Only Look Once) ML model. The robot aims to effectively detect and classify weeds, reducing the reliance on chemical herbicides and minimizing labor costs in agriculture. The prototype robot is designed, and its algorithms are trained and tested, achieving a model accuracy of 82.6% and a validation accuracy of 77.72%. The project's outcomes include improved weed management, cost savings, increased efficiency, sustainability, and advancements in agricultural technologies.

Keywords: Weed Detection Techniques, YOLO, TensorFlow, Raspberry Pi, Agricultural Robotics, Sustainable Farming, Herbiside Reduction, Image Processing, Precision Farming.