A Feature-Based Machine Learning Agent for Automatic Rice and Weed Discrimination

Abstract

Rice is an important crop utilized as a staple food in many parts of the world and particularly of importance in Asia. The process to grow rice is very human labor intensive. Much of the difficult labor of rice production can be automated with intelligent and robotic platforms. We propose an intelligent agent which can use sensors to automate the process of distinguishing between rice and weeds, so that a robot can cultivate fields. This paper describes a feature-based learning approach to automatically identify and distinguish weeds from rice plants. A Harris Corner Detection algorithm is firstly applied to find the points of interests such as the tips of leaf and the rice ear, secondly, multiple features for each points surrounding area are extracted to feed into a machine learning algorithm to discriminate weed from rice, last but not least, a clustering algorithm is used for noise removal based on the points position and density. Evaluation performed on images downloaded from internet yielded very promising classification result.