Automated weed classification with local pattern-based texture descriptors

In conventional cropping systems, removal of weed population extensively relies on the application of chemical herbicides. However, this practice should be minimized because of the adverse effects of herbicide applications on environment, human health, and other living organisms. In this context, if the distribution of broadleaf and grass weeds could be sensed locally with a machine vision system, then the selection and dosage of herbicides applications could be optimized automatically. This paper presents a simple, yet effective texture-based weed classification method using local pattern operators. The objective is to evaluate the feasibility of using micro-level texture patterns to classify weed images into broadleaf and grass categories for real-time selective herbicide applications. Three widely-used texture operators, namely Local Binary Pattern (LBP), Local Ternary Pattern (LTP), and Local Directional Pattern (LDP) are considered in our study. Experiments on 400 sample field images with 200 samples from each category show that, the proposed method is capable of effectively classifying weed images and provides superior performance than several existing methods.