Statistics-based segmentation using a continuous-scale naive Bayes approach

Abstract

Segmentation is a popular preprocessing stage in the field of machine vision. In agricultural applications it can be used to distinguish between living plant material and soil in images. The normalized difference vegetation index (NDVI) and excess green (ExG) color features are often used in the segmentation of images with multiple color channels. In this paper, a Bayesian method is used to combine existing color features into a common color feature. This feature is then used to segment images into separate regions containing vegetation and soil. The common color feature produces an improved segmentation over the normalized vegetation difference index and excess green. The inputs to this color feature are the R, G, B, and near-infrared color wells, their chromaticities, and NDVI, ExG, and excess red. We apply the developed technique to a dataset consisting of 20 manually segmented images captured under artificial illumination. The results show that our combined feature enables better segmentation using the individual color features. Better segmentation allows for more robust vision-based weeding, thereby allowing for lower safety margins within cell-sprayers and lower herbicide usage.