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Robust Weed Recognition Through Color Based Image Segmentation and Convolution Neural Network Based Classification


 Current image classification techniques for weed detection (classic vision techniques and deep-neural net) provide encouraging results under controlled environment. But most of the algorithms are not robust enough for real-world application. Different lighting conditions and shadows directly impact vegetation color. Varying outdoor lighting conditions create different colors, noise levels, contrast and brightness. High component of illumination causes sensor (industrial camera) saturation. As a result, threshold-based classification algorithms usually fail. To overcome this shortfall, we used visible spectral-index based segmentation to segment the weeds from background. Mean, variance, kurtosis, and skewness are calculated for each input image and image quality (good or bad) is determined. Bad quality image is converted to good-quality image using contrast limited adaptive histogram equalization (CLAHE) before segmentation. A convolution neural network (CNN) based classifier is then trained to classify three different types of weed (Ragweed, Pigweed and Cocklebur) common in a corn field. The main objective of this work is to construct a robust classifier, capable of classifying between three weed species in the presence of occlusion, noise, illumination variation, and motion blurring. Proposed histogram statistics-based image enhancement process solved weed mis-segmentation under extreme lighting condition. CNN based classifier shows accurate, robust classification under low-to-mid level motion blurring and various levels of noise.