Visual features based boosted classification of weeds for real-time selective herbicide sprayer systems

Recent years have shown enthusiastic research interest in weed classification for selective herbicide sprayer systems which are helpful in eradicating unwanted plants such as weeds from fields, minimizing the side effects of chemicals on the environment and crops. Two commonly found weeds are monocots (thin leaf) and dicots (broad leaf), requiring separate chemical herbicides for eradication. Researchers have used various computer vision-assisted techniques for eradication of these weeds. However, the changing and un-predictive lighting conditions in fields make the process of weed detection and identification very challenging. Therefore, in this paper, we present an efficient weed classification framework for real-time selective herbicide sprayer systems, exploiting boosted visual features of images, containing weeds. The proposed method effectively represents the image using local shape and texture features which are extracted during the leaf growth stage using an efficient method, preserving the discrimination between various weed species. Such effective representation allows accurate recognition at early growth stages. Furthermore, the various illumination problems prior to feature extraction are minimized using an adaptive segmentation algorithm. AdaBoost with Naïve Bayes as a base classifier discriminates the two weed species. The proposed method achieves an overall accuracy 98.40%, with true positive rate of 0.983 and false positive rate of 0.0121 for the original dataset and achieved 94.72% accuracy with the expanded dataset. The execution time of the proposed method is about 35 millisecond per image, which is less than state-of-the-art methods.