A review on weed detection using ground-based machine vision and image processing techniques

Weeds are among the major factors that could harm crop yield. With the advances in electronic and information technologies, machine vision combined with image processing techniques has become a promising tool for precise real-time weed and crop detection in the field, providing valuable sensing information for site-specific weed management. This review summarized the advances of weed detection using ground-based machine vision and image processing techniques. Concretely, the four procedures, i.e., pre-processing, segmentation, feature extraction and classification, for weed detection were presented in detail. To separate vegetation from background, different color indices and classification approaches like color index-based, threshold-based and learning-based ones, were developed. The difficulty of weed detection lies in discriminating between crops and weeds that often have similar properties. Generally, four categories of features, i.e., biological morphology, spectral features, visual textures and spatial contexts, were used for the task, which were discussed in this review. Application of conventional machine learning-based and recently developed deep learning-based approaches for weed detection were also presented. Finally, challenges and solutions provided by researchers for weed detection in the field, including occlusion and overlap of leaves, varying lighting conditions and different growth stages, were discussed.