Plant classification system for crop /weed discrimination without segmentation

This paper proposes a machine vision approach for plant classification without segmentation and its application in agriculture. Our system can discriminate crop and weed plants growing in commercial fields where crop and weed grow close together and handles overlap between plants. Automated crop / weed discrimination enables weed control strategies with specific treatment of weeds to save cost and mitigate environmental impact. Instead of segmenting the image into individual leaves or plants, we use a Random Forest classifier to estimate crop/weed certainty at sparse pixel positions based on features extracted from a large overlapping neighborhood. These individual sparse results are spatially smoothed using a Markov Random Field and continuous crop/weed regions are inferred in full image resolution through interpolation. We evaluate our approach using a dataset of images captured in an organic carrot farm with an autonomous field robot under field conditions. Applying the plant classification system to images from our dataset and performing cross-validation in a leave one out scheme yields an average classification accuracy of 93.8 %.