Crop feature extraction from images with probabilistic superpixel Markov random field

In the process of agriculture automation, mechanization and intelligentialization, image segmentation for crop extraction plays a crucial role. However, the performance of crop segmentation is closely related to the quality of the captured image, which is easily affected by the variability, randomness, and complexity of the natural illumination. The previously proposed crop extraction approaches produce inaccurate segmentation under natural illumination when highlight occurs. And specularity removal techniques are still hard to improve the crop extraction performance, because of the flaw of their assumption and the high requirement of the experimental configuration. In this paper, we propose a novel crop extraction method resistant to the strong illumination by using probabilistic superpixel Markov random field. Our method is based on the assumption that color changes gradually between highlight areas and its neighboring non-highlight areas and the same holds true for the other regions. This priori knowledge is embedded into the MRF-MAP framework by modeling the local and mutual evidences of nodes. Besides, superpixel and Fisher linear discriminant are utilized to construct the probabilistic superpixel patches. Loopy belief propagation algorithm is adopted in the optimization step. And the label for the crop segmentation is provided in the final iteration result. We also compare our method to the other state-of-the-art approaches. The results demonstrate that our method is resistant to the strong illumination and can be applied to generic species. Moreover, our approach is also capable of extracting the crop from the shadow regions. Statistics from comparative experiments manifest that our crop segmentation method yields the highest mean value of 92.29% with the lowest standard deviation of 4.65%, which can meet the requirement of practical uses in our agriculture automatic vision system.