Plant species identification in the wild is a difficult problem in part due to the high variability of the input data, but also because of complications induced by the long-tail effects of the datasets distribution. Inspired by the most recent fine-grained visual classification approaches which are based on attention to mitigate the effects of data variability, we explore the idea of using object detection as a form of attention. We introduce a bottom-up approach based on detecting plant organs and fusing the predictions of a variable number of organ-based species classifiers. We also curate a new dataset with a long-tail distribution for evaluating plant organ detection and organ-based species identification, which is publicly available.
M. R. Keaton, R. J. Zaveri, M. Kovur, C. Henderson, D. A. Adjeroh, and G. Doretto. Fine-Grained Visual Classi-fication of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021.