# Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention

Source: [Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention](<https://www.sota2.com/research/paper/68fa6a0c1e209131cea25c0c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:15:37.395Z

Authors: Matthew R. Keaton, Ram J. Zaveri, Meghana Kovur, Cole Henderson, Donald A. Adjeroh, Gianfranco Doretto

Published: 2021-06-03T21:22:18.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2106.02141>)

[PDF](<https://arxiv.org/pdf/2106.02141.pdf>)