An evaluation of a pre-trained transformer-based self-distillation model (DINOv2) for cross-domain plant species identification

Chin Ann, Ong and Fei Siang, Tay and Yi Lung, Then and Chris, McCarthy (2025) An evaluation of a pre-trained transformer-based self-distillation model (DINOv2) for cross-domain plant species identification. Neural Computing and Applications. pp. 1-27. ISSN 1433-3058

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Abstract

Plant species identification is a fundamental process in botany and agriculture sector. In recent years, deep neural networks have become the primary approach for automating this task, providing valuable insights into biodiversity, ecological systems, and agricultural practices. Along with more discoveries in plant species, training a deep neural network becomes very challenging as the cost required to collect and annotate plant samples is expensive and impractical. Despite the lack of labelled plant samples, recent studies have explored the potential of leveraging publicly available and systematically annotated plant specimens in herbaria coupled with field images for plant species identification through cross-domain adaptation techniques. However, the accuracy of these methods remains unsatisfactory, motivating the exploration of alternative approaches. In this paper, we evaluated the feasibility of employing a pre-trained transformer-based self-distillation model (DINOv2) for cross-domain plant species identification tasks. We trained our model with the PlantCLEF2020 dataset comprised of approximately 320 k herbarium and field images representing 997 plant species. Our approach leverages the advanced feature extraction capabilities of DINOv2, which enhances cross-domain adaptation by effectively bridging the gap between herbarium and field images, achieving a 17.7% improvement over the best model proposed in previous work, that employs ensembles of Siamese network architectures with triplet loss (HFTL-ENS and OSM-ENS).

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