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This paper details the third-place solution by DS@GT ARC for the PlantCLEF 2026 challenge, focusing on multi-species plant identification from high-resolution vegetation quadrat images. The approach employs a fine-tuned DINOv2 ViT-L/14 classifier, leveraging multi-scale tile decomposition, kNN retrieval, and habitat-fit priors to enhance species prediction accuracy. Key findings reveal that habitat-fit demotion and multi-scale aggregation significantly contribute to performance, achieving a macro-F1 score of 0.43902 on the private leaderboard.
Habitat-fit demotion and multi-scale aggregation emerged as critical factors in achieving competitive performance for multi-species plant identification from complex imagery.
This paper describes DS@GT ARC's third-place solution to the PlantCLEF 2026 challenge on multi-species plant identification in vegetation quadrat images, where systems must predict every species present in high-resolution (~3000 x 3000 pixel) plot photographs while training only on single-label images of individual plants. The pipeline is built around a fine-tuned DINOv2 ViT-L/14 classifier applied over a multi-scale tile decomposition of each quadrat, with per-tile predictions blended with a FAISS kNN retriever and post-processed by source-aware temporal fusion across repeated plot visits, a habitat-fit demotion that injects geographic and altitude priors from the training data, and a South-Western Europe geographic mask. Habitat-fit demotion and multi-scale aggregation are the largest individual contributors in the ablations. Two complementary training-centric directions, a cross-region transformer with noisy-student distillation on the LUCAS dataset and a label-as-query transformer decoder over synthetic CLS-domain pseudo-quadrats, yielded null results. An inference-time augmentation with instance-aware segmentation crops also did not improve performance. The selected submission reaches a private-leaderboard macro-F1 of 0.43902 (third place; public 0.51096); an unselected configuration of the same pipeline scored above 0.45 on the private set. Code: https://github.com/dsgt-arc/plantclef-2026.
