Work

On the role of geometric representations for ultra-fine-grained crop variety identification

Year: 2026

Type: article

Abstract: Distinguishing plant varieties within a single species, an Ultra-Fine-Grained Visual Categorization (Ultra-FGVC) problem, remains largely unsolved under real-world agricultural conditions. While foundation models have significantly improved species-level plant identification, their ability to capture the subtle phenotypic variations that define cultivars remains unclear.In this work, we investigate which representation learning paradigm is best suited for infraspecific plant identification. We compare two approaches: (i) geometric self-supervised learning (DINOv2/v3), which learns representations by enforcing consistency across different sub-regions (patches) of the same image, and (ii) semantic alignment (BioCLIP 2), which aligns images with taxonomic textual descriptions. We further evaluate a hybrid strategy combining geometric pre-training with species-level supervised fine-tuning (Pl@ntNet), aiming to leverage the complementary strengths of both approaches. Experiments are conducted across three complementary datasets: a controlled market garden dataset (22 varieties), a participatory banana dataset (87 classes), and a grape variety dataset exhibiting a strong domain shift (34 varieties).Our results show that geometric self-supervised representations provide the most suitable foundation for ultra-fine-grained recognition, consistently outperforming semantic alignment approaches. While species-level supervision yields modest improvements, its contribution remains limited compared to the gains obtained from geometric pre-training, indicating that the discriminative signal at the varietal level is primarily morphological rather than semantic. Furthermore, domain-specific adaptation provides small but consistent gains, and naïve multi-organ aggregation can dilute discriminative signals in asymmetric taxa such as \textit{Musa} (bananas).These findings demonstrate that ultra-fine-grained crop variety identification relies primarily on geometric sensitivity, and highlight the importance of designing models and aggregation strategies that preserve fine-scale morphological information for agrobiodiversity monitoring.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2026.102442