Domain-adapted augmentations and plant-specific training data improve self-supervised representations for fine-grained plant species recognition over standard SSL pipelines.
In: Working Notes of CLEF 2024 – Conference and Labs of the Evaluation Forum
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Knowledge distillation produces smaller, lower-cost vision models that match larger models on plant species and disease recognition tasks across 70 trained models on Pl@ntNet300K-v2 and Deep-Plant-Disease.
citing papers explorer
-
Self-Supervised Learning of Plant Image Representations
Domain-adapted augmentations and plant-specific training data improve self-supervised representations for fine-grained plant species recognition over standard SSL pipelines.
-
Energy-Efficient Plant Monitoring via Knowledge Distillation
Knowledge distillation produces smaller, lower-cost vision models that match larger models on plant species and disease recognition tasks across 70 trained models on Pl@ntNet300K-v2 and Deep-Plant-Disease.