Domain-specific augmentations and plant-only training data produce stronger self-supervised representations for fine-grained plant recognition than standard SSL pipelines or ImageNet pretraining.
Transactions on Machine Learning Research (TMLR) (2023)
2 Pith papers cite this work. Polarity classification is still indexing.
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Knowledge distillation allows smaller neural networks to match the accuracy of much larger models on plant species and disease recognition while using substantially less computation.
citing papers explorer
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Self-Supervised Learning of Plant Image Representations
Domain-specific augmentations and plant-only training data produce stronger self-supervised representations for fine-grained plant recognition than standard SSL pipelines or ImageNet pretraining.
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Energy-Efficient Plant Monitoring via Knowledge Distillation
Knowledge distillation allows smaller neural networks to match the accuracy of much larger models on plant species and disease recognition while using substantially less computation.