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arXiv Machine Learning By Ngoc-Bao Ho-Lam, Thai-Anh Nguyen

TinyCNN: A 193K-Parameter Network for On-Device Plant Disease Detection, with a Cross-Dataset Robustness Diagnosis

Read the original on arXiv Machine Learning →

TinyCNN is a lightweight convolutional neural network with only 193,190 trainable parameters designed for on‑device plant disease classification. It achieves 98.88% test accuracy on the 38‑class PlantVillage benchmark, outperforming larger models while consuming far less energy, memory, and cost. The study also explores knowledge distillation to further compress the model and evaluates cross‑dataset robustness, finding a significant performance drop when moving from PlantVillage to PlantDoc due to background‑driven shortcut learning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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