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arXiv AI By Zongsen Qiu, Jianjun Wang, Yue Zhou, Zibo Zhou, Rui Chen

STA-Net: A Decoupled Shape and Texture Attention Network for Lightweight Plant Disease Classification

Read the original on arXiv AI →

STA‑Net is a lightweight neural network designed for plant disease classification on edge devices. It combines a training‑free neural architecture search (DeepMAD) to build an efficient backbone with a novel Shape‑Texture Attention Module (STAM) that separates shape and texture processing using deformable convolutions and a Gabor filter bank. On the CCMT plant disease dataset, STA‑Net achieved 89.00% accuracy and 88.96% F1 score with only 401K parameters and 51.1M FLOPs.

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 AI.

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