Lightweight Transformer Models for Real-Time Rice Leaf Disease Detection on Edge Devices
Rina Kusuma,
Bagus Prasetyo
Universitas Nusantara Raya
Received4 May 2026
Revised23 Jul 2026
Accepted22 Aug 2026
Published21 Sep 2026
Abstract
Rice leaf diseases cause yield losses of up to 30% in smallholder farms, yet most detection models are too heavy for the low-cost devices farmers can afford. We propose a distilled vision transformer with 3.1 million parameters that runs at 24 frames per second on a single-board computer. Trained on 18,400 field images from four provinces, the model reaches 94.6% accuracy across six disease classes, within 1.2 points of a full-size baseline. Field trials with 42 farmers show that diagnosis time fell from days to under a minute.
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