Lightweight Transformer Models for Real-Time Rice Leaf Disease Detection on Edge Devices Kusuma, Rina Prasetyo, Bagus vision transformer edge computing plant disease model compression 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. Karoteh Utama Publisher 2026-09-21 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://uji.purwantoro.my.id/article/1 JURNAL UJI COBA; Vol. 1 No. 1 (2026); 1-14 1234-273X eng https://uji.purwantoro.my.id/article/1/download/1 Copyright (c) 2026 The Authors https://creativecommons.org/licenses/by/4.0/