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        <datestamp>2026-09-21T13:33:30Z</datestamp>
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          <dc:title>Lightweight Transformer Models for Real-Time Rice Leaf Disease Detection on Edge Devices</dc:title>
          <dc:creator>Kusuma, Rina</dc:creator>
          <dc:creator>Prasetyo, Bagus</dc:creator>
          <dc:subject>vision transformer</dc:subject>
          <dc:subject>edge computing</dc:subject>
          <dc:subject>plant disease</dc:subject>
          <dc:subject>model compression</dc:subject>
          <dc:description>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.</dc:description>
          <dc:publisher>Karoteh Utama Publisher</dc:publisher>
          <dc:date>2026-09-21</dc:date>
          <dc:type>info:eu-repo/semantics/article</dc:type>
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          <dc:type>Peer-reviewed Article</dc:type>
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          <dc:identifier>https://uji.purwantoro.my.id/article/1</dc:identifier>
          <dc:source>JURNAL UJI COBA; Vol. 1 No. 1 (2026); 1-14</dc:source>
          <dc:source>1234-273X</dc:source>
          <dc:language>eng</dc:language>
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