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	<title>mobile agricultural technology &#8211; BIOENGINEER.ORG</title>
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		<title>Introducing GBiDC-PEST: A Lightweight Model for Real-Time Multiclass Tiny Pest Detection and Mobile Deployment</title>
		<link>https://bioengineer.org/introducing-gbidc-pest-a-lightweight-model-for-real-time-multiclass-tiny-pest-detection-and-mobile-deployment/</link>
		
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		<pubDate>Tue, 12 Aug 2025 14:46:32 +0000</pubDate>
				<category><![CDATA[Agriculture]]></category>
		<category><![CDATA[lightweight deep learning model]]></category>
		<category><![CDATA[mobile agricultural technology]]></category>
		<category><![CDATA[multiclass pest identification]]></category>
		<category><![CDATA[real-time pest detection]]></category>
		<category><![CDATA[tiny pest detection]]></category>
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					<description><![CDATA[In the rapidly evolving landscape of agricultural technology, the battle against crop-damaging pests has gained a powerful new ally: deep learning-based intelligent recognition. These advanced algorithms have shown remarkable promise in identifying pests from images, a task traditionally reliant on painstaking manual inspections. However, deploying such resource-intensive models on mobile platforms—vital for real-time, on-site agricultural [&#8230;]]]></description>
		
		
		
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