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		<title>One Camera, Three Lanes: Lightweight AI Prototype Detects and Ejects Defective Apples in Online Sorting Test</title>
		<link>https://bioengineer.org/one-camera-three-lanes-lightweight-ai-prototype-detects-and-ejects-defective-apples-in-online-sorting-test/</link>
		
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		<pubDate>Fri, 23 Oct 2026 13:57:46 +0000</pubDate>
				<category><![CDATA[Agriculture]]></category>
		<category><![CDATA[agricultural robotics]]></category>
		<category><![CDATA[apple sorting]]></category>
		<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[defect detection]]></category>
		<category><![CDATA[lightweight deep learning]]></category>
		<category><![CDATA[machine vision]]></category>
		<category><![CDATA[multi-object tracking]]></category>
		<category><![CDATA[object detection transformer]]></category>
		<category><![CDATA[pneumatic sorting]]></category>
		<category><![CDATA[post-harvest grading]]></category>
		<category><![CDATA[precision agriculture]]></category>
		<category><![CDATA[RT-DETR]]></category>
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					<description><![CDATA[A lightweight machine-vision prototype pairs a single industrial camera with a streamlined detector called MOD-DETR and a SORT-based tracker called ASORT to detect and eject defective apples across three lanes; in an online test on 200 apples it falsely rejected 2.5 percent of normal fruit but missed 15.0 percent of defective apples, which the authors attribute to paddle timing, fruit spacing and pneumatic delay.]]></description>
		
		
		
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