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		<title>Enhanced AI Vision System Teaches Greenhouse Robots to Actually Pick the Fruit</title>
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		<pubDate>Sun, 11 Oct 2026 14:50:55 +0000</pubDate>
				<category><![CDATA[Agriculture]]></category>
		<category><![CDATA[agricultural robotics]]></category>
		<category><![CDATA[collaborative robots]]></category>
		<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[depth validation]]></category>
		<category><![CDATA[fruit detection]]></category>
		<category><![CDATA[greenhouse automation]]></category>
		<category><![CDATA[perception-to-action]]></category>
		<category><![CDATA[RGB-D sensing]]></category>
		<category><![CDATA[robotic harvesting]]></category>
		<category><![CDATA[UR3e manipulator]]></category>
		<category><![CDATA[YOLOv11]]></category>
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					<description><![CDATA[A new RGB-D perception-to-action framework called E-YOLOv11-RGBD raised robotic greenhouse harvesting success from 51.7 to 92.5 percent by converting AI fruit detections into depth-validated, reachable and graspable robot actions.]]></description>
		
		
		
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