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		<title>Smartphone Photos and Deep Learning Reveal Nitrogen Stress in Maize Leaves</title>
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				<category><![CDATA[Agriculture]]></category>
		<category><![CDATA[BMC Plant Biology]]></category>
		<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[EfficientNet]]></category>
		<category><![CDATA[Grad-CAM]]></category>
		<category><![CDATA[maize]]></category>
		<category><![CDATA[nitrogen diagnosis]]></category>
		<category><![CDATA[precision agriculture]]></category>
		<category><![CDATA[ResNet34]]></category>
		<category><![CDATA[smallholder farming]]></category>
		<category><![CDATA[smartphone imaging]]></category>
		<category><![CDATA[Tanzania]]></category>
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					<description><![CDATA[Tanzanian researchers trained deep neural networks to classify nitrogen status in maize leaves from smartphone photos, achieving over 93 percent accuracy in controlled trials and preserving correct nitrogen ranking even on real smallholder farms.]]></description>
		
		
		
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