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	<title>2023 &#8211; 学术创新中心</title>
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	<title>2023 &#8211; 学术创新中心</title>
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		<title>[文献T509183]Intelligent grading method for walnut kernels based on deep learning and physiological indicators</title>
		<link>https://www.leexinghai.com/aic/%e6%96%87%e7%8c%aeintelligent-grading-method-for-walnut-kernels-based-on-deep-learning-and-physiological-indicators/</link>
		
		<dc:creator><![CDATA[外部作者]]></dc:creator>
		<pubDate>Thu, 18 Sep 2025 00:39:05 +0000</pubDate>
				<category><![CDATA[文献库]]></category>
		<category><![CDATA[2023]]></category>
		<category><![CDATA[中科院SCI一区]]></category>
		<guid isPermaLink="false">https://www.leexinghai.com/aic/?p=3399</guid>

					<description><![CDATA[Walnut grading is an important step before the product enters the market.
 However, traditional walnut grading primarily relies on manual assessment of
 physiological features, which is difficult to implement efficiently. Furthermore,
 walnut kernel grading is, at present, relatively unsophisticated. Therefore,
 this study proposes a novel deep-learning model based on a spatial
 attention mechanism and SE-network structure to grade walnut kernels using
 machine vision to ensure accuracy and improve assessment efficiency. In
 this experiment, we found through the literature that both the lightness (L
 value) and malondialdehyde (MDA) contens of walnut kernels were correlated
 with the oxidation phenomenon in walnuts. Subsequently, we clustered
 four partitionings using the L values. We then used the MDA values to
 verify the rationality of these partitionings. Finally, four network models
 were used for comparison and training: VGG19, EfficientNetB7, ResNet152V2,
 and spatial attention and spatial enhancement network combined with
 ResNet152V2 (ResNet152V2-SA-SE). We found that the ResNet152V2-SA-SE
 model exhibited the best performance, with a maximum test set accuracy
 of 92.2%. The test set accuracy was improved by 6.2, 63.2, and 74.1%
 compared with that of ResNet152V2, EfficientNetB7, and VGG19, respectively.
 Our testing demonstrated that combining spatial attention and spatial
 enhancement methods improved the recognition of target locations and
 intrinsic information, while decreasing the attention given to non-target
 regions. Experiments have demonstrated that combining spatial attention
 mechanismswithSEnetworksincreasesfocusonrecognizingtargetlocations
 and intrinsic information, while decreasing focus on non-target regions.
 Finally, by comparing different learning rates, regularization methods]]></description>
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		<title>[文献U509182]WT-YOLOM: An Improved Target Detection Model Based on YOLOv4 for Endogenous Impurity in Walnuts</title>
		<link>https://www.leexinghai.com/aic/%e6%96%87%e7%8c%aewt-yolom-an-improved-target-detection-model-based-on-yolov4-for-endogenous-impurity-in-walnuts/</link>
		
		<dc:creator><![CDATA[外部作者]]></dc:creator>
		<pubDate>Thu, 18 Sep 2025 00:35:02 +0000</pubDate>
				<category><![CDATA[文献库]]></category>
		<category><![CDATA[2023]]></category>
		<category><![CDATA[中科院SCI二区]]></category>
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		<item>
		<title>[文献U509181]Identification of hickory nuts with different oxidation levels by integrating self-supervised and supervised learning</title>
		<link>https://www.leexinghai.com/aic/%e6%96%87%e7%8c%aeidentification-of-hickory-nuts-with-different-oxidation-levels-by-integrating-self-supervised-and-supervised-learning/</link>
		
		<dc:creator><![CDATA[外部作者]]></dc:creator>
		<pubDate>Thu, 18 Sep 2025 00:31:07 +0000</pubDate>
				<category><![CDATA[文献库]]></category>
		<category><![CDATA[2023]]></category>
		<category><![CDATA[中科院SCI二区]]></category>
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