{"id":3399,"date":"2025-09-18T08:39:05","date_gmt":"2025-09-18T00:39:05","guid":{"rendered":"https:\/\/www.leexinghai.com\/aic\/?p=3399"},"modified":"2025-09-18T09:12:26","modified_gmt":"2025-09-18T01:12:26","slug":"%e6%96%87%e7%8c%aeintelligent-grading-method-for-walnut-kernels-based-on-deep-learning-and-physiological-indicators","status":"publish","type":"post","link":"https:\/\/www.leexinghai.com\/aic\/%e6%96%87%e7%8c%aeintelligent-grading-method-for-walnut-kernels-based-on-deep-learning-and-physiological-indicators\/","title":{"rendered":"[\u6587\u732eT509183]Intelligent grading method for walnut kernels based on deep learning and physiological indicators"},"content":{"rendered":"\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/www.leexinghai.com\/aic\/wp-content\/uploads\/2025\/09\/fnut-09-1075781.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"\u5d4c\u5165 fnut-09-1075781\"><\/object><a id=\"wp-block-file--media-840c5642-fefe-463a-a6c2-dad4ff12dc9e\" href=\"https:\/\/www.leexinghai.com\/aic\/wp-content\/uploads\/2025\/09\/fnut-09-1075781.pdf\">fnut-09-1075781<\/a><a href=\"https:\/\/www.leexinghai.com\/aic\/wp-content\/uploads\/2025\/09\/fnut-09-1075781.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-840c5642-fefe-463a-a6c2-dad4ff12dc9e\">\u4e0b\u8f7d<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Walnut grading is an important step before the product enters the market.<br \/>\n However, traditional walnut grading primarily relies on manual assessment of<br \/>\n physiological features, which is difficult to implement efficiently. Furthermore,<br \/>\n walnut kernel grading is, at present, relatively unsophisticated. Therefore,<br \/>\n this study proposes a novel deep-learning model based on a spatial<br \/>\n attention mechanism and SE-network structure to grade walnut kernels using<br \/>\n machine vision to ensure accuracy and improve assessment efficiency. In<br \/>\n this experiment, we found through the literature that both the lightness (L<br \/>\n value) and malondialdehyde (MDA) contens of walnut kernels were correlated<br \/>\n with the oxidation phenomenon in walnuts. Subsequently, we clustered<br \/>\n four partitionings using the L values. We then used the MDA values to<br \/>\n verify the rationality of these partitionings. Finally, four network models<br \/>\n were used for comparison and training: VGG19, EfficientNetB7, ResNet152V2,<br \/>\n and spatial attention and spatial enhancement network combined with<br \/>\n ResNet152V2 (ResNet152V2-SA-SE). We found that the ResNet152V2-SA-SE<br \/>\n model exhibited the best performance, with a maximum test set accuracy<br \/>\n of 92.2%. The test set accuracy was improved by 6.2, 63.2, and 74.1%<br \/>\n compared with that of ResNet152V2, EfficientNetB7, and VGG19, respectively.<br \/>\n Our testing demonstrated that combining spatial attention and spatial<br \/>\n enhancement methods improved the recognition of target locations and<br \/>\n intrinsic information, while decreasing the attention given to non-target<br \/>\n regions. Experiments have demonstrated that combining spatial attention<br \/>\n mechanismswithSEnetworksincreasesfocusonrecognizingtargetlocations<br \/>\n and intrinsic information, while decreasing focus on non-target regions.<br \/>\n Finally, by comparing different learning rates, regularization methods<\/p>\n","protected":false},"author":39,"featured_media":3401,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[70],"tags":[73,72],"class_list":["post-3399","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-wxk","tag-73","tag-sci"],"_links":{"self":[{"href":"https:\/\/www.leexinghai.com\/aic\/wp-json\/wp\/v2\/posts\/3399","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.leexinghai.com\/aic\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.leexinghai.com\/aic\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.leexinghai.com\/aic\/wp-json\/wp\/v2\/users\/39"}],"replies":[{"embeddable":true,"href":"https:\/\/www.leexinghai.com\/aic\/wp-json\/wp\/v2\/comments?post=3399"}],"version-history":[{"count":4,"href":"https:\/\/www.leexinghai.com\/aic\/wp-json\/wp\/v2\/posts\/3399\/revisions"}],"predecessor-version":[{"id":3420,"href":"https:\/\/www.leexinghai.com\/aic\/wp-json\/wp\/v2\/posts\/3399\/revisions\/3420"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.leexinghai.com\/aic\/wp-json\/wp\/v2\/media\/3401"}],"wp:attachment":[{"href":"https:\/\/www.leexinghai.com\/aic\/wp-json\/wp\/v2\/media?parent=3399"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.leexinghai.com\/aic\/wp-json\/wp\/v2\/categories?post=3399"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.leexinghai.com\/aic\/wp-json\/wp\/v2\/tags?post=3399"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}