基于多模态信息融合的皮蛋溏心沙心分类方法OA北大核心

The soft-yolk preserved eggs(SYP eggs)and hard-yolk preserved eggs(HYP eggs)each possess distinct textures and flavors,captivating their respective discerning consumers.Presently,artisans can only discern whether an egg is a soft-yolk or hard-yolk preserved egg based on the duration of the brining process,a method that not only demands their extensive expertise but also entails a high rate of misjudgment.To address this issue,the design of infrared imaging and visible/near-infrared spectroscopy acquisition devices was introduced,alongside a classification model for SYP eggs and HYP eggs.Utilizing gathered infrared image data,an enhanced model,ResNet_MLCA,was crafted by incorporating a mixed local channel attention(MLCA)module into the ResNet18 framework,achieving a noteworthy classification accuracy of 95.0％in distinguishing SYP eggs from HYP eggs.Furthermore,leveraging visible/near-infrared spectroscopy data,a one-dimensional residual module was designed,and through its stacking,the 1D_ResNet model for feature extraction and classification of visible/near-infrared spectroscopy data was developed,yielding an identical accuracy of 95.0％in discriminating SYP eggs from HYP eggs.In a bid to further augment detection accuracy,the infrared image features extracted by the ResNet_MLCA model and the visible/near-infrared spectroscopy features extracted by the 1D_ResNet were amalgamated.The resultant fusion model,ResNet_OP,achieved an outstanding classification accuracy of 98.3％in distinguishing SYP eggs from HYP eggs.In summary,this research can offer a novel,cost-effective,and high-precision classification model for SYP eggs and HYP eggs,which held significant implications for guiding preserved egg production and enhancing its quality.Additionally,the proposed method offered a theoretical reference for enhancing the performance of classification models for other agricultural products,aiming to further increase their accuracy and reduce the number of parameters in the fusion model.