基于分类特征提取和深度学习的牛肉品质识别

目的：降低数据差异性和光谱特征冗余度对牛肉品质识别的影响。方法：提出一种基于分类特征提取和深度学习的牛肉品质识别方法，采用改进的DPeak算法对光谱数据进行自适应聚类分析，实现对数据的差异性分析。定义牛肉光谱特征提取目标函数，采用离散狮群算法进行求解，提取每个分类的最佳光谱特征子集，最大限度降低特征冗余度。运用改进狮群算法(ILSO)对每个分类对应的支持向量机(SVM)模型参数进行优化，提出融合分类特征提取和ILSO优化SVM的牛肉品质识别模型，完成对牛肉品质的分类识别。结果：相比于SSA-SVM、PCA-SVM识别模型，该模型识别精度提高了约12.3%~14.5%。结论：基于分类特征提取和深度学习的牛肉品质识别模型能够提高牛肉品质识别精度。

Beef quality recognition based on classification feature extraction and deep learning

Objective: To reduce the influence of data difference and spectral feature redundancy on beef quality recognition. Methods: A beef quality recognition method based on classification feature extraction and deep learning was proposed. The spectral feature extraction method of classified beef was designed, and the improved DPeak algorithm was used for adaptive clustering analysis of spectral data to realize the difference analysis of data. The objective function of beef spectral feature extraction was defined and solved by discrete lion swarm algorithm. The optimal spectral feature subset of each classification was extracted to minimize feature redundancy. The improved lion swarm algorithm (ILSO) was used to optimize the support vector machine (SVM) model parameters corresponding to each classification, and a beef quality recognition model integrating classification feature extraction and ILSO optimized SVM was proposed to complete the classification and evaluation of beef quality. Results: Compared with SSA-SVM and PCA-SVM, the recognition accuracy of this model is improved about 12.3%~14.5%. Conclusion: The beef quality recognition model based on classification feature extraction and deep learning can improve the accuracy of beef quality recognition.