Fruit cracking and rust spots are common diseases of nectarines that seriously affect their yield and quality. Therefore, it is essential to construct fast and accurate disease-identification models for agricultural products. In this paper, a sparse dictionary learning method was proposed to realize the rapid and nondestructive identification of nectarine disease based on multiple color features combined with improved LK-SVD (Label K-Singular Value Decomposition). According to the color characteristics of the nectarine itself and the significant color differences existing in the three categories of nectarine (diseased, normal, and background parts), multiple color spaces of RGB, HSV, Lab, and YCbCr were studied. It was concluded that the G channel in RGB space, Y channel in YCbCr space, and L channel in Lab space can better distinguish the diseased part from the other parts. At the model-training stage, pixels of the diseased, normal, and background parts in the nectarine image were randomly selected as the initial training sets, and then, the neighboring image blocks of the pixels were selected to construct the feature vectors based on the above color space channels. An improved LK-SVD dictionary learning algorithm was proposed that integrated the category label into the process of dictionary learning, and thus, an over-complete feature dictionary with significant discrimination was obtained. At the model-testing stage, the orthogonal matching pursuit (OMP) algorithm was used for sparse reconstruction of the original data, which can obtain the classification categories based on the optimized feature dictionary. The experimental results show that the sparse dictionary learning method based on multi-color features combined with improved LK-SVD can identify fruit cracking and rust spot diseases of nectarines quickly and accurately, and the average overall classification accuracies were 92.06% and 88.98%, respectively, which were better than those of k-nearest neighbor (KNN), support vector machine (SVM), DeepLabV3+, and Unet++; the identification results of DeepLabV3+ and Unet++ were also relatively high, but their average time costs were much higher, requiring 126.46~265.65 s. It is demonstrated that this study can provide technical support for disease identification in agricultural products.

Fruit cracking and rust spot are common diseases of nectarine which would seriously affect its yield and quality. Therefore, it is essential to construct disease detection models for agricultural products with high speed and accuracy. In this paper, a sparse dictionary learning method was proposed to realize the rapid and nondestructive detection of nectarine disease based on the multiple color features combined with the improved K-SVD (K-Singular Value Decomposition). According to the color characteristics of nectarine itself and the significant color differences existing in the three categories of nectarine (diseased, normal and background parts), multiple color spaces of RGB, HSV, Lab and YCbCr were studied. It was concluded that the G channel in RGB space, Y channel in YCbCr space and L channel in Lab space can better distinguish the diseased part from the other parts. At the model training stage, pixels of the diseased, normal and background parts in the nectarine image were randomly selected as the initial training sets, then the neighborhood image block of the pixels were selected to construct the feature vectors based on the above color space channels. An improved LK-SVD (Label K-SVD) dictionary learning algorithm was proposed that integrating the category label into the process of dictionary learning, then an over-complete feature dictionary with significant discrimination was obtained. At the model testing stage, orthogonal matching pursuit (OMP) algorithm was used to sparse reconstruction the original data which can obtain the classification categories based on the optimized feature dictionary. Experimental results show that the sparse dictionary learning method based on multi-color features combined with improved LK-SVD can detect fruit cracking and rust spot diseases of nectarine quickly and accurately, and the average overall classification accuracies were 92.06% and 88.98% respectively, which was better than k-nearest neighbor (KNN) and support vector machine (SVM). It is demonstrated that this study can provide technical support for the diseases detection of agricultural products.