Jinlong Wu

Aiming at the problems of less research on cherry segmentation and identification, with slow recognition speed and low classification accuracy in agricultural products, a method based on DeepLabV3 was proposed to realize the rapid segmentation and identification of cherry in complex orchard environment. Complex environment mainly includes front lighting, back lighting, cloudy and rainy days, single fruit, multi fruit, fruit overlap, and branch and leaf occlusion. This model proposed the Atrous Spatial Pyramid Pooling (ASPP) module to effectively extract multi-scale contextual information, and solved the problem of target segmentation at multiple scales. The obtained data was divided into training, validation and testing sets in 7:1:2 ratios, and the residual network 50 (ResNet50) was selected as backbone of the DeepLabV3. Experimental results show that the algorithm in this paper can segment cherry quickly and accurately, the mean intersection over union (MIoU) was 91.06%, the mean pixel accuracy (MPA) was 93.05%, and the kappa coefficient was 0.89, which was better than fully convolutional networks (FCN), SegNet, DeepLabV1 and DeepLabV2. It is demonstrated that this study can provide technical support for intelligent segmentation of agricultural products.

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.