ABSTRACT We propose a segmentation algorithm for raisin extraction. The proposed approach consists of the following aspects. Deep learning is used to predict the number of raisins in each connected region, and the shape features such as the roundness, area, X-axis value for the centroid, Y-axis value for the centroid, axis length and perimeter of each region will be used to establish the prediction model. Morphological analysis, based on edge parameters including the polar axis, polar angle and angular velocity, is applied to search for the suitable break points that are useful for identifying the dividing lines between two adjacent raisins. To make our segmentation more accurate, some machine-learning algorithms such as the random forest (RF), support vector machine (SVM) and deep learning (deep neural network, DNN) are applied to predict the number of raisins and to decide whether the raisins need more segmentation. The performance of the three models is compared, and the DNN is the most accurate.

ABSTRACT Bombyx mori nucleopolyhedrovirus (BmNPV) disease is one of the most serious silkworm diseases, and it has caused great economic losses to the sericulture industry. So far, the disease has not been controlled effectively by therapeutic agents. Breeding resistant silkworm varieties breeding may be an effective way to improve resistance to BmNPV and reduce economic losses. A precise resistance-detection method will help to accelerate the breeding process. For this purpose, here we described the individual inoculation method (IIM). Details of the IIM include pathogen BmNPV preparation, mulberry leaf size, pathogen volume, rearing conditions, course of infection, and breeding conditions. Finally, a resistance comparison experiment was performed using the IIM and the traditional group inoculation method (GIM). The incidence of BmNPV infection and the within-group variance results showed that the IIM was more precise and reliable than the GIM.