EMC-Net：A Jujube Fruit Defects Identification Network

Abstract Jujube defects typically occur during the growth, harvesting, packaging and transportation of jujube fruits. Reliable and rapid identification of jujube fruits defects and control measures are crucial for defect identification. However, traditional jujube fruits defects identification mostly relies on expert experience. It requires lots of labor and is subjective. In our study, we improved the ECA attention module and designed the Efficient Channel Attention2 (ECAA) attention module. Then we added ECAA to the MobileNet V2 and ConvNeXt base modules. Finally, for the output of each sub-model, we used weighted sum to achieve sub-models fusion to construct the ECAA MobileNet ConvNeXt(EMCNet) model. We also established a jujube fruits defects identification dataset that contained 4203 images of healthy and defected jujube fruits and their category labels. For EMC-Net model, we tested the performance of EMC-Net on the jujube fruits defects identification dataset. Experimental results show that the proposed model achieve an accuracy of 98.2% on the jujube fruits dataset. These results suggest that the proposed EMC-Net network can effectively identify jujube fruits defects.