Day-age recognition model for the domestic silkworm based on improved residual neural network
Accurate identification of silkworm day-age contributes to precise feeding and animal welfare.In order to accurately identify 14 day-age from the first day of age 3 to the seventh day of age 5 in the growth period of silkworm,this paper constructs a dataset with 14 day-age units by collecting specific silkworm species in a real environment.This paper proposes Moga-ResNet method based on an improved residual neural network.This method introduces a multi-order gating mechanism to obtain the saliency features of day-age images based on the classical residual neural network ResNet50.Through model training and testing on the same domestic silkworm day-age dataset,the recognition accuracy of Moga-ResNet is 96.57％,the F1 value is 96.57％,and the recall rate is 96.62％.Compared with the evaluation indexes of four classic models,including Swin Transformer,MobileNet v3,CSPNet and DenseNet,Moga-ResNe achieved a stronger recognition ability in day-age recognition of domestic silkworm,which can provide a foundation for carrying out work related to precise feeding and digital management.
万方数据
维普
