Chicken Manure Disease Recognition Model Based on Improved ResNeXt50

Abstract The feces of sick chickens often show a certain degree morbidity. It is an effective method to identify by deep learning. Concerning the environmental factors chicken manure shooting that have great influence, this paper proposes introduction mixed attention mechanism into ResNeXt50 network model and applies it data set. While using residual structure learning, enhances perception learning ability input image. results showed overall accuracy ResNeXt50-3A on test set reached 97.4%, which was 17.3, 1.5, 28.6, 1.5 percentage points higher than RegNetY_400MF, Vgg19, ViT (Vision Transformer) models respectively. has high recognition can provide support for accurate identification diseased in all aspects breeding, replacing manual detection chickens.