Gating Attention Convolutional Model Based on Long/Short-Range Features and its Application in Intelligent Livestock Farming

To address the challenges in fine-grained image classification stemming from significant intra-class variations and subtle inter-class differences, as well as the performance instability caused by simplistic fusion of deep and shallow features in conventional methods, this paper proposes a long–short range features network (LSNet), a novel gated convolutional model based on long/short-range feature integration. The model innovatively reformulates the convolution process as a sequential task, leveraging the specific order of each convolutional module in the network architecture to implement long–short-term memory network (LSTM)-based gating mechanisms for layer-wise feature selection and adaptive fusion between modules. LSNet achieves phased integration of spatial-static features from convolutional models and temporal-dynamic characteristics from recurrent models, effectively resolving the interference between deep and shallow features. Experimental results demonstrate that under equivalent parameter counts, LSNet achieves classification accuracy improvements of 2.58% and 4.80% over the baseline model on the FGVC-Aircraft and Stanford Cars datasets, with final accuracies reaching 90.15% and 91.97%, respectively, while its architecture allows seamless integration into any deep convolutional model. Gradient-based class activation mapping (Grad-CAM) visualizations further validate the model’s capability to capture discriminative subtle features. Additionally, we have successfully applied LSNet in intelligent livestock farming, achieving precise facial recognition of pigs in high-density breeding environments. The model achieves a recognition accuracy of 92.82% on the pig face recognition dataset. This research not only provides an effective solution for fine-grained classification but also demonstrates extensibility to other vision tasks requiring refined feature extraction.