Design of a Pig Farm Monitoring Video Scene Classification and Recognition Model Based on 3D Convolutional Neural Networks

With the expansion of pig farm scales and the increase in automation, traditional manual monitoring methods can no longer meet the demands for efficient and real-time management. To address this, this paper proposes a pig farm monitoring video scene classification and recognition model based on 3D Convolutional Neural Networks (3D CNN) to achieve automated analysis and scene classification of pig farm video surveillance data. The model utilizes the feature extraction capabilities of 3D Convolutional Neural Networks in the spatiotemporal domain, optimizing the C3D model by adjusting the convolutional layers and the depth of convolutional kernels. This enables the final model to effectively capture both temporal variations and spatial information in the video, thus accurately recognizing different monitoring scenes. Experimental results show that the proposed model achieves high classification accuracy and real-time performance on pig farm monitoring video datasets, and can effectively recognize various monitoring scenes, such as feeding, resting, and cleaning. This provides an intelligent and automated solution for pig farm management.