Deep Learning-Driven Poultry Fecal Image Recognition and Disease Early Warning System Design

Abstract

The sustainable development of the poultry industry is constrained by frequent disease outbreaks and delayed clinical diagnosis, while conventional disease-control approaches based on manual experience are inadequate for precise management in large-scale farming. This study proposes an intelligent poultry-disease early-warning system based on deep-learning-driven fecal image recognition. Fecal morphological features are used as key indicators for early disease detection, and a dataset containing 10,548 valid samples across five categories—healthy, coccidiosis, Newcastle disease, infectious bursal disease, and salmonellosis—is constructed in collaboration with large-scale poultry farms in Guangdong Province. EfficientNet-B3 is adopted as the backbone network, and a Convolutional Block Attention Module (CBAM) and lightweight Lite-FPN multi-scale feature fusion structure are embedded to enhance fine-grained lesion recognition. The system integrates edge computing, industrial imaging, and wireless sensing for real-time deployment in poultry houses. Results show that warning accuracy reaches 91.6%, system availability reaches 99.94%, and the mortality-and-culling rate decreases by 22.6%, demonstrating the engineering feasibility and practical utility of the proposed system.