Poultryguard: AI-Driven Poultry Disease Detection and Clinical Decision Support Using Efficientnet-B3

Abstract

Poultry farming is a critical component of global food security, yet it is highly vulnerable to fast spreading infectious diseases such as coccidiosis, salmonellosis and Newcastle disease. Conventional diagnostic pathways depend on clinical examination and laboratory tests, which are time consuming, expertise dependent and often inaccessible for small and medium scale farmers. This paper presents a combined survey and implementation of PoultryGuard, a smartphone-centric framework for early poultry disease detection from chicken fecal images using convolutional neural networks. First, recent progress in image-based poultry disease detection is reviewed, with emphasis on fecal-image classification and transfer learning strategies. Then, the design and implementation of an EfficientNet-B3 based classifier trained on 8,067 annotated fecal images across four classes (healthy, coccidiosis, salmonella and Newcastle disease) is described. The model is deployed inside a Streamlit application that integrates user authentication, real time image acquisition, a structured veterinary knowledge base, automated diagnostic report generation in PDF format, Telegram based emergency alerts, a veterinary hospital locator and an admin analytics dashboard. Experimental evaluation on a heldout test set shows that the implemented EfficientNet-B3 model achieves 93.73% accuracy, with macro-averaged precision, recall and F1-scores above 0.93, while maintaining low inference latency suitable for smartphone assisted deployment. The study shows that poultry disease management in resource constrained farm environments can be revolutionized by combining deep learning with clinical decision support and communication modules.