AI-POWERED IMAGE-BASED POULTRY DISEASE DETECTION SYSTEM

Abstract

Poultry farming contributes significantly to global food security, yet disease outbreaks continue to 
pose a major threat to flock health and productivity. Traditional diagnostic methods rely on visual 
inspection by farm personnel and confirmatory laboratory tests—such as bacterial cultures or PCR 
assays—which are time-consuming, costly, and often available only in centralized facilities. 
Consequently, delays in diagnosis can allow pathogens to spread rapidly through large flocks, leading 
to high morbidity and mortality rates, economic losses, and increased use of antibiotics. The problem 
is that many farmers lack immediate access to veterinary services or laboratory infrastructure, 
particularly in rural regions. Early symptoms of diseases like coccidiosis, Newcastle disease, and 
avian influenza can be subtle and easily overlooked, making manual screening unreliable. Even when 
signs become overt, transporting samples to a lab and waiting days for results delays intervention, 
allowing infections to escalate. Existing image-based solutions often require high computational 
resources or lack flexible, user-friendly interfaces, limiting their adoption among small- to medium
scale producers. There is a clear need for a rapid, accurate, and accessible diagnostic tool that can 
operate directly on-farm without specialized equipment. Such a system should leverage recent 
advances in deep learning to automatically recognize disease patterns from digital images, minimizing 
dependence on expert veterinarians and centralized labs. By integrating pre-trained convolutional 
neural networks—such as InceptionV3, MobileNetV2, and VGG16—into a unified application, it 
becomes possible to harness robust feature extractors while maintaining lightweight computational 
requirements. Embedding this functionality within a Tkinter-based GUI ensures that users with 
minimal technical expertise can upload images, initiate analysis, and receive near–real-time diagnostic 
feedback. In summary, this research addresses critical gaps in poultry disease management by 
developing a transfer learning–based, desktop application tailored to the needs of farmers. By 
focusing on automated image classification and an intuitive interface, the system aims to reduce 
diagnostic turnaround time, minimize economic losses, and enhance on-farm decision-making for 
disease control