DL-Poultry Care: Transfer Learning for Fast and Accurate Poultry Disease Detection

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.