# Accessible AI-powered poultry disease diagnostics: development, validation, and web deployment of a farmer-friendly MobileNet-based system for coccidiosis and salmonella detection in resource-constrained settings

## TL;DR

This paper presents an AI-powered web tool for farmers to detect poultry diseases like coccidiosis and salmonella using smartphone images, with high accuracy and low resource requirements.

## Contribution

A lightweight, accurate, and accessible AI system for poultry disease detection in resource-limited settings, with a publicly available web application.

## Key findings

- MobileNetV2-SVM achieved 96.17% accuracy in classifying poultry diseases from faecal images.
- The optimized system enables real-time inference at 61 milliseconds per image on standard hardware.
- A web-based application was developed to democratize AI diagnostics for farmers and veterinarians.

## Abstract

Automated detection of diseases in the poultry farming industry is seriously challenged in resource-limited farming environments where computational resources and technical expertise are scarce. This work fills this gap, via systematic evaluation of lightweight transfer learning architectures for practicalpro deployment. Two state-of-the-art pre-trained Convolutional Neural Network (CNN) models, MobileNetV2 and MobileNetV3Small, were tested along with three traditional Machine Learning Models (Support Vector Machine (SVM), Logistic Regression (LR) and K-Nearest Neighbours (KNN)) by using a balanced dataset containing 6436 images of faecal samples from three classes: Coccidiosis, Salmonella and Healthy. MobileNetV2-SVM showed better performance with 96.17% test accuracy (96% precision, recall, and F1-score), which was much better than other pipelines based on MobileNetV3Small (maximum 83.94% accuracy). The optimized pipeline achieves real-time inference at 61 milliseconds per image, enabling deployment on standard hardware. A publicly accessible web-based application was developed, allowing farmers and veterinary practitioners to perform smartphone-based disease classification without specialized expertise, democratizing AI-powered diagnostics for resource- limited agricultural settings. This research establishes a systematic benchmark for lightweight feature extraction architectures combined with traditional machine learning classifiers in poultry disease detection and demonstrates that practical, farmer-accessible AI diagnostics can achieve clinical-grade accuracy even in resource constrained environments.