A Text-based Poultry Health System: An Interactive Disease Detection and Prescription Recommendations

Abstract

Poultry farming is vital to Uganda’s economy, providing income for many rural households. However, broiler chicken farmers struggle with early disease detection and management, leading to significant flock losses and financial hardship. Although advanced diagnostic tools exist, they are often too expensive and complicated for small-scale farmers in rural areas to access. This research presents a multilingual, symptom-based poultry disease prediction system, a lightweight, mobile-friendly machine learning solution that addresses the limitations of existing diagnostic tools. By allowing farmers to input observable symptoms like bird behavior, droppings, and flock age through a simple text-based interface, it eliminates the need for costly equipment, lab tests, or other traditional methods. Several machine learning algorithms were tested to identify the best method for disease prediction, including SVM, Random Forest, XGBoost, and KNN. KNN and SVM performed best, each achieving 96% accuracy and 97% precision, with Random Forest close behind. XGBoost performed poorly, with only 11% accuracy. Although SVM matched KNN in accuracy, it struggled with real-world probability calibration. KNN, on the other hand, provided reliable and interpretable confidence scores, making it the preferred choice for deployment. The final application is deployed using the Streamlit framework, enabling seamless access across desktop and mobile browsers. It provides real-time disease predictions, along with tailored prescriptions and prevention strategies. Additional features include a QR code for easy sharing, which enhances both the user experience and accessibility. This project bridges the gap between advanced AI and the practical realities of low-resource agricultural settings.