Abstract:
This project focuses on the design of a mobile-based machine learning system for the recognition of poultry diseases in Uganda. The livestock sector in Uganda is on the rise and the fast-growing population requires improved poultry production. However, several constraints, including diseases, lack of drugs, improper farm management, and wrong motives, affect increased production. The use of artificial intelligence for real-time disease recognition in poultry is promising due to its non-intrusive properties and ability to provide a wide range of information. The system uses Tensor Flow to analyze and train the model which is later deployed into a mobile application with the help of Android Studio. The literature review highlights the main concepts of poultry diseases and the use of machine learning methods in poultry. The result is a system that will have reduced operating costs and improved disease detection for farmers.
