Motivation

Poultry diseases are economically significant from their effects on poultry production, performance and some are zoonotic. Poultry farming in Tanzania operates on small to medium scale managed by youths and women in peri-urban and rural areas. Farms are either backyard type or semi-intensive.

Poultry sector in Tanzania valued at USD 210 million in 2015 has 36 million chickens in 4.6 million households (27 million people). The poultry sector is challenged by low productivity from diseases including Salmonella, Newcastle and Coccidiosis. The economic effects include high mortality rates.

Vaccination coverage is limited to some farming systems and specific areas. Therefore, robust diagnostics are required to these diseases especially in endemic areas like Tanzania. With the help of deep learning; farmers will have the potential to better diagnose poultry diseases and improve livestock health.

The ongoing efforts to develop an automated poultry diseases diagnostics tool using deep learning will produce a dataset of 2,000 high-quality labeling for 500 images per class of poultry fecal1. The classes are healthy, Salmonella, Newcastle Disease and Coccidiosis.

The initial baseline models trained for image classification on VGG16 and Resnet 50 deep learning frameworks overfit on the dataset of 271 to 500 images per class2,3. We aim to supplement the dataset with 2,000 images per class to train a large enough model for diagnostics.

Outcomes

The main objective of the proposed project is developing a dataset for poultry diseases diagnostics using machine learning. The specific objectives are:

• Generation of Training/Testing dataset for poultry diseases diagnostics
• Dataset curation
• Diagnostics

The expected outcome of the proposed project is establishing an annotated dataset for poultry diseases diagnostics for small to medium scale poultry farmers.

The dataset will be shared on open access to the ML community on Kaggle datasets and used for teaching at different Machine Learning initiatives in Africa and globally. The different algorithms used on the project will be shared on GitHub to ensure the work is reproducible.

Long term vision

The proposed project will contribute to the efforts of modernizing the poultry sector in Tanzania4 by providing a data driven solution to support concentrated delivery of veterinary and extension services to smallholder poultry farmers. The dataset will be open access enabling the solution to be reproduced in other countries.

Personnel

1. Hope Emmanuel Mbelwa- Project Lead, Nelson Mandela African Institution of Science and Technology (NM-AIST)
2. Ezinne Nwankwo, Duke University
3. Dr. Dina Machuve, Nelson Mandela African Institution of Science and Technology (NM-AIST)
4. Dr. Neema Mduma, Nelson Mandela African Institution of Science and Technology (NM-AIST)
5. Dr. Evarest Maguo, Elang’ata Agrovet Services