| dc.creator |
Mbelwa, Hope |
|
| dc.date |
2021-10-06T11:02:50Z |
|
| dc.date |
2021-10-06T11:02:50Z |
|
| dc.date |
2021-07 |
|
| dc.date.accessioned |
2022-10-25T09:14:54Z |
|
| dc.date.available |
2022-10-25T09:14:54Z |
|
| dc.identifier |
https://dspace.nm-aist.ac.tz/handle/20.500.12479/1344 |
|
| dc.identifier.uri |
http://hdl.handle.net/123456789/94456 |
|
| dc.description |
A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of
Master’s in Information and Communication Science and Engineering of the Nelson
Mandela African Institution of Science and Technology |
|
| dc.description |
The poultry sector in the country is highly affected by diseases including Coccidiosis,
Salmonella and Newcastle that have a significant impact on production. Lack of reliable
information and proper methods of farming has led to the spread of the diseases as the majority
of farmers practice traditional farming, hence lack a systematic way to detect and diagnose the
disorders. Poultry farmers rely on experts to diagnose and detect the diseases; access to the
experts is also a challenge due to the limited number of extension officers. With the available
tools from artificial intelligence and machine learning, there is a potential to semi-automate the
diagnostics process for the most common diseases in chickens. This study proposes a solution
for predicting diseases in chickens using faecal images and deep Convolutional Neural
Networks (CNN). Additionally, the work leverages the use of pre-trained models and develop
the solution for the same problem. Based on the comparison, it is indicated that the model
developed from the XceptionNet deep learning framework outperforms other models for all
the metrics used. The experimental results indicate the accuracy of transfer learning at 94%
using pre-training over the other models from fully training on the same dataset. The results
show that the pre-trained XceptionNet framework (94%) has the best overall performance and
highest prediction accuracy, and can be suitable for chicken disease detection application. The
findings show that the proposed model is ideal for poultry diseases detection method. |
|
| dc.format |
application/pdf |
|
| dc.language |
en |
|
| dc.publisher |
NM-AIST |
|
| dc.subject |
Research Subject Categories::TECHNOLOGY |
|
| dc.title |
Image - based poultry disease detection using deep convolutional neural network |
|
| dc.type |
Thesis |
|