| dc.creator |
Mbelwa, Hope |
|
| dc.creator |
Machuve, Dina |
|
| dc.creator |
Mbelwa, Jimmy |
|
| dc.date |
2021-06-24T06:21:45Z |
|
| dc.date |
2021-06-24T06:21:45Z |
|
| dc.date |
2021 |
|
| dc.date.accessioned |
2022-10-25T09:15:55Z |
|
| dc.date.available |
2022-10-25T09:15:55Z |
|
| dc.identifier |
https://dx.doi.org/10.14569/IJACSA.2021.0120295 |
|
| dc.identifier |
https://dspace.nm-aist.ac.tz/handle/20.500.12479/1253 |
|
| dc.identifier.uri |
http://hdl.handle.net/123456789/94692 |
|
| dc.description |
This research article published by the International Journal of Advanced Computer Science and Applications, Vol. 12, No. 2, 2021 |
|
| dc.description |
For many years in the society, farmers rely on
experts to diagnose and detect chicken diseases. As a result,
farmers lose many domesticated birds due to late diagnoses or
lack of reliable experts. With the available tools from artificial
intelligence and machine learning based on computer vision and
image analysis, the most common diseases affecting chicken can
be identified easily from the images of chicken droppings. In this
study, we propose a deep learning solution based on Convolution
Neural Networks (CNN) to predict whether the faeces of chicken
belong to either of the three classes. We also leverage the use of
pre-trained models and develop a solution for the same problem.
Based on the comparison, we show that the model developed
from the XceptionNet outperforms other models for all metrics
used. The experimental results show the apparent gain of transfer
learning (validation accuracy of 94% using pretraining over its
contender 93.67% developed CNN from fully training on the
same dataset). In general, the developed fully trained CNN comes
second when compared with the other model. The results show
that pre-trained XceptionNet method has overall performance
and highest prediction accuracy, and can be suitable for chicken
disease detection application. |
|
| dc.format |
application/pdf |
|
| dc.language |
en |
|
| dc.publisher |
International Journal of Advanced Computer Science and Applications |
|
| dc.subject |
Image classification |
|
| dc.subject |
Convolutional Neural Networks (CNNs) |
|
| dc.subject |
Disease detection |
|
| dc.subject |
Transfer learning |
|
| dc.title |
Deep Convolutional Neural Network for Chicken Diseases Detection |
|
| dc.type |
Article |
|