Abstract:
Chicken is the most famous meat in society and the apparently the most salable
meat in the markets. The most of the farmers are raising chickens to get eggs
and meat for commercial purposes. There is a disease called Coryza which is
infected to the chicken’s eyes hardly. As a result, the chicken must be blind as
well as they cannot eat well, and finally Coryza can destroy their lives.
Therefore, the production of meat and eggs must be decreased. This study is
mainly focused on the identification of Coryza-infected chickens using image
processing techniques. 800 images of Coryza infected and well-being chickens
are captured as the training dataset and 200 images of chickens as the testing
dataset. Then image preprocessing and feature extraction are performed.
Mutated eyes, which are extracted as features, are difficult to identify from
images with complex backgrounds. The image preprocessing techniques are
used to overcome that issue. A classification model is developed using
MobilenetV2 in a convolutional neural network and the developed model is
trained using training dataset with accuracy of 91%. The model is able to
predict any given image as infected or well-being chicken with the average
testing accuracy of 76%. After the identification process, if it is an infected
chicken, relevant medicines are suggested to the farmers as the final outcome
of this research. It will be more helpful to maintain sustainable eggs and meat
production for a commercial purpose.
