| Summary: | The frequency at which diseases occur in poultry nowadays is staggering. Poultry diseases, such as Newcastledisease, Avian Influenza etc. usually brings about serious economic losses to poultry business owners and also tofarm produce consumers. Prompt warning and identification of emerging poultry disease outbreaks is of utmostimportance in the poultry business. Digital imaging technology and machine learning algorithms have made roomfor the effective observation / monitoring of poultry health status via surveillance cameras online and in real timehas proven to be an effective way to prevent large-scale outbreaks of diseases. To analyze the images of healthyand diseased birds, images of healthy birds were taken directly from poultry farms using different camera devicessuch as Digital cameras, Mobile Phones etc. The first step we took was to transform the images into a fixed sizedlength of dimension (64, 64, 3). The images were then augmented. Firstly, to help increase the size of the dataset,Secondly, to create variations that will better capture reality, so as to increase the ability of the model to generalizebetter and predict out of sample data more accurately. Other augmentation carried out on the training set includeScaling, Rotation, Shifting, Zooming and Flipping (horizontally). Using the Models implemented in this research,accuracy rates of 95% and 98% are obtained. The results show that digital image processing and the machinelearning algorithm implemented in this research can effectively detect and classify sick/diseased birds fromhealthy Birds whilst giving high accuracy and good performance which will aid in giving early warning signals.This research can serve as a reference for the intelligent detection and classification / identification of sick birdsfrom healthy birds.
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