A smartphone app that can identify and classify disease from pictures of chicken feces has been developed to aid poultry veterinarians and farmers in making accurate and swift disease diagnoses.
According to a report in Farming Future Food, scientists in Ethiopia have tested a system based on deep-learning algorithms that detect objects and classify images. The algorithms were trained using a dataset of more than 8,000 annotated images of chicken feces, which were classified as healthy, Salmonella, Newcastle disease or coccidiosis.
Investigators at Jimma University found that it identifies areas of interest with an accuracy of 87.48%, and can then classify them with 98.7% accuracy.
The researchers then developed an app called KUKU, which allows farmers and veterinarians easy access to the tool, where they can photograph chicken droppings and have them assessed for signs of the diseases.
Better assistance on farms
“Common poultry disease detection methods include observing the behavior, physical appearance, type of droppings of the birds, and laboratory examination of sample of chicken’s droppings. Some of these methods, however, are prone to human error, while others are difficult to implement on a regular basis,” they explained in the journal Smart Agricultural Technology.