| dc.description |
Coccidiosis, Salmonella, and Newcastle are the common poultry diseases that
curtail poultry production if they are not detected early. In Tanzania, these
diseases are not detected early due to limited access to agricultural support
services by poultry farmers. Deep learning techniques have the potential for
early diagnosis of these poultry diseases. In this study, a deep Convolutional
Neural Network (CNN) model was developed to diagnose poultry diseases by
classifying healthy and unhealthy fecal images. Unhealthy fecal images may be
symptomatic of Coccidiosis, Salmonella, and Newcastle diseases. We collected
1,255 laboratory-labeled fecal images and fecal samples used in Polymerase
Chain Reaction diagnostics to annotate the laboratory-labeled fecal images.
We took 6,812 poultry fecal photos using an Open Data Kit. Agricultural support
experts annotated the farm-labeled fecal images. Then we used a baseline
CNN model, VGG16, InceptionV3, MobileNetV2, and Xception models. We
trained models using farm and laboratory-labeled fecal images and then
fine-tuned them. The test set used farm-labeled images. The test accuracies
results without fine-tuning were 83.06% for the baseline CNN, 85.85% for
VGG16, 94.79% for InceptionV3, 87.46% for MobileNetV2, and 88.27% for
Xception. Finetuning while freezing the batch normalization layer improved
model accuracies, resulting in 95.01% for VGG16, 95.45% for InceptionV3,
98.02% for MobileNetV2, and 98.24% for Xception, with F1 scores for all
classifiers above 75% in all four classes. Given the lighter weight of the trained
MobileNetV2 and its better ability to generalize, we recommend deploying this
model for the early detection of poultry diseases at the farm level. |
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