Image - based poultry disease detection using deep convolutional neural network

Abstract

The poultry sector in the country is highly affected by diseases including Coccidiosis, Salmonella and Newcastle that have a significant impact on production. Lack of reliable information and proper methods of farming has led to the spread of the diseases as the majority of farmers practice traditional farming, hence lack a systematic way to detect and diagnose the disorders. Poultry farmers rely on experts to diagnose and detect the diseases; access to the experts is also a challenge due to the limited number of extension officers. With the available tools from artificial intelligence and machine learning, there is a potential to semi-automate the diagnostics process for the most common diseases in chickens. This study proposes a solution for predicting diseases in chickens using faecal images and deep Convolutional Neural Networks (CNN). Additionally, the work leverages the use of pre-trained models and develop the solution for the same problem. Based on the comparison, it is indicated that the model developed from the XceptionNet deep learning framework outperforms other models for all the metrics used. The experimental results indicate the accuracy of transfer learning at 94% using pre-training over the other models from fully training on the same dataset. The results show that the pre-trained XceptionNet framework (94%) has the best overall performance and highest prediction accuracy, and can be suitable for chicken disease detection application. The findings show that the proposed model is ideal for poultry diseases detection method.