10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
Authors - Given Sichilima, Jackson Phiri Abstract - the health and productivity of poultry farms are significantly impacted by the timely detection of diseases within chicken houses. Manual disease monitoring in poultry is laborious and prone to errors, underscoring the need for sustainable, efficient, reliable, and cost-effective farming practices. The adoption of advanced technologies, such as artificial intelligence (AI), is essential to address this need. Smart farming solutions, particularly machine learning, have proven to be effective predictive analytical tools for large volumes of data, finding applications in various domains including medicine, finance, and sports, and now increasingly in agriculture. Poultry diseases, including coccidiosis, can significantly impact chicken productivity if not identified early. Machine learning and deep learning algorithms can facilitate earlier detection of these diseases. This study introduces a framework that employs a Convolutional Neural Network (CNN) to classify poultry diseases by examining fecal images to distinguish between healthy and unhealthy samples. Unhealthy fecal images may indicate the presence of disease. An image classification dataset was utilized to train the model, which achieved an accuracy of 84.99% on the training set and 90.05% on the testing set. The evaluation indicated that this model was the most effective for classifying chicken diseases. This research underscores the benefits of automated disease detection within smart farming practices in Zambia.
