Integrating Chicken Fecal Image Analysis with Machine Learning for Early Detection of Poultry Diseases in Developing Countries

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 like Coccidiosis can lower chicken productivity if they are not detected early on. Machine learning, Deep learning algorithms can assist with the early identification of diseases. In this study, a Convolutional Neural Network based framework has been proposed to classify poultry diseases by distinguishing healthy and unhealthy fecal images. Unhealthy images can be a sign of poultry diseases. The Image Classification dataset was used to train a model, and it was discovered that it performed with an accuracy of 84.99%, 90.05% on the training set, testing set respectively. When the proposed network's performance was evaluated, it was discovered that the proposed model was unquestionably the best one for classifying chicken disease. This study explores the benefits of automated chicken disease detection as a function of smart farming in Zambia.