Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification

SIMPLE SUMMARY: The integration of artificial intelligence and advanced computer vision techniques holds significant promise for non-invasive health assessments within the poultry industry. Monitoring poultry health through droppings can provide valuable insights as alterations in texture and color may signal the presence of severe and contagious illnesses. This study, in contrast to previous research that often employed binary or limited multi-class classifications for droppings, employs image processing algorithms to categorize droppings into six distinct classes, each representing various abnormality levels, with data collected from three different poultry farms in Lithuania, including diverse litter types. ABSTRACT: The use of artificial intelligence techniques with advanced computer vision techniques offers great potential for non-invasive health assessments in the poultry industry. Evaluating the condition of poultry by monitoring their droppings can be highly valuable as significant changes in consistency and color can be indicators of serious and infectious diseases. While most studies have prioritized the classification of droppings into two categories (normal and abnormal), with some relevant studies dealing with up to five categories, this investigation goes a step further by employing image processing algorithms to categorize droppings into six classes, based on visual information indicating some level of abnormality. To ensure a diverse dataset, data were collected in three different poultry farms in Lithuania by capturing droppings on different types of litter. With the implementation of deep learning, the object detection rate reached 92.41% accuracy. A range of machine learning algorithms, including different deep learning architectures, has been explored and, based on the obtained results, we have proposed a comprehensive solution by combining different models for segmentation and classification purposes. The results revealed that the segmentation task achieved the highest accuracy of 0.88 in terms of the Dice coefficient employing the K-means algorithm. Meanwhile, YOLOv5 demonstrated the highest classification accuracy, achieving an ACC of 91.78%.