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

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%.

Mathematical Formulation of Deep Learning Model for Poultry Disease Classification using EfficientNet- B3 CNN Model

“…Diseases in birds detected using thermal-image processing and AI [13]. An all-inclusive approach that combines many models for classification and segmentation [14]. Although existing technologies are proving to be more effective than human inspection, early detection on farms has not made full use of clinical symptom-based monitoring systems [15].…”

Poultry Disease Identification In Fecal Images Using Vision Transformer

“…The model outperformed advanced alternatives, like Inception V3, ResNet50, and VGG16, showing promise for early and accurate poultry disease diagnosis. similarly, [8] explored AI and computer vision for non-invasive poultry health examinations by classifying chicken droppings based on visual anomalies.…”

Physical and Chemical Characteristics of Droppings as Sensitive Markers of Chicken Health Status

“…The health of chickens can be directly indicated by their droppings, which also serve as an important indicator of illness and digestive health. Potential intestine health issues brought on by bacterial, viral, or parasite diseases as well as nutritional inadequacies can be identified early on by examining the features of chicken droppings [13,14]. Currently, manual observation by veterinarians is used to examine unusual chicken droppings.…”

“…Currently, manual observation by veterinarians is used to examine unusual chicken droppings. Although this method is time-consuming and labor-intensive, the effective development and integration of other technologies, such as automated sensors or vision technologies, into the poultry production chain is still pending [14,15]. Although some farm management practices, such as visual assessment of dropping consistency for diarrhea severity in pig farms, offer a non-invasive method, these subjective scoring systems lack objectivity and consistency [16].…”