Poultry Disease Identification Based on Light Weight Deep Neural Networks

Liu, Xiaodan; Zhou, Yinghua; Liu, Yuxiang

doi:10.1109/ccai57533.2023.10201323

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Cited by 3 publications

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Innovative applications of artificial intelligence in zoonotic disease management

Guo,

Lv,

Guo

et al. 2023

Science in One Health

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Innovative applications of artificial intelligence in zoonotic disease management

Guo,

Lv,

Guo

et al. 2023

Science in One Health

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ResPoultry: An Enhanced ResNet50 Model for Multiclass Classification of Poultry Diseases

Kaur,

Kukreja,

Upadhyay

et al. 2024

2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)

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

Nakrosis,

Paulauskaite-Taraseviciene,

Raudonis

et al. 2023

Animals

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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%.

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Poultry Disease Identification Based on Light Weight Deep Neural Networks

Cited by 3 publications

References 10 publications

Innovative applications of artificial intelligence in zoonotic disease management

Innovative applications of artificial intelligence in zoonotic disease management

ResPoultry: An Enhanced ResNet50 Model for Multiclass Classification of Poultry Diseases

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

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