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

Author:

Nakrosis Arnas1,Paulauskaite-Taraseviciene Agne12ORCID,Raudonis Vidas23,Narusis Ignas1,Gruzauskas Valentas24,Gruzauskas Romas2,Lagzdinyte-Budnike Ingrida1

Affiliation:

1. Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania

2. Artificial Intelligence Centre, Kaunas University of Technology, K. Barsausko 59, 51423 Kaunas, Lithuania

3. Faculty of Electrical and Electronics, Kaunas University of Technology, Studentu 48, 51367 Kaunas, Lithuania

4. Institute of Computer Science, Vilnius University, 08303 Vilnius, Lithuania

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%.

Publisher

MDPI AG

Subject

General Veterinary,Animal Science and Zoology

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Computer Vision-Based cybernetics systems for promoting modern poultry Farming: A critical review;Computers and Electronics in Agriculture;2024-10

2. Poultry Pathogen Detection: Using Deep Learning for Coccidiosis Identification;2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN);2024-07-03

3. Physical and Chemical Characteristics of Droppings as Sensitive Markers of Chicken Health Status;Animals;2024-05-06

4. Deep Learning-Based Poultry Health Diagnosis: Detecting Abnormal Feces and Analyzing Vocalizations;2024 10th International Conference on Applied System Innovation (ICASI);2024-04-17

5. Non-Intrusive Detection of Poultry Diseases through Faecal Analysis using Deep Learning;2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT);2024-03-15

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