| Autor: |
Supriyanto, Eko, Isnanto, R. Rizal, Purnomo, Sutrisno Hadi |
| Zdroj: |
Ingénierie des Systèmes d'Information; Dec2024, Vol. 29 Issue 6, p2525-2532, 8p |
| Abstrakt: |
There is a critical need for the early detection of disease in order to mitigate its effects on chicken populations and prevent its transmission to other chickens. Nevertheless, farmers frequently need more efficiency and accuracy in their manual chicken health monitoring. Especially on large-scale farms, visual observation of disease symptoms is frequently either inaccurate or too late. Therefore, this research aims to develop a system for non-invasive monitoring of chicken health conditions using CNN-SVM image classification techniques based on RGB and infrared images in the chicken coop. The DenseNet121 architecture yielded the most promising feature extraction results, with an accuracy level of 83-98%, as indicated by the results of the tests. Furthermore, the model can accurately identify and classify chicken health images with a 93.6% accuracy rate during the evaluation process. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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