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Poultry Disease Classification Using EfficientNetV2-L and MobileNetV2 Based on Fecal Images
Corresponding Author(s) : Rosida Vivin Nahari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 3, August 2026 (Article in Progress)
Abstract
The timely identification of poultry diseases is essential for maintaining high livestock yields and curbing the transmission of infections, which directly supports global food security. The integration of multi-layered neural architectures has advanced this diagnostic process, offering an innovative approach to automated health monitoring through enhanced classification accuracy. By employing Convolutional Neural Networks (CNNs), the extraction of discriminative features from fecal images is automated, providing a scalable and robust approach to sustainable agriculture. This study proposes poultry disease classification using two CNN architectures, EfficientNetV2-L and MobileNetV2, trained under three scenarios: baseline, class weights, and Focal Loss. Using a dataset of 6,812 chicken fecal images, the experimental results demonstrate that applying Focal Loss significantly improves performance across all metrics. The EfficientNetV2-L model with Focal Loss achieved superior results, with 99.51% accuracy, 99.57% precision, 99.51% recall, and 99.52% F1-score. Meanwhile, MobileNetV2 performed reasonably well with a faster training time, making it suitable for resource-constrained environments. These findings indicate that combining Focal Loss with efficient CNN architectures enhances the classification of imbalanced datasets and provides a promising technological solution for real-time poultry disease detection systems.
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- H. Lee, Y. S. Park, S. Yang, H. Lee, T. J. Park, and D. Yeo, “A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation,” Applied Sciences (Switzerland), vol. 14, no. 10, May 2024. https://doi.org/10.3390/app14104322
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References
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D. Machuve, E. Nwankwo, N. Mduma, and J. Mbelwa, “Poultry diseases diagnostics models using deep learning,” Lyndon Estes, 2022.
M. Li, Y. Jiang, Y. Zhang, and H. Zhu, “Medical image analysis using deep learning algorithms,” Front Public Health, vol. 11, Nov. 2023. https://doi.org/10.3389/fpubh.2023.1273253
Y. Zhang, J. M. Gorriz, and Z. Dong, “Deep learning in medical image analysis,” J Imaging, vol. 7, no. 4, p. NA, Apr. 2021. https://doi.org/10.3390/jimaging7040074
X. Liu, L. Song, S. Liu, and Y. Zhang, “A review of deep-learning-based medical image segmentation methods,” Sustainability (Switzerland), vol. 13, no. 3, p. 29, Jan. 2021. https://doi.org/10.3390/su13031224
L. Dumortier, F. Guépin, M. L. Delignette-Muller, C. Boulocher, and T. Grenier, “Deep learning in veterinary medicine, an approach based on CNN to detect pulmonary abnormalities from lateral thoracic radiographs in cats,” Sci Rep, vol. 12, no. 11418, Jul. 2022. https://doi.org/10.1038/s41598-022-14993-2
A. I. Pereira et al., “Artificial Intelligence in Veterinary Imaging: An Overview,” Vet Sci, vol. 10, no. 5, Apr. 2023, doi: 10.3390/vetsci10050320.
X. Sun, G. Li, P. Qu, X. Xie, X. Pan, and W. Zhang, “Research on plant disease identification based on CNN,” Cognitive Robotics, vol. 2, Jul. 2022. https://doi.org/10.1016/j.cogr.2022.07.001
M. Mahmood ur Rehman, J. Liu, A. Nijabat, M. Faheem, W. Wang, and S. Zhao, “Leveraging Convolutional Neural Networks for Disease Detection in Vegetables: A Comprehensive Review,” Agronomy, vol. 14, no. 10, Sep. 2024. https://doi.org/10.3390/agronomy14102231
R. W. Bello, R. O. Ogundokun, P. A. Owolawi, E. A. van Wyk, and C. Tu, “Application of Convolutional Neural Networks in Animal Husbandry: A Review,” Mathematics, vol. 13, no. 12, Jun. 2025. https://doi.org/10.3390/math13121906
J. Liang, W. Cai, Z. Xu, G. Zhou, J. Li, and Z. Xiang, “A Fine-Grained Image Classification Approach for Dog Feces Using MC-SCMNet under Complex Backgrounds,” Animals, vol. 13, no. 10, May 2023. https://doi/org/10.3390/ani13101660
A. Dhungana, X. Yang, B. Paneru, S. Dahal, G. Lu, and L. Chai, “An Integrated Deep Learning Approach for Poultry Disease Detection and Classification Based on Analysis of Chicken Manure Images,” AgriEngineering, vol. 7, no. 9, Aug. 2025. https://doi.org/10.3390/agriengineering7090278
I. Naseer, S. Akram, T. Masood, A. Jaffar, M. A. Khan, and A. Mosavi, “Performance Analysis of State-of-the-Art CNN Architectures for LUNA16,” Sensors, vol. 22, no. 12, Jun. 2022. https://doi.org/10.3390/s22124426
N. Aziz, N. Minallah, J. Frnda, M. Sher, M. Zeeshan, and A. H. Durrani, “Precision meets generalization: Enhancing brain tumor classification via pretrained DenseNet with global average pooling and hyperparameter tuning,” PLoS One, vol. 19, no. 9, Sep. 2024. https://doi.org/10.1371/journal.pone.0307825
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M. Tan and Q. V Le, “EfficientNetV2: Smaller Models and Faster Training,” in Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 2021, 2021. https://doi.org/10.48550/arXiv.2104.00298
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J. H. Joloudari, A. Marefat, M. A. Nematollahi, S. S. Oyelere, and S. Hussain, “Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks,” Applied Sciences (Switzerland), vol. 13, no. 6, Mar. 2023. https://doi.org/10.3390/app13064006
M. Yeung, E. Sala, C. B. Schönlieb, and L. Rundo, “Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation,” Computerized Medical Imaging and Graphics, vol. 95, Dec. 2022. https://doi.org/10.1016/j.compmedimag.2021.102026
K. Kannan and Plutoze, “Poultry Diseases Detection.” Accessed: Oct. 22, 2025. https://www.kaggle.com/datasets/kausthubkannan/poultry-diseases-detection
M. Sivakumar, S. Parthasarathy, and T. Padmapriya, “Trade-off between training and testing ratio in machine learning for medical image processing,” PeerJ Comput Sci, vol. 10, Sep. 2024. https://doi.org/10.7717/PEERJ-CS.2245
M. M. Musthafa, M. T R, V. K. V, and S. Guluwadi, “Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification,” BMC Med Imaging, vol. 24, no. 201, Aug. 2024. https://doi.org/10.1186/s12880-024-01356-8
H. Lee, Y. S. Park, S. Yang, H. Lee, T. J. Park, and D. Yeo, “A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation,” Applied Sciences (Switzerland), vol. 14, no. 10, May 2024. https://doi.org/10.3390/app14104322
T. Ekmekyapar and B. Taşcı, “Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis,” Diagnostics, vol. 13, no. 19, Sep. 2023. https://doi.org/10.3390/diagnostics13193030
X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, and M. Parmar, “A review of convolutional neural networks in computer vision,” Artif Intell Rev, vol. 57, no. 99, Mar. 2024. https://doi.org/10.1007/s10462-024-10721-6
Z. Tao, C. Xiaoyu, L. Huiling, Y. Xinyu, L. Yuncan, and Z. Xiaomin, “Pooling Operations in Deep Learning: From ‘Invariable’ to ‘Variable,’” Biomed Res Int, vol. 2022, no. 4067581, Jun. 2022. https://doi.org/10.1155/2022/4067581
D. Machuve, E. Nwankwo, N. Mduma, and J. Mbelwa, “Poultry diseases diagnostics models using deep learning,” vol. 5, Aug. 2022. https://doi.org/10.3389/frai.2022.733345