Deep Learning Based Detection Of Laying Hen Health Status From Excreta Images Using MobileNetV2
Deteksi Status Kesehatan Ayam Petelur Berbasis Deep Learning Dari Citra Ekskreta Menggunakan MobileNetV2
DOI:
https://doi.org/10.53866/jimi.v6i2.1285Keywords:
Convolutional Neural Network, MobileNetV2, Excreta Images, Deep Learning, Poultry DiseasesAbstract
Early and accurate disease detection is a critical challenge in modern poultry farming. This study aimed to develop and evaluate a deep learning-based classification system using MobileNetV2 Convolutional Neural Network (CNN) architecture for automated detection of poultry diseases from excreta images only, and to validate model predictions against laboratory microbiological analyses. A total dataset of 8,087 labeled excreta images was compiled across four health categories: Healthy, Salmonella, Coccidiosis, and Newcastle Disease, and subsequently split into training (6,471) and validation (1,616) subsets at an 80:20 ratio. The MobileNetV2 model was trained over eight epochs with data augmentation strategies and evaluated using precision, recall, F1-score, accuracy, and confusion matrix analysis. The model achieved an overall accuracy of 91%, with the highest per-class F1-score for Coccidiosis (0.97) and the lowest for Newcastle Disease (0.75). The CNN MobileNetV2 architecture demonstrates strong potential for real-time, non-invasive poultry disease monitoring.
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Copyright (c) 2026 Muhammad Nidhomun Ni'am, Sri Widiastuti, Wildan Deni Fahrezi, Thoriqul Irfah Al-Huda, Abdul Karim Muqofi, Rizal Aji Mustofa, Arib Zainul Muafi
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