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 - Muhammad Nidhomun Ni'am; Sri Widiastuti; Wildan Deni Fahrezi; Thoriqul Irfah Al-Huda; Abdul Karim Muqofi; Rizal Aji Mustofa; Arib Zainul Muafi (DAS Institute, 2026)
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.