糞便画像を用いた家禽疾患の早期検出のためのアンサンブルベースの深層学習アプローチ

1. Early identification of various poultry illnesses may be possible with deep learning techniques. Salmonellosis, newcastle disease and coccidiosis illnesses can present poor faecal scores as symptoms. A deep convolutional neural network (CNN) model known as an 'ensemble-based CNN' was constructed to identify poultry diseases by classifying healthy and unhealthy faecal images and tested on farm.2. Four distinct deep learning models; MobileNetV2, EfficientNetB0, Xception and ResNet50V2, were compared and their corresponding inference findings recorded. To identify the best results, ensemble techniques and transfer learning, using a fine-tuning approach, were applied.3. Evaluation scores for accuracy, precision and recall were used to assess the four models to determine which performed best.4. The sum-ensemble approach and weighted average ensemble algorithm achieved 99% and the majority voting ensemble achieved 98.5% accuracy, respectively.5. The Xception model outperformed the three other models in identifying type of disease. Therefore, Xception was deemed the preferred model when accuracy is the main concern whereas the ensemble approach enhanced the performance of identification.