DeepFowl: Disease prediction from chicken excreta images using deep learning

Abstract The poultry industry faces numerous obstacles in maintaining the health and well-being of the chicken population due to the widespread occurrence of various diseases. Early detection and timely intervention are crucial to prevent diseases and minimize losses. The advocate solution is the need of the hour to cater to the discussed challenges. Therefore, the authors proposed a novel method called “DeepFowl.” A modified DenseNet169 deep learning-based model has been utilized by DeepFowl to diagnose diseases by analyzing the images of chicken excreta. The DeepFowl method, enhanced by a proposed data oversampling algorithm, has improved disease recognition accuracy in chickens from 96.3 to 98.4%. The model’s performance was evaluated across metrics such as accuracy, recall, precision, and F 1-score, consistently achieving a high value of 98.4%. This demonstrates its effectiveness in early disease detection among chickens. Additionally, DeepFowl supports several sustainable development goals (SDGs): SDG2 (food security and sustainable agriculture), SDG3 (public health), and SDG9 (innovation).