Salmonella risk prediction in poultry farms via deep learning image classification Models, Cross-Region Validation, and edge computing

Salmonella remains a major cause of foodborne illnesses that are commonly associated with the consumption of contaminated poultry products. Therefore, there is a strong emphasis and a crucial need to reduce Salmonella across the farm to fork continuum. However, there are no easy and rapid on-farm prediction tools to estimate if a flock carries Salmonella . In this study, we used deep learning to develop a Salmonella risk classification model for chicken feces. The model was developed using a two-part framework combining deep learning and tree-based classification techniques. In the first stage, deep learning models such as ResNet152, Xception, VGG19, and others were employed to extract high-level features from chicken fecal images. In the second stage, these features were used by decision tree classifiers including Random Forest and Gradient Boosting for binary classification into Salmonella PCR Positive or Negative. The model was developed using fixed train-validation-test splits on two datasets: an American dataset with 720 images from a controlled multi-serovar Salmonella challenge study and African dataset with 4333 publicly available images. This integrated approach achieved up to 85% classification accuracy when validated across geographical regions. The developed framework was integrated into a user-friendly mobile and web application and allows users to custom train the deep learning classifiers to improve the classification of unseen fecal images. Although the model was primarily validated with the same dataset, this proof-of-concept framework can serve as a decision-support tool for Salmonella control and intervention, contributing to enhanced food safety. Overall, the objective of this study is to develop and validate a hybrid AI approach for Salmonella prediction using fecal images, with explicit cross-region evaluation and proof-of-concept deployment applications.