Food Origin Authenticity Using Deep Learning and Citizen Science: Bananas Case Study

This study introduces an Artificial Intelligence (AI)-based proof-of-concept approach to tackle food fraud by using convolutional neural networks (CNNs) and citizen science-generated imagery to predict the country of origin of Cavendish banana cultivars (Musa spp.). A total of 6000 images were collected from iNaturalist, and a CNN classifier was trained to distinguish bananas sourced from six countries. Transfer learning was leveraged, and among nine pre-trained models tested, MobileNetV1 demonstrated the best trade-off between performance and computational efficiency. Following model fine-tuning, data augmentation was implemented to mitigate class imbalance and ensure a dense feature space. The model achieved an average accuracy of 0.86 with Monte Carlo Cross Validation and 0.77 with a 5-Fold Cross Validation. The final selected model attained a validation accuracy of 0.79. Accordingly, this study should be viewed as a foundational proof-of-concept demonstrating the potential of AI for origin detection at the cultivation stage. While the current evaluation framework reflects an early-stage experimental setting, the findings introduce a promising new dimension for proactive food fraud detection. Moving forward, this pipeline provides a foundation that can be expanded and independently validated.