Saffron Crocus Sativus L. is among the most valuable cash crops globally, prized for its distinct color, aroma, and flavor, making it widely used in culinary and medicinal applications. However, the increasing global demand for saffron has also heightened the risk of adulteration, posing a significant challenge in its marketing and authenticity verification. Traditional methods for detecting saffron adulteration are expensive, time-consuming, and require specialized skills and advanced technology. This research proposes a machine vision-based, non-destructive saffron adulteration prediction system utilizing a Deep Learning (DL) based convolutional neural network model trained on RGB images of both genuine and adulterated saffron samples. The proposed model achieved an accuracy of 99.12 \(\%\) , outperforming well-known transfer learning models such as ResNet50, InceptionNet, and VGG16. To enhance interpretability, explainable artificial intelligence techniques, including SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), are employed to highlight the key features influencing the model’s predictions.