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Traditional manual visual grading of fruits has been one of the important challenges faced by the agricultural industry due to its laborious nature as well as inconsistency in inspection and classification process. Automated defects detection using computer vision and machine learning has become a promising area of research with a high and direct impact on the domain of visual inspection. In this study, we propose an efficient and effective machine vision system based on the state-of-the-art deep learning techniques and stacking ensemble methods to offer a non-destructive and cost-effective solution for automating the visual inspection of fruits’ freshness and appearance. We trained, tested and compared the performance of various deep learning models including ResNet, DenseNet, MobileNetV2, NASNet and EfficientNet to find the best model for the grading of fruits. The proposed system also provides a real time …