Applying convolutional neural networks to assess the external quality of strawberries

•Convolutional neural networks (CNN) were trained using RGB images of strawberries.•CNN can determine the quality of strawberries with a high accuracy of about 97 %.•The efficient discrimination model was developed at a low cost and in a short time. Deterioration in the appearance of strawberries is attributed to rapid metabolic changes, cellular damage, and softening occurring during their distribution and storage. To quickly and non-destructively monitor the external quality of strawberries, recognition models based on 750 Red Green Blue (RGB) image classifications and using convolutional neural networks (CNNs) were developed. A model using eight configurations was used to compare the discrimination accuracies according to the following variables: training–test set distributions, number of learning images, and number of epochs. Strawberry samples were classified as fresh, bruised, or moldy. According to our validation results, training the model with 90 % of the image data ensured a high learning performance. Using our test dataset, we found that the accuracy, precision, specificity, and sensitivity of the model reached 97 %. In the feature map derived from convolutional layers, the bruised and moldy areas of the strawberry were also identified. Together, these results suggest that CNNs have potential use in the non-destructive monitoring of quality changes in the food industry.