Fresh or Rotten? Enhancing Rotten Fruit Detection With Deep Learning and Gaussian Filtering
Leopold Fischer-Brandies, Lucas O. Müller, Justus J. Riegger, Ricardo Buettner
Abstract
We address the pressing issue of food waste by proposing a robust image classification model that can reliably detect rotten fruits. More than half of the fruit yield is lost along the supply chain, with post-harvest losses due to rottenness playing a pivotal role, as even a single decomposing piece can cause huge damage to nearby produce. Our transfer learning-based model uses the ResNet50 convolutional neural network architecture as a binary classification model to distinguish between fresh and rotten fruits. The model performance is enhanced with a Gauss filter and a dropout layer to ensure robustness and prevent overfitting. We achieve high accuracies beyond 99% on unseen test data, setting a new benchmark and outperforming previous efforts. Our work has theoretical and practical implications. To the best of our knowledge, we are the first to explore the use of Gauss filters to preprocess input images in fruit classification. We find that Gauss filters with small kernel sizes improve the performance of our model. Our research can improve post-harvest applications through automation. It can thus help reduce food waste, improve food safety, and reduce costs for growers, distributors, and retailers, thereby improving the overall efficiency of the supply chain.