Saedi, S.I.; Rezaei, M. A Modified Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages. Inventions2024, 9, 6.
Saedi, S.I.; Rezaei, M. A Modified Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages. Inventions 2024, 9, 6.
Saedi, S.I.; Rezaei, M. A Modified Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages. Inventions2024, 9, 6.
Saedi, S.I.; Rezaei, M. A Modified Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages. Inventions 2024, 9, 6.
Abstract
Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate postharvest processing. This study introduces an automatic computer vision system that utilizes deep learning technology to classify the `Roghani` Iranian olive cultivar into five ripening stages using color images. The developed model employs convolutional neural networks (CNN) and transfer learning based on the Xception architecture and ImageNet weights as the base network. The model was fine-tuned by testing multiple configurations of well-known CNN layers. To minimize overfitting and enhance model generality, data augmentation techniques were employed. By considering different optimizers and two image sizes, four final candidate models were generated. These models were then compared in terms of loss and accuracy on the test dataset, classification performance (classification report and confusion matrix), and generality. All four candidates exhibited high accuracies ranging from 86.93% to 93.46% and comparable classification performance. In all models, at least one class was recognized with 100% accuracy. However, by taking into account the risk of overfitting, two models were discarded. Finally, a model with an image size of 224 × 224 and an SGD optimizer, which had a loss of 1.23 and an accuracy of 86.93%, was selected as the preferred option. The results of this study offer robust tools for automatic olive sorting systems, simplifying the differentiation of olives at various ripening levels for different post-harvest products.
Keywords
Olive; Color Image; Xception; Sorting; Deep Learning
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
