Superior EVOO Quality Production: An RGB Sorting Machine for Olive Classification

Violino, Simona; Moscovini, Lavinia; Costa, Corrado; Re, Paolo Del; Giansante, Lucia; Toscano, Pietro; Tocci, Francesco; Vasta, Simone; Manganiello, Rossella; Ortenzi, Luciano; Pallottino, Federico

doi:10.3390/foods11182917

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…By applying one of the two CNN models on calibrated or non-calibrated images, it is possible to obtain oils of different quality [ 29 ], for example: Superior EVOO (“Top green” olives only) using 17% of total olives; High quality oil obtained by green olives (only “Top green” and “Good green” olives) using 40% of the total olives; High quality oil (produced by only “Top green,” “good green” and “good black” olives) using 72% of the total olives. …”

Section: Discussionmentioning

confidence: 99%

Olive Fruit Selection through AI Algorithms and RGB Imaging

Figorilli

Violino

Moscovini

et al. 2022

Foods

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(1) Background: Extra virgin olive oil production is strictly influenced by the quality of fruits. The optical selection allows for obtaining high quality oils starting from batches with different qualitative characteristics. This study aims to test a CNN algorithm in order to assess its potential for olive classification into several quality classes for industrial purposes, specifically its potential integration and sorting performance evaluation. (2) Methods: The acquired samples were all subjected to visual analysis by a trained operator for the distinction of the products in five classes related to the state of external veraison and the presence of visible defects. The olive samples were placed at a regular distance and in a fixed position on a conveyor belt that moved at a constant speed of 1 cm/s. The images of the olives were taken every 15 s with a compact industrial RGB camera mounted on the main frame in aluminum to allow overlapping of the images, and to avoid loss of information. (3) Results: The modelling approaches used, all based on AI techniques, showed excellent results for both RGB datasets. (4) Conclusions: The presented approach regarding the qualitative discrimination of olive fruits shows its potential for both sorting machine performance evaluation and for future implementation on machines used for industrial sorting processes.

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Section: Discussionmentioning

confidence: 99%

Olive Fruit Selection through AI Algorithms and RGB Imaging

Figorilli

Violino

Moscovini

et al. 2022

Foods

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“…To address this challenge, integrating a computer vision system into olive processing units as part of the automatic separation machinery offers a potential solution. The system consists of an image-capturing unit, which relies on a robust image processing model to ensure rapid and accurate results for mechanical separation (Violino et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages

Saedi,

Rezaei

2023

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

A Modified Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages

Saedi,

Rezaei

2023

Inventions

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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 post-harvest 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 modified by adding some well-known CNN layers to the last layer. 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 in addition to the network stability, 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.

show abstract

Superior EVOO Quality Production: An RGB Sorting Machine for Olive Classification

Cited by 6 publications

References 22 publications

Olive Fruit Selection through AI Algorithms and RGB Imaging

Olive Fruit Selection through AI Algorithms and RGB Imaging

Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages

A Modified Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages

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