Non-Destructive Estimation of Physicochemical Properties and Detection of Ripeness Level of Apples Using Machine Vision
Nondestructive estimation of physicochemical properties, post-harvest physiology, and level of ripeness of fruits is essential to their automated harvesting, sorting, and handling. Recent research efforts have identified machine vision systems as a promising noninvasive nondestructive tool for explo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | International Journal of Fruit Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15538362.2022.2092580 |
| _version_ | 1811284271868936192 |
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| author | S. Sabzi M. Nadimi Y. Abbaspour-Gilandeh J. Paliwal |
| author_facet | S. Sabzi M. Nadimi Y. Abbaspour-Gilandeh J. Paliwal |
| author_sort | S. Sabzi |
| collection | DOAJ |
| description | Nondestructive estimation of physicochemical properties, post-harvest physiology, and level of ripeness of fruits is essential to their automated harvesting, sorting, and handling. Recent research efforts have identified machine vision systems as a promising noninvasive nondestructive tool for exploring the relationship between physicochemical and appearance characteristics of fruits at various ripening levels. In this regard, the purpose of the current study is to provide an intelligent algorithm for estimating two physical properties including firmness, and soluble solid content (SSC), three chemical properties viz. starch, acidity, and titratable acidity (TA), as well as detection of the ripening level of apples (cultivar Red Delicious) using video processing and artificial intelligence. To this end, videos of apples in orchards at four levels of ripeness were recorded and 444 color and texture features were extracted from these samples. Five physicochemical properties including firmness, SSC, starch, acidity, and TA were measured. Using the hybrid artificial neural network-difference evolution (ANN-DE), six most effective features (one texture and five color features) were selected to estimate the physicochemical properties of apples. The physicochemical estimation was then further optimized using a hybrid multilayer perceptron artificial neural network-cultural algorithm (ANN-CA). The results showed that the coefficient of determinations (R2) related to the prediction models for the physicochemical properties were in excess of 0.92. Additionally, the ripeness level of apples was estimated based on physicochemical properties using a hybrid multilayer perceptron artificial neural network-harmonic search algorithm (ANN-HS) classifier. The developed machine vision system examined ripeness levels of 1356 apples in natural orchard environments and achieved a correct classification rate (CCR) of 97.86%. |
| first_indexed | 2024-04-13T02:25:57Z |
| format | Article |
| id | doaj.art-f1cea576a7ce4ad9a55a9bcb14d5b875 |
| institution | Directory Open Access Journal |
| issn | 1553-8362 1553-8621 |
| language | English |
| last_indexed | 2024-04-13T02:25:57Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Fruit Science |
| spelling | doaj.art-f1cea576a7ce4ad9a55a9bcb14d5b8752022-12-22T03:06:47ZengTaylor & Francis GroupInternational Journal of Fruit Science1553-83621553-86212022-12-0122162864510.1080/15538362.2022.2092580Non-Destructive Estimation of Physicochemical Properties and Detection of Ripeness Level of Apples Using Machine VisionS. Sabzi0M. Nadimi1Y. Abbaspour-Gilandeh2J. Paliwal3Department of Computer Engineering, Sharif University of Technology, Tehran, IranDepartment of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, CanadaDepartment of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, IranDepartment of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, CanadaNondestructive estimation of physicochemical properties, post-harvest physiology, and level of ripeness of fruits is essential to their automated harvesting, sorting, and handling. Recent research efforts have identified machine vision systems as a promising noninvasive nondestructive tool for exploring the relationship between physicochemical and appearance characteristics of fruits at various ripening levels. In this regard, the purpose of the current study is to provide an intelligent algorithm for estimating two physical properties including firmness, and soluble solid content (SSC), three chemical properties viz. starch, acidity, and titratable acidity (TA), as well as detection of the ripening level of apples (cultivar Red Delicious) using video processing and artificial intelligence. To this end, videos of apples in orchards at four levels of ripeness were recorded and 444 color and texture features were extracted from these samples. Five physicochemical properties including firmness, SSC, starch, acidity, and TA were measured. Using the hybrid artificial neural network-difference evolution (ANN-DE), six most effective features (one texture and five color features) were selected to estimate the physicochemical properties of apples. The physicochemical estimation was then further optimized using a hybrid multilayer perceptron artificial neural network-cultural algorithm (ANN-CA). The results showed that the coefficient of determinations (R2) related to the prediction models for the physicochemical properties were in excess of 0.92. Additionally, the ripeness level of apples was estimated based on physicochemical properties using a hybrid multilayer perceptron artificial neural network-harmonic search algorithm (ANN-HS) classifier. The developed machine vision system examined ripeness levels of 1356 apples in natural orchard environments and achieved a correct classification rate (CCR) of 97.86%.https://www.tandfonline.com/doi/10.1080/15538362.2022.2092580Video processingapple ripenessartificial neural networksphysicochemical propertiesnondestructive estimation |
| spellingShingle | S. Sabzi M. Nadimi Y. Abbaspour-Gilandeh J. Paliwal Non-Destructive Estimation of Physicochemical Properties and Detection of Ripeness Level of Apples Using Machine Vision International Journal of Fruit Science Video processing apple ripeness artificial neural networks physicochemical properties nondestructive estimation |
| title | Non-Destructive Estimation of Physicochemical Properties and Detection of Ripeness Level of Apples Using Machine Vision |
| title_full | Non-Destructive Estimation of Physicochemical Properties and Detection of Ripeness Level of Apples Using Machine Vision |
| title_fullStr | Non-Destructive Estimation of Physicochemical Properties and Detection of Ripeness Level of Apples Using Machine Vision |
| title_full_unstemmed | Non-Destructive Estimation of Physicochemical Properties and Detection of Ripeness Level of Apples Using Machine Vision |
| title_short | Non-Destructive Estimation of Physicochemical Properties and Detection of Ripeness Level of Apples Using Machine Vision |
| title_sort | non destructive estimation of physicochemical properties and detection of ripeness level of apples using machine vision |
| topic | Video processing apple ripeness artificial neural networks physicochemical properties nondestructive estimation |
| url | https://www.tandfonline.com/doi/10.1080/15538362.2022.2092580 |
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