Image analysis to predict the maturity index of strawberries
Traditionally, strawberries are harvested manually when the typical colour of the cultivar does not reach at least 80% of the surface. The focus of this research activity is to develop an automatic system based on image analysis in order to objectively define the optimal harvest time. Strawberries...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Firenze University Press
2023-02-01
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| Series: | Advances in Horticultural Science |
| Subjects: | |
| Online Access: | https://oaj.fupress.net/index.php/ahs/article/view/13856 |
| _version_ | 1797829289933012992 |
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| author | Antonia Corvino Roberto Romaniello Michela Palumbo Ilde Ricci Maria Cefola Sergio Pelosi Bernardo Pace |
| author_facet | Antonia Corvino Roberto Romaniello Michela Palumbo Ilde Ricci Maria Cefola Sergio Pelosi Bernardo Pace |
| author_sort | Antonia Corvino |
| collection | DOAJ |
| description |
Traditionally, strawberries are harvested manually when the typical colour of the cultivar does not reach at least 80% of the surface. The focus of this research activity is to develop an automatic system based on image analysis in order to objectively define the optimal harvest time. Strawberries (cv. Sabrosa), with different degrees of maturation, were analyzed in four different harvesting periods and subsequently selected and classified, based on the ripening percentage, in three maturity classes: R0-25, R50-70 and R75-100. Each class of 10 strawberries, evaluated in triplicate, was subjected to image analysis and physiological and qualitative evaluation by measuring the following parameters: respiration rate, pH, total soluble solids content, and titratable acidity. The images captured, by a digital camera, were processed using Matlab® software and all the data found were supported by multivariate analysis. The image processing has made it possible to create an algorithm measuring objectively the percentage and the saturation level of red assigning the fruits to each class. Principal component analysis shows that discriminating parameters are the Chroma and the red Area, then used in a Partial Last Square Regression (PLSR) model to predict the TSS/TA ratio with R2 of 0.7 and 0.6 for calibration and validation set, respectively.
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| first_indexed | 2024-04-09T13:18:09Z |
| format | Article |
| id | doaj.art-a5743a7f65e14dfdbeaa686e2de31ea8 |
| institution | Directory Open Access Journal |
| issn | 0394-6169 1592-1573 |
| language | English |
| last_indexed | 2024-04-09T13:18:09Z |
| publishDate | 2023-02-01 |
| publisher | Firenze University Press |
| record_format | Article |
| series | Advances in Horticultural Science |
| spelling | doaj.art-a5743a7f65e14dfdbeaa686e2de31ea82023-05-11T14:31:53ZengFirenze University PressAdvances in Horticultural Science0394-61691592-15732023-02-0137110.36253/ahsc-13856Image analysis to predict the maturity index of strawberriesAntonia Corvino0Roberto Romaniello1Michela Palumbo2Ilde Ricci3Maria Cefola4Sergio Pelosi5Bernardo Pace6Institute of Sciences of Food Production, National Research Council (CNR) c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy.Department of Agriculture, Food and Natural Resources and Engineering, University of Foggia, Via Napoli, 25, 71122 Foggia, Italy.Institute of Sciences of Food Production, National Research Council (CNR) c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy.Institute of Sciences of Food Production, National Research Council (CNR) c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy.Institute of Sciences of Food Production, National Research Council (CNR) c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy.Institute of Sciences of Food Production, National Research Council (CNR) c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy.Institute of Sciences of Food Production, National Research Council (CNR) c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy. Traditionally, strawberries are harvested manually when the typical colour of the cultivar does not reach at least 80% of the surface. The focus of this research activity is to develop an automatic system based on image analysis in order to objectively define the optimal harvest time. Strawberries (cv. Sabrosa), with different degrees of maturation, were analyzed in four different harvesting periods and subsequently selected and classified, based on the ripening percentage, in three maturity classes: R0-25, R50-70 and R75-100. Each class of 10 strawberries, evaluated in triplicate, was subjected to image analysis and physiological and qualitative evaluation by measuring the following parameters: respiration rate, pH, total soluble solids content, and titratable acidity. The images captured, by a digital camera, were processed using Matlab® software and all the data found were supported by multivariate analysis. The image processing has made it possible to create an algorithm measuring objectively the percentage and the saturation level of red assigning the fruits to each class. Principal component analysis shows that discriminating parameters are the Chroma and the red Area, then used in a Partial Last Square Regression (PLSR) model to predict the TSS/TA ratio with R2 of 0.7 and 0.6 for calibration and validation set, respectively. https://oaj.fupress.net/index.php/ahs/article/view/13856Computer vision systemcv. SabrosaFragaria × ananassa Duchharvestmultivariate analysisripening |
| spellingShingle | Antonia Corvino Roberto Romaniello Michela Palumbo Ilde Ricci Maria Cefola Sergio Pelosi Bernardo Pace Image analysis to predict the maturity index of strawberries Advances in Horticultural Science Computer vision system cv. Sabrosa Fragaria × ananassa Duch harvest multivariate analysis ripening |
| title | Image analysis to predict the maturity index of strawberries |
| title_full | Image analysis to predict the maturity index of strawberries |
| title_fullStr | Image analysis to predict the maturity index of strawberries |
| title_full_unstemmed | Image analysis to predict the maturity index of strawberries |
| title_short | Image analysis to predict the maturity index of strawberries |
| title_sort | image analysis to predict the maturity index of strawberries |
| topic | Computer vision system cv. Sabrosa Fragaria × ananassa Duch harvest multivariate analysis ripening |
| url | https://oaj.fupress.net/index.php/ahs/article/view/13856 |
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