Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars
The paper focuses on the seasonal oil accumulation in traditional and super-high density (SHD) olive plantations and its modelling employing image-based linear models. For these purposes, at 7-10-day intervals, fruit samples (cultivar Arbequina, Fasola, Frantoio, Koroneiki, Leccino, Maiatica) were p...
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Frontiers Media S.A.
2024-10-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1456800/full |
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| author | Giuseppe Montanaro Antonio Carlomagno Angelo Petrozza Francesco Cellini Ioanna Manolikaki Georgios Koubouris Vitale Nuzzo |
| author_facet | Giuseppe Montanaro Antonio Carlomagno Angelo Petrozza Francesco Cellini Ioanna Manolikaki Georgios Koubouris Vitale Nuzzo |
| author_sort | Giuseppe Montanaro |
| collection | DOAJ |
| description | The paper focuses on the seasonal oil accumulation in traditional and super-high density (SHD) olive plantations and its modelling employing image-based linear models. For these purposes, at 7-10-day intervals, fruit samples (cultivar Arbequina, Fasola, Frantoio, Koroneiki, Leccino, Maiatica) were pictured and images segmented to extract the Red (R), Green (G), and Blue (B) mean pixel values which were re-arranged in 35 RGB-derived colorimetric indexes (CIs). After imaging, the samples were crushed and oil concentration was determined (NIR). The analysis of the correlation between oil and CIs revealed a differential hysteretic behavior depending on the covariates (CI and cultivar). The hysteresis area (Hyst) was then quantified and used to rank the CIs under the hypothesis that CIs with the maximum or minimum Hyst had the highest correlation coefficient and were the most suitable predictors within a general linear model. The results show that the predictors selected according to Hyst-based criteria had high accuracy as determined using a Global Performance Indicator (GPI) accounting for various performance metrics (R2, RSME, MAE). The use of a general linear model here presented is a new computational option integrating current methods mostly based on artificial neural networks. RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the transition of the olive sector towards a digital agriculture domain. |
| format | Article |
| id | doaj-art-31b28047735049618db497bf95d8e7fe |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-31b28047735049618db497bf95d8e7fe2024-11-12T10:08:48ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-10-011510.3389/fpls.2024.14568001456800Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivarsGiuseppe Montanaro0Antonio Carlomagno1Angelo Petrozza2Francesco Cellini3Ioanna Manolikaki4Georgios Koubouris5Vitale Nuzzo6Department of Agricultural, Forest, Food, and Environmental Sciences, Potenza, ItalyDepartment of Agricultural, Forest, Food, and Environmental Sciences, Potenza, ItalyAgenzia Lucana di Sviluppo e Innovazione in Agricoltura (ALSIA) Centro Ricerche Metapontum Agrobios, Metaponto, MT, ItalyAgenzia Lucana di Sviluppo e Innovazione in Agricoltura (ALSIA) Centro Ricerche Metapontum Agrobios, Metaponto, MT, ItalyHellenic Agricultural Organization ELGO-DIMITRA, Institute of Olive Tree, Subtropical Crops and Viticulture, Chania, GreeceHellenic Agricultural Organization ELGO-DIMITRA, Institute of Olive Tree, Subtropical Crops and Viticulture, Chania, GreeceDepartment of Agricultural, Forest, Food, and Environmental Sciences, Potenza, ItalyThe paper focuses on the seasonal oil accumulation in traditional and super-high density (SHD) olive plantations and its modelling employing image-based linear models. For these purposes, at 7-10-day intervals, fruit samples (cultivar Arbequina, Fasola, Frantoio, Koroneiki, Leccino, Maiatica) were pictured and images segmented to extract the Red (R), Green (G), and Blue (B) mean pixel values which were re-arranged in 35 RGB-derived colorimetric indexes (CIs). After imaging, the samples were crushed and oil concentration was determined (NIR). The analysis of the correlation between oil and CIs revealed a differential hysteretic behavior depending on the covariates (CI and cultivar). The hysteresis area (Hyst) was then quantified and used to rank the CIs under the hypothesis that CIs with the maximum or minimum Hyst had the highest correlation coefficient and were the most suitable predictors within a general linear model. The results show that the predictors selected according to Hyst-based criteria had high accuracy as determined using a Global Performance Indicator (GPI) accounting for various performance metrics (R2, RSME, MAE). The use of a general linear model here presented is a new computational option integrating current methods mostly based on artificial neural networks. RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the transition of the olive sector towards a digital agriculture domain.https://www.frontiersin.org/articles/10.3389/fpls.2024.1456800/fullcolorimetric indexeshysteresisOlea europaea L.plantation systemsSHDNIR |
| spellingShingle | Giuseppe Montanaro Antonio Carlomagno Angelo Petrozza Francesco Cellini Ioanna Manolikaki Georgios Koubouris Vitale Nuzzo Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars Frontiers in Plant Science colorimetric indexes hysteresis Olea europaea L. plantation systems SHD NIR |
| title | Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars |
| title_full | Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars |
| title_fullStr | Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars |
| title_full_unstemmed | Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars |
| title_short | Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars |
| title_sort | predicting oil accumulation by fruit image processing and linear models in traditional and super high density olive cultivars |
| topic | colorimetric indexes hysteresis Olea europaea L. plantation systems SHD NIR |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1456800/full |
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