Use of Ad Index to Define Harvest Time and Characterize Ripening Variability at Harvest in 'Gala' Apple

Costamagna, F.; Giordani, Luca; Costa, Guglielmo; Noferini, M.

doi:10.17660/actahortic.2013.998.12

Cited by 30 publications

(20 citation statements)

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“…IAD was also strongly correlated to SPI (r 2 = 0.9), with moderate correlations being observed for FFF (r 2 = 0.7) in cv. 'Gala' apples grown in Piedmont [28]. However, in the study done in the same region in another season, IAD did not show any significant correlations with FFF and SPI [29].…”

mentioning

confidence: 51%

“…Similarly, a linear trend was observed in cv. 'Gala' grown in Piedmont [28]. Others observed values stabilizing at around 0.2 for the cv.…”

Section: Kinetics Of Iad In Relation To Starch Pattern Index and Fruimentioning

confidence: 95%

“…Similar temporal differences in the distribution of IAD, SPI, and FFF during ripening were also observed in cv. 'Gala' from Piedmont [28]. Based on these observations of heterogeneity differences, it is very likely that any potential correlations between the abovementioned parameters would be characterized by large confidence intervals.…”

Section: Kinetics Of Iad In Relation To Starch Pattern Index and Fruimentioning

confidence: 95%

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A Study on the Potential of IAD as a Surrogate Index of Quality and Storability in cv. ‘Gala’ Apple Fruit

Sadar

Zanella

2019

Agronomy

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The decline of relative chlorophyll contents during fruit ripening is considered to be an important indicator of fruit physiological condition. The recent availability of low-cost portable visible spectrum (VIS) spectrometers has spurred research interest towards optical sensing of chlorophyll changes in intact fruit, with many scientists attempting to link the shifts in optical signals, attributed to chlorophyll changes, to different maturity and quality parameters. One of the widely available portable devices for non-destructive estimation of relative chlorophyll contents is the DA meter, which provides a maturity index that is calculated as a difference between absorption at 670 nm (near the chlorophyll-a absorption peak) and 720 nm (background of the spectrum), abbreviated as IAD. In the present study, the evolution of IAD and its relation to starch pattern index (SPI) and fruit flesh firmness (FFF) was monitored in fruit of two cv. 'Gala' clones during maturation and storage, aiming to identify a potential existence of a usable IAD range for the assessment and prediction of the optimal harvest window and storage potential. In both clones, canopy positions, fruit sides, and seasons IAD, SPI, and FFF generally changed in a linear fashion over time, but with partially very different slopes, i.e., they were changing at different rates. What all of these parameters had in common was the presence of a very high biological variability, which is typical of apple fruit. Significantly powerful estimations of SPI (r 2 > 0.7, p < 0.005) and pre-and post-storage FFF (r 2 > 0.6, p < 0.005) were achieved. However, the very large biological variability could not be neutralized, which means that the predictions always included large confidence intervals of up to 0.46-0.59 units for SPI and 0.82-1.1 kgF FFF, which ultimately makes them unusable for practical applications. Experiments done under real-life conditions in a commercial fruit storage facility on several different fruit batches confirmed that IAD measured at harvest cannot be used indiscriminately for predicting post-storage FFF of cv. 'Gala' originating from different orchards. Nevertheless, mean IAD values that were obtained at optimal maturity from samples of the same orchards remained stable over seasons (0.8-1.2), which strongly suggests that, provided that the calibrations and validations are not only cultivar, but also orchard-specific, IAD has a potential for estimating maturity and storability of apple fruit. In this case, IAD could replace standard maturity indices, otherwise it would be suited for use as a supplementary index for determining fruits physiological maturity status.Agronomy 2019, 9, 642 2 of 16 they are based on destructive analyses, with the obtained data presenting low repeatability and high vulnerability to interpretation errors [1,2].In recent years, various methods for quality and ripeness evaluation of intact agricultural products have been developed [3][4][5][6][7]. A potential technique for assessing fruits physiological stage is to m...

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mentioning

confidence: 51%

“…Similarly, a linear trend was observed in cv. 'Gala' grown in Piedmont [28]. Others observed values stabilizing at around 0.2 for the cv.…”

Section: Kinetics Of Iad In Relation To Starch Pattern Index and Fruimentioning

confidence: 95%

Section: Kinetics Of Iad In Relation To Starch Pattern Index and Fruimentioning

confidence: 95%

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A Study on the Potential of IAD as a Surrogate Index of Quality and Storability in cv. ‘Gala’ Apple Fruit

Sadar

Zanella

2019

Agronomy

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“…"Plus Plus" peach fruit segregated with the DA-stationary machine into 4 ripening classes, the % of the fruit in each class, and consumer likelihood of purchasing each class. For pome fruit, the I AD was also used to define the proper harvest time of several apple varieties, including "Gala", "Golden Delicious", "Red Delicious", "Fuji", "Granny Smith", and "Pink Lady" [18][19][20] and of "Abbé Fétel"pear [5]. A consumer test trial performed with "Gala" apples indicated that the most desirable fruit were harvested at an I AD value of 0.6-0.9 [19].…”

Section: Assessing Proper Harvest Timementioning

confidence: 99%

“…For pome fruit, the I AD was also used to define the proper harvest time of several apple varieties, including "Gala", "Golden Delicious", "Red Delicious", "Fuji", "Granny Smith", and "Pink Lady" [18][19][20] and of "Abbé Fétel"pear [5]. A consumer test trial performed with "Gala" apples indicated that the most desirable fruit were harvested at an I AD value of 0.6-0.9 [19]. Using "Abbé Fètel" pear, a consumer test trial performed with more than 100 people at the "AGER-Innovapero" Conference (Ferrara, Italy, 18 October 2013) [21] at the end of the storage period showed that the most desirable fruit were those harvested when the I AD had reached value of 1.8-1.9, but when the I AD value was >2 the fruit were not as desirable to consumers.…”

Section: Assessing Proper Harvest Timementioning

confidence: 99%

Use of Nondestructive Devices to Support Pre- and Postharvest Fruit Management

et al. 2016

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Fruit quality is greatly affected by the ripening stage at harvest. In order to preserve quality traits, increase product marketability, and extend both the storage time and the shelf life, it is crucial to tailor postharvest strategies to ripening and physiological stages, and these need to be determined precisely. Surveying instruments working with modern technologies such as visible spectrometry can be precise and effective in assessing ripening stage and in grouping fruit in homogeneous classes. This paper reviews results using original nondestructive devices developed at the University of Bologna to define the fruit ripening of several fruit species through a new index (Index of Absorbance Difference (I AD )) to compare relationships among fruit ripening stage, fruit quality, and postharvest life. The devices defining the I AD can be used in the field (by the Difference Absorbance (DA)-Meter), at the packinghouse (with the DA-head, a stationary device), and at the cold storage level (with the DA Fruit Logger (DAFL)).

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Destructive and non-destructive measurement approaches and the application of AI models in precision agriculture: a review

Islam,

Bijjahalli,

Fahey

et al. 2024

Precision Agric

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The estimation of pre-harvest fruit quality and maturity is essential for growers to determine the harvest timing, storage requirements and profitability of the crop yield. In-field fruit maturity indicators are highly variable and require high spatiotemporal resolution data, which can be obtained from contemporary precision agriculture systems. Such systems exploit various state-of-the-art sensors, increasingly relying on spectrometry and imaging techniques in association with advanced Artificial Intelligence (AI) and, in particular, Machine Learning (ML) algorithms. This article presents a critical review of precision agriculture techniques for fruit maturity estimation, with a focus on destructive and non-destructive measurement approaches, and the applications of ML in the domain. A critical analysis of the advantages and disadvantages of different techniques is conducted by surveying recent articles on non-destructive methods to discern trends in performance and applicability. Advanced data-fusion methods for combining information from multiple non-destructive sensors are increasingly being used to develop more accurate representations of fruit maturity for the entire field. This is achieved by incorporating AI algorithms, such as support vector machines, k-nearest neighbour, neural networks, and clustering. Based on an extensive survey of recently published research, the review also identifies the most effective fruit maturity indices, namely: sugar content, acidity and firmness. The review concludes by highlighting the outstanding technical challenges and identifies the most promising areas for future research. Hence, this research has the potential to provide a valuable resource for the growers, allowing them to familiarize themselves with contemporary Smart Agricultural methodologies currently in use. These practices can be gradually incorporated from their perspective, taking into account the availability of non-destructive techniques and the use of efficient fruit maturity indices.

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Use of Ad Index to Define Harvest Time and Characterize Ripening Variability at Harvest in 'Gala' Apple

Cited by 30 publications

References 0 publications

A Study on the Potential of IAD as a Surrogate Index of Quality and Storability in cv. ‘Gala’ Apple Fruit

A Study on the Potential of IAD as a Surrogate Index of Quality and Storability in cv. ‘Gala’ Apple Fruit

Use of Nondestructive Devices to Support Pre- and Postharvest Fruit Management

Destructive and non-destructive measurement approaches and the application of AI models in precision agriculture: a review

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