Reliability of a Handheld Bluetooth Colourimeter and Its Application to Measuring the Effects of Time from Harvest, Row Orientation and Training System on Nectarine Skin Colour

Scalisi, Alessio; O’Connell, M.; Pelliccia, Daniele; Plozza, Tim; Frisina, C.; Chandra, Subhash; Goodwin, Ian

doi:10.3390/horticulturae7080255

Cited by 7 publications

(8 citation statements)

References 22 publications

(33 reference statements)

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“…McGuire (1992) highlighted how the use of CIELab and/or LCh color attributes guarantees an objective color measure. Literature showed that h is associated with redness and maturity in peach and nectarine (Ferrer et al 2005;Robertson et al 1990;Scalisi et al 2020Scalisi et al , 2021a. Our results showed good accuracy of h predictions in peach and nectarine (Fig.…”

Section: Discussionsupporting

confidence: 71%

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A Ground-based Platform for Reliable Estimates of Fruit Number, Size, and Color in Stone Fruit Orchards

Islam

Scalisi

O’Connell

et al. 2022

hortte

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Automatic in-field fruit recognition techniques can be used to estimate fruit number, fruit size, fruit skin color, and yield in fruit crops. Fruit color and size represent two of the most important fruit quality parameters in stone fruit (Prunus sp.). This study aimed to evaluate the reliability of a commercial mobile platform, sensors, and artificial intelligence software system for fast estimates of fruit number, fruit size, and fruit skin color in peach (Prunus persica), nectarine (P. persica var. nucipersica), plum (Prunus salicina), and apricot (Prunus armeniaca), and to assess their spatial and temporal variability. An initial calibration was needed to obtain estimates of absolute fruit number per tree and a forecasted yield. However, the technology can also be used to produce fast relative density maps in stone fruit orchards. Fruit number prediction accuracy was ≥90% in all the crops and training systems under study. Overall, predictions of fruit number in two-dimensional training systems were slightly more accurate. Estimates of fruit diameter (FD) and color did not need an initial calibration. The FD predictions had percent standard errors <10% and root mean square error <5 mm under different training systems, row spacing, crops, and fruit position within the canopy. Hue angle, a color attribute previously associated with fruit maturity in peach and nectarine, was the color attribute that was best predicted by the mobile platform. A new color parameter—color development index (CDI), ranging from 0 to 1—was derived from hue angle. The adoption of CDI, which represents the color progression or distance from green, improved the interpretation of color measurements by end-users as opposed to hue angle and generated more robust color estimations in fruit that turn purple when ripe, such as dark plum. Spatial maps of fruit number, FD, and CDI obtained with the mobile platform can be used to inform orchard decisions such as thinning, pruning, spraying, and harvest timing. The importance and application of crop yield and fruit quality real-time assessments and forecasts are discussed.

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Section: Discussionsupporting

confidence: 71%

“…1C). The colorimeter generated different color attributes in the CIELab/LCh color wheel (i.e., L*, a*, b*, C*, and h ), as previously shown by Scalisi et al (2021a). Color was predicted with the mobile platform within the same bounding boxes used for fruit number and diameter predictions.…”

Section: Estimations Of Crop Parametersmentioning

confidence: 99%

A Ground-based Platform for Reliable Estimates of Fruit Number, Size, and Color in Stone Fruit Orchards

Islam

Scalisi

O’Connell

et al. 2022

hortte

View full text Add to dashboard Cite

show abstract

“…The a* value corresponds to the degree of red or green color; the -a* value is green, and the a* value is red. Specifically, a* and hue angle are two good indicators of fruit maturity in peach [41], nectarine [42], and most fruits that turn from green to red. CIELab color parameters can be derived from RGB by image processing.…”

Section: Physical Propertiesmentioning

confidence: 99%

Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach

2022

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Most mango farms classify the maturity stage manually by trained workers using external indicators such as size, shape, and skin color, which can lead to human error or inconsistencies. We developed four common machine learning (ML) classifiers, the k-mean, naïve Bayes, support vector machine, and feed-forward artificial neural network (FANN), all of which were aimed at classifying the ripeness stage of mangoes at harvest. The ML classifiers were trained on biochemical data and then tested on physical and electrical data.The performance of the ML models was compared using fourfold cross validation. The FANN classifier performed the best, with a mean accuracy of 89.6% for unripe, ripe, and overripe classes, when compared to the other classifiers.

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“…The use of colourimeters that measure colour in different colour spaces (e.g., RGB, CMYK, XYZ CIELab/LCh) can be found in the relevant literature, but they often required contact devices such as portable spectrometers [2]. For example, a* and hue angle were successfully linked to maturity in peach and nectarine [3][4][5][6][7], and hue angle was related to harvest time in 'Majestic Pearl' nectarines in a recent study [8]. These devices have proved reliable and useful for objectively measuring skin colour in different crops.…”

Section: Introductionmentioning

confidence: 99%

“…Although Cartographer has been validated and used for measurements of fruit number, fruit colour and size in peach and nectarine cultivars [13], the consistency of fruit colour measurements in different light environments or time of the day, and the relationship between fruit colour and ripeness or harvest time need to be investigated to determine if scanning the orchards can provide a valuable tool to plan harvest time based on objective fruit colour thresholds. This work aimed to (i) derive a fruit skin colour development index (CDI, ranging from 0 to 1) that can be easily calculated from hue angle data-a CIELab colour attribute that was successfully related to harvest time and maturity in stone fruit [3][4][5][6][7][8] using a fast-scanning mobile platform; (ii) determine the temporal variability of CDI readings when measurements are collected at different times of the day thereby providing changing light environments on different sides of the canopy; and (iii) test the relationship between the CDI and a conventional fruit maturity index used in peach and nectarine. One of our propositions was that collecting big data on the entire fruit population in an orchard block would offset the need to separate background and foreground skin colour for maturity estimation; thus, our premise is that the overall colour estimated on large samples of fruit in the orchard can support harvest decision making.…”

Section: Introductionmentioning

confidence: 99%

A Fruit Colour Development Index (CDI) to Support Harvest Time Decisions in Peach and Nectarine Orchards

et al. 2022

Self Cite

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Fruit skin colour is one of the most important visual fruit quality parameters driving consumer preferences. Proximal sensors such as machine vision cameras can be used to detect skin colour in fruit visible in collected images, but their accuracy in variable orchard light conditions remains a practical challenge. This work aimed to derive a new fruit skin colour attribute—namely a Colour Development Index (CDI), ranging from 0 to 1, that intuitively increases as fruit becomes redder—to assess colour development in peach and nectarine fruit skin. CDI measurements were generated from high-resolution images collected on both east and west sides of the canopies of three peach and one nectarine cultivars using the commercial mobile platform Cartographer (Green Atlas). Fruit colour (RGB values) was extracted from the central pixels of detected fruit and converted into a CDI. The repeatability of CDI measurements under different light environments was tested by scanning orchards at different times of the day. The effects of cultivar and canopy side on CDI were also determined. CDI data was related to the index of absorbance difference (IAD)—an index of chlorophyll degradation that was correlated with ethylene emission—and its response to time from harvest was modelled. The CDI was only significantly altered when measurements were taken in the middle of the morning or in the middle of the afternoon, when the presence of the sun in the image caused significant alteration of the image brightness. The CDI was tightly related to IAD, and CDI values plateaued (0.833 ± 0.009) at IAD ≤ 1.20 (climacteric onset) in ‘Majestic Pearl’ nectarine, suggesting that CDI thresholds show potential to be used for harvest time decisions and to support logistics. In order to obtain comparable CDI datasets to study colour development or forecast harvest time, it is recommended to scan peach and nectarine orchards at night, in the early morning, solar noon, or late afternoon. This study found that the CDI can serve as a standardised and objective skin colour index for peaches and nectarines.

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Reliability of a Handheld Bluetooth Colourimeter and Its Application to Measuring the Effects of Time from Harvest, Row Orientation and Training System on Nectarine Skin Colour

Cited by 7 publications

References 22 publications

A Ground-based Platform for Reliable Estimates of Fruit Number, Size, and Color in Stone Fruit Orchards

A Ground-based Platform for Reliable Estimates of Fruit Number, Size, and Color in Stone Fruit Orchards

Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach

A Fruit Colour Development Index (CDI) to Support Harvest Time Decisions in Peach and Nectarine Orchards

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