Predicting Early Harvest Maturity Dates for ‘Delicious’ Apples in New York1

Blanpied, G. D.

doi:10.21273/hortsci.14.6.710

Cited by 13 publications

(15 citation statements)

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“…Dropped apples or changes in background skin color were indicators of harvestable trees (Watkins, 2003). In addition, a sample apple was taken from each tree and touched to assess firmness, tasted to assess starch and sweetness, cut in half to check browning of seeds, and sprayed with iodine solution to evaluate starch content (Blanpied & Silsby, 1992). The combination of these indicators was used to determine whether an accession was ready to be harvested.…”

Section: Methodsmentioning

confidence: 99%

Quantifying apple diversity: A phenomic characterization of Canada’s Apple Biodiversity Collection

Watts

Migicovsky

McClure

et al. 2021

Plants People Planet

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Section: Methodsmentioning

confidence: 99%

Quantifying apple diversity: A phenomic characterization of Canada’s Apple Biodiversity Collection

Watts

Migicovsky

McClure

et al. 2021

Plants People Planet

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“…'McIntosh' displayed the highest I AD (1.0-1.3), while 'Gala' had the highest firmness (17.6-21.5 lb-force) and lowest I AD (0.19-0.56). Blanpied and Silsby (1992), where 1 = 100% starch staining and 8 = no starch.…”

Section: Resultsmentioning

confidence: 99%

“…Fruit firmness was determined on opposite sides of each apple after peel removal, using an electronic texture analyzer fitted with an 11 mm tip (GÜSS, South Africa) and displayed as pound-force (1 lb (lb)-force = 4.45 N). Starch index was determined using the generic starch-iodine index chart for apples by Blanpied and Silsby (1992). Apples were cut in half at the equator, dipped in potassium-iodine solution, and rated on a scale of 1 to 8, where 1 = 100% starch staining and 8 = no starch.…”

Section: Fruit Maturity Analysismentioning

confidence: 99%

Variability of index of absorbance difference (I_AD) to indicate fruit maturity at harvest for major apple cultivars in Ontario

Mostofi,

DeEll

2024

Can. J. Plant Sci.

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The overall objective of this study was to evaluate the Index of Absorbance Difference (IAD) of four major apple cultivars in Ontario (‘Honeycrisp’, ‘Ambrosia’, ‘Gala’ and ‘McIntosh’) during the harvest window over multiple seasons (≥ 4 years), as well as its relationship with fruit firmness, internal ethylene concentration, and starch index values. IAD values differed among the four cultivars, with ‘McIntosh’ showing the highest IAD (1.03 to 1.33) overall and ‘Gala’ having the lowest (0.19 to 0.56). Principal component analysis (PCA) showed that the cultivars were separated into distinct groups. ‘Honeycrisp’ was clustered with starch and ethylene, while ‘Gala’ and ‘McIntosh’ were mainly clustered with firmness and IAD, respectively. Variable correlations between IAD and other maturity indices were found over the years. The negative relationship between IAD and ethylene for ‘Gala’ showed variability with R2 ranging from 0.008 in 2012 and 2019 to 0.47 in 2018. The correlation between IAD and starch for ‘Gala’ was very strong (rs= -0.82 ****) in 2018, whereas it was not significant in any year for ‘McIntosh’. Overall, IAD may relate to harvest maturity, but it did not correlate closely or consistently with other maturity indices, varied greatly year-to-year, and was cultivar dependent. IAD measures are not consistently related to fruit maturity every year, making reliability difficult to attain.

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“…Starch clearing is a widely used index for apple fruit maturity (Blanpied & Silsby, 1992). While the extent of starch clearing is relatable to fruit maturity across apple cultivars generally, there are cultivar-specific characteristics for the rate of clearing, patterns of clearing ( i.e.…”

Section: Discussionmentioning

confidence: 99%

“…Starch content can be estimated from fruit cross-sections treated with a potassium iodide and iodine solution (Blanpied & Silsby, 1992; Figure. 1A & C).…”

Section: Methodsmentioning

confidence: 99%

Rating Pome Fruit Quality Traits Using Deep Learning and Image Processing

Nguyen,

Michaud,

Mogollon

et al. 2024

Preprint

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Quality assessment of pome fruits (i.e.apples and pears) is used not only crucial for determining the optimal harvest time, but also the progression of fruit-quality attributes during storage. Therefore, it is typical to repeatedly evaluate fruits during the course of a postharvest experiment. This evaluation often includes careful visual assessments of fruit for apparent defects and physiological symptoms. A general best practice for quality assessment is to rate fruit using the same individual rater or group of individuals raters to reduce bias. However, such consistency across labs, facilities, and experiments is often not feasible or attainable. Moreover, while these visual assessments are critical empirical data, they are often coarse-grained and lack consistent objective criteria. Granny, is a tool designed for rating fruit using machine-learning and image-processing to address rater bias and improve resolution. Additionally, Granny supports backwards compatibility by providing ratings compatible with long-established standards and references, promoting research program continuity. Current Granny ratings include starch content assessment, rating levels of peel defects, and peel color analyses. Integrative analyses enhanced by Granny’s improved resolution and reduced bias, such as linking fruit outcomes to global scale-omics data, environmental changes, and other quantitative fruit quality metrics like soluble solids content and flesh firmness, will further enrich our understanding of fruit quality dynamics. Lastly, Granny is open-source and freely available.

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Predicting Early Harvest Maturity Dates for ‘Delicious’ Apples in New York1

Cited by 13 publications

References 0 publications

Quantifying apple diversity: A phenomic characterization of Canada’s Apple Biodiversity Collection

Quantifying apple diversity: A phenomic characterization of Canada’s Apple Biodiversity Collection

Variability of index of absorbance difference (I_AD) to indicate fruit maturity at harvest for major apple cultivars in Ontario

Rating Pome Fruit Quality Traits Using Deep Learning and Image Processing

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Predicting Early Harvest Maturity Dates for ‘Delicious’ Apples in New York1

Cited by 13 publications

References 0 publications

Quantifying apple diversity: A phenomic characterization of Canada’s Apple Biodiversity Collection

Quantifying apple diversity: A phenomic characterization of Canada’s Apple Biodiversity Collection

Variability of index of absorbance difference (IAD) to indicate fruit maturity at harvest for major apple cultivars in Ontario

Rating Pome Fruit Quality Traits Using Deep Learning and Image Processing

Contact Info

Product

Resources

About

Variability of index of absorbance difference (I_AD) to indicate fruit maturity at harvest for major apple cultivars in Ontario