Determination of watermelon volume using ellipsoid approximation and image processing

Koc, A. Bulent

doi:10.1016/j.postharvbio.2007.03.010

Cited by 115 publications

(88 citation statements)

References 7 publications

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“…Thus, fruit volume was estimated from the length (FL), the largest diameter (D1F) and the smallest diameter (D2F) to construct the equation of ellipsoid approximation (EA) (Koc, 2007):…”

Section: Phenotyping Of the Morpho-agronomic Fruit Traitsmentioning

confidence: 99%

“…Several authors have reported success in estimating the diameter and length of fruits using digital images. For instance, Koc (2007) validated these traits in watermelon with the use of digital images, obtaining averages similar to the measurements taken with a caliper. The correlation between the volume calculated by WCD and by the EA using the dimensions obtained with a digital caliper was high (r = 0.96) ( Figure 4A).…”

Section: Phenotyping Of the Morpho-agronomic Fruit Traitsmentioning

confidence: 99%

“…Thus, it has been used, for example, in validating the volume estimate of watermelon (Koc, 2007), orange (Khojastehnazhand et al, 2009) and the surface area of zucchini (Arjenaki et al, 2012).…”

Section: Phenotyping Of the Morpho-agronomic Fruit Traitsmentioning

confidence: 99%

“…Although the distance between the camera and the measurement box was constant, the distance between the fruit surface and the camera reduced with increasing fruit size. Koc (2007), who also attributed image estimation errors to the distance change as fruit size increased, reported this problem in watermelon. According to Khojastehnazhand et al (2009), this problem can be solved by using several cameras and developing more complex algorithms, so that a 3D fruit silhouette can be reconstructed.…”

Section: Phenotyping Of the Morpho-agronomic Fruit Traitsmentioning

confidence: 99%

“…Algorithms of digital image processing have been validated with a high degree of accuracy to estimate the number of commercial fruits in apple-tree (Aggelopoulou et al, 2011), mango (Payne et al, 2013) and vine (Roscher et al, 2014). Likewise, processing algorithms have been validated to estimate length, diameter and volume of fruit in watermelon (Koc, 2007), orange (Khojastehnazhand et al, 2009) and cantaloupe (Rashidi et al, 2009). As a result, this study aimed to validate two methodologies for the phenotyping of morpho-agronomic plant traits using image analysis and fruit traits through image processing.…”

Section: Introductionmentioning

confidence: 99%

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Model-assisted phenotyping by digital images in papaya breeding program

Cortes

Santa-Catarina

Barros

et al. 2017

Sci. agric. (Piracicaba, Braz.)

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Manual phenotyping for papaya Carica papaya (L) breeding purposes limits the evaluation of a great number of plants and hampers selection of superior genotypes. This study aimed to validate two methodologies for the phenotyping of morpho-agronomic plant traits using image analysis and fruit traits through image processing. In plants of the 'THB' variety and 'UENF/ Caliman-01' hybrid two images (A and B) were analyzed to estimate commercial and irregularly shaped fruits. Image A was also used in the estimation of plant height, stem diameter and the first fruit insertion height. In 'THB' fruits, largest and smallest diameters, length, and volume were estimated by using a caliper and image processing (IP). Volume was obtained by water column displacement (WCD) and by the expression of ellipsoid approximation (EA). Correlations above 0.85 between manual and image measurements were obtained for all traits. The averages of the morpho-agronomic traits, estimated by using images, were similar when compared to the averages measured manually. In addition, the errors of the proposed methodologies were low compared to manual phenotyping. Bland-Altman's approach indicated agreement between the volume estimated by WCD and EA using caliper and IP. The strong association obtained between volume and fruit weight suggests the use of regression to estimate this trait. Thus, the expectation is that image-based phenotyping can be used to expand the experiments, thereby maintaining accuracy and providing greater genetic gains in the selection of superior genotypes.

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“…Thus, fruit volume was estimated from the length (FL), the largest diameter (D1F) and the smallest diameter (D2F) to construct the equation of ellipsoid approximation (EA) (Koc, 2007):…”

Section: Phenotyping Of the Morpho-agronomic Fruit Traitsmentioning

confidence: 99%

Section: Phenotyping Of the Morpho-agronomic Fruit Traitsmentioning

confidence: 99%

Section: Phenotyping Of the Morpho-agronomic Fruit Traitsmentioning

confidence: 99%

Section: Phenotyping Of the Morpho-agronomic Fruit Traitsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Model-assisted phenotyping by digital images in papaya breeding program

Cortes

Santa-Catarina

Barros

et al. 2017

Sci. agric. (Piracicaba, Braz.)

View full text Add to dashboard Cite

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Association between traits related to fruit quality in papaya (Carica papaya L.) hybrids and their implications for selection

Pereira Miranda,

Ramos,

Santa‐Catarina

et al. 2023

Agronomy Journal

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The knowledge of the associations between the variables and the cause‐effect discrimination allows the breeders to make efficient of new cultivars development with traits that meet of producers and consumers demands. Therefore, the present work aimed to study the associations and among the quality variables of papaya (Carica papaya L.) fruits and the effects in selection process. Eight variables related to fruit quality were evaluated in 62 topcross hybrids. The phenotypic and genotypic correlations and path analyses, were performed to verify the associations between the variables and the effects on pulp volume. Canonical correlation analysis between primary and secondary quality variables was also carried out. Fruit firmness (FF) and soluble solids content showed a positive and significant genotypic correlation, providing evidence for selecting fruits with high averages for both variables. Fruit weight and pulp thickness (PT) were the variables that most contributed to the reduction and increased pulp volume percentage (%PV), respectively. The canonical correlation analysis showed an increase in PT associated with an increase in the fruit length and %PV variables. The physical‐chemical variables showed a low association with others, which will make the selection process more laborious to obtain satisfactory gains between the groups of variables.This article is protected by copyright. All rights reserved

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Rapid identification of chrysanthemum teas by computer vision and deep learning

Gao

et al. 2020

Food Science & Nutrition

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Seven commercial Chinese chrysanthemum tea products were classified by computer vision combined with machine learning algorithms. Without the need of building any specific hardware, the image acquisition was achieved in two computer vision approaches. In the first approach, a series of multivariate classification models were built after morphological feature extraction of the image. The best prediction accuracies when classifying flowering stages and tea types were respectively 90% and 63%. In comparison, the deep neural network was applied directly on the raw image, yielded 96% and 89% correct identifications when classifying flowering stage and tea type, respectively. The model can be applied for rapid and automatic quality determination of teas and other related foods. The result indicated that computer vision, especially when combined with deep learning or other machine learning techniques can be a convenient and versatile method in the evaluation of food quality. K E Y W O R D S chrysanthemum tea, computer vision classification, deep neural network, morphological feature

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Determination of watermelon volume using ellipsoid approximation and image processing

Cited by 115 publications

References 7 publications

Model-assisted phenotyping by digital images in papaya breeding program

Model-assisted phenotyping by digital images in papaya breeding program

Association between traits related to fruit quality in papaya (Carica papaya L.) hybrids and their implications for selection

Rapid identification of chrysanthemum teas by computer vision and deep learning

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