Physical properties of <i>Physalis peruviana</i> L.

Oliveira, Solange; Gonçalves, Fernando; Correia, Paula; Guiné, Raquel

doi:10.1515/opag-2016-0007

Cited by 30 publications

(20 citation statements)

References 6 publications

(17 reference statements)

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“…In this regard, Table 4 compares the mean parameter values in the L*a*b* color space obtained in this work with those reported by Vásquez-Parra et al [5] and Puente et al [54]. The differences between the L*a*b* parameters obtained in this research and those shown by previous studies are because the cultivar, ripening stage or cultivation procedure for each sample was different among these studies, as was suggested by Oliveira et al [14].…”

Section: Discussionsupporting

confidence: 63%

Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces

et al. 2019

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The classification of fresh fruits according to their visual ripeness is typically a subjective and tedious task; consequently, there is a growing interest in the use of non-contact techniques to automate this process. Machine learning techniques, such as artificial neural networks, support vector machines (SVMs), decision trees, and K-nearest neighbor algorithms, have been successfully applied for classification problems in the literature, particularly for images of fruit. However, the particularities of each classification problem make it difficult, if not impossible, to select a general technique that is applicable to all types of fruit. In this paper, the combinations of four machine learning techniques and three color spaces (RGB, HSV, and L*a*b*) were evaluated with regard to their ability to classify Cape gooseberry fruits. To this end, 925 Cape gooseberry fruit samples were collected, and each fruit was manually classified into one of seven different classes according to its level of ripeness. The color values of each fruit image in the three color spaces and their corresponding ripening stages were organized for training and validation following a fivefold cross-validation strategy in an iterative process repeated 100 times. According to the results, the classification of Cape gooseberry fruits by their ripeness level was sensitive to both the color space and the classification technique used. The models based on the L*a*b* color space and the SVM classifier showed the highest f-measure regardless of the color space, and the principal component analysis combination of color spaces improved the performance of the models at the expense of increased complexity. INDEX TERMS Cape gooseberry, artificial neural networks, support vector machines, decision trees, K-nearest neighbors, color spaces, PCA, multiclass confusion matrix.

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Section: Discussionsupporting

confidence: 63%

Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces

et al. 2019

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“…In this regard, Table 4 compares the mean parameter values in the L*a*b* color space obtained in this work with those reported by Puente et al Puente et al (2011) and Vásquez-Parra et al Vásquez-Parra et al (2013). The differences between the L*a*b* parameters obtained in this research and those shown by previous studies are because the cultivar, ripening stage or cultivation procedure for each sample was different among these studies, as was suggested by Oliveira et al Oliveira et al (2016).…”

Section: Discussionsupporting

confidence: 58%

“…Moreover, color has been closely associated with various quality factors (ripeness, variety, and desirability) and food safety. Therefore, color is an essential classification element for most food products Castro et al (2017); Oliveira et al (2016); Avila et al (2015); Wu and Sun (2013).…”

Section: Introductionmentioning

confidence: 99%

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

Castro

Oblitas

De-la-Torre

et al. 2019

Preprint

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The classification of fresh fruits according to their visual ripeness is typically a subjective and tedious task; consequently, there is growing interest in the use of non-contact techniques to automate this process. Machine learning techniques such as artificial neural networks, support vector machines, decision trees, and K-nearest neighbor algorithms have been successfully applied for classification problems in the literature, particularly for images of fruit. However, the particularities of each classification problem make it difficult, if not impossible, to select a general technique that is applicable to all types of fruit. In this paper, combinations of four machine learning techniques and three color spaces (RGB, HSV, and L*a*b*) were evaluated with regard to their ability to classify Cape gooseberry fruits. To this end, 925 Cape gooseberry fruit samples were collected, and each fruit was manually classified into one of seven different classes according to its level of ripeness. The color values of each fruit image in the three color spaces and their corresponding ripening stages were organized for training and validation following a 5-fold cross-validation strategy in an iterative process repeated 100 times. According to the results, the classification of Cape gooseberry fruits by their ripeness level was sensitive to both the color space and the classification technique used. The models based on the L*a*b* color space and the SVM classifier showed the highest f-measure regardless of the color space, and the principal component analysis combination of color spaces improved the performance of the models at the expense of an increased complexity.

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“…The fruit of P. peruviana can be consumed in fresh fruit form or made into various processed foods such as jams, juices, etc. The cultivation of P. peruviana is not too difficult, as this plant has good adaptive capabilities in hot as well as cool climates (Oliveira et al, 2016). P. peruviana was also reported belong to moderately tolerant crop to several environmental stress conditions such as salinity (Miranda et al, 2010) and cadmium contaminant (Thiebeauld et al, 2005).…”

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confidence: 99%

Physalis peruviana L.: Growth, Yield and Phytochemical Content- A Review

Yamika

Aini

Waluyo

2019

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Physalis peruviana is one of the species in genus Physalis which is often cultivated in various regions including Indonesia. In Indonesia, P. peruviana is often called ciplukan. P. peruviana plants produce various phytochemical compounds which have benefical medicinal properties. P. peruviana plants have not been widely cultivated commercially as the information about cultivation practices of this plant are still limited. Many research activities have been done to study about its chemical and biological properties, but they are still not widely spread. Therefore, an effort has been made to compile the available inforamtion in this paper.

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Physical properties of Physalis peruviana L.

Cited by 30 publications

References 6 publications

Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces

Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

Physalis peruviana L.: Growth, Yield and Phytochemical Content- A Review

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