Design and application of a smartphone-based device for in vineyard determination of anthocyanins content in red grapes

Menozzi, Camilla; Calvini, Rosalba; Nigro, Giovanni; Tessarin, Paola; Bossio, Domenico; Calderisi, Marco; Ferrari, V.; Foca, Giorgia; Ulrici, Alessandro

doi:10.1016/j.microc.2023.108811

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…AC CR was shown to be the best index for nine varieties of grains but not for HZHN1. This unique abnormality could be due to the presence of white wax on the peel, thus causing obscure images [30,77,78]. It has also been reported that other factors such as the cell shape, cell wall thickness or pigment location may also alter the color perceived by sensors [79], causing errors whist estimating the anthocyanin content from digital images.…”

Section: Modeling Robustnessmentioning

confidence: 99%

Phenotyping the Anthocyanin Content of Various Organs in Purple Corn Using a Digital Camera

Wang,

Liu,

Wang

et al. 2024

Agriculture

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Anthocyanins are precious industrial raw materials. Purple corn is rich in anthocyanins, with large variation in their content between organs. It is imperative to find a rapid and non-destructive method to determine the anthocyanin content in purple corn. To this end, a field experiment with ten purple corn hybrids was conducted, collecting plant images using a digital camera and determining the anthocyanin content of different organ types. The average values of red (R), green (G) and blue (B) in the images were extracted. The color indices derived from RGB arithmetic operations were applied in establishing a model for estimation of the anthocyanin content. The results showed that the specific color index varied with the organ type in purple corn, i.e., ACCR for the grains, BRT for the cobs, ACCB for the husks, R for the stems, ACCB for the sheaths and BRT for the laminae, respectively. Linear models of the relationship between the color indices and anthocyanin content for different organs were established with R2 falling in the range of 0.64–0.94. The predictive accuracy of the linear models, assessed according to the NRMSE, was validated using a sample size of 2:1. The average NRMSE value was 11.68% in the grains, 13.66% in the cobs, 8.90% in the husks, 27.20% in the stems, 7.90% in the sheaths and 15.83% in the laminae, respectively, all less than 30%, indicating that the accuracy and stability of the model was trustworthy and reliable. In conclusion, this study provided a new method for rapid, non-destructive prediction of anthocyanin-rich organs in purple corn.

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Section: Modeling Robustnessmentioning

confidence: 99%

Phenotyping the Anthocyanin Content of Various Organs in Purple Corn Using a Digital Camera

Wang,

Liu,

Wang

et al. 2024

Agriculture

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Analysis in Wine Research: Current and Emerging Approaches

2024

Understanding Wine Chemistry

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Estimation of Anthocyanins in Heterogeneous and Homogeneous Bean Landraces Using Probabilistic Colorimetric Representation with a Neuroevolutionary Approach

Morales-Reyes,

Aquino-Bolaños,

Acosta-Mesa

et al. 2024

MCA

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The concentration of anthocyanins in common beans indicates their nutritional value. Understanding this concentration makes it possible to identify the functional compounds present. Previous studies have presented color characterization as two-dimensional histograms, based on the probability mass function. In this work, we proposed a new type of color characterization represented by three two-dimensional histograms that consider chromaticity and luminosity channels in order to verify the robustness of the information. Using a neuroevolutionary approach, we also found a convolutional neural network (CNN) for the regression task. The results demonstrate that using three two-dimensional histograms increases the accuracy compared to the color characterization represented by one two-dimensional histogram. As a result, the precision was 93.00 ± 5.26 for the HSI color space and 94.30 ± 8.61 for CIE L*a*b*. Our procedure is suitable for estimating anthocyanins in homogeneous and heterogeneous colored bean landraces.

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Design and application of a smartphone-based device for in vineyard determination of anthocyanins content in red grapes

Cited by 3 publications

References 35 publications

Phenotyping the Anthocyanin Content of Various Organs in Purple Corn Using a Digital Camera

Phenotyping the Anthocyanin Content of Various Organs in Purple Corn Using a Digital Camera

Analysis in Wine Research: Current and Emerging Approaches

Estimation of Anthocyanins in Heterogeneous and Homogeneous Bean Landraces Using Probabilistic Colorimetric Representation with a Neuroevolutionary Approach

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