Prediction of pelargonidin-3-glucoside in strawberries according to the postharvest distribution period of two ripening stages using VIS-NIR and SWIR hyperspectral imaging technology

Cho, Jeong-Seok; Lim, Jeong‐Ho; Park, Kee Jai; Choi, Jeong Hee; Ok, Gyeong Sik

doi:10.1016/j.lwt.2021.110875

Cited by 17 publications

(6 citation statements)

References 33 publications

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“…Indeed, Underhill et al (2020) showed that the RGB color space differentiated variability in grape skin color, associated with different anthocyanin concentrations. Strawberries with higher concentrations of anthocyanin had lower pixel intensities in the green spectrum (the lowest intensity was near 530 nm) based on hyperspectral imaging (Cho et al, 2021). Monitoring anthocyanins in fruits and vegetables will be beneficial not only in assessing phenotypic variation in anthocyanins in a non-destructive manner, but also in automating post-harvest quality evaluation or for robotic harvesting.…”

Section: Discussionmentioning

confidence: 99%

“…There are many successful examples of anthocyanin predictions based on hyperspectral imaging combined with machine learning techniques ( Chen et al., 2015 ; Askey et al., 2019 ; Simko, 2020 ; Cho et al., 2021 ; Kim et al., 2021 ). However, these machine learning models require hyperspectral imaging systems with similar wavelengths as used to develop the machine learning model.…”

Section: Discussionmentioning

confidence: 99%

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Image-based phenotyping to estimate anthocyanin concentrations in lettuce

Kim

Iersel

2023

Front. Plant Sci.

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Anthocyanins provide blue, red, and purple color to fruits, vegetables, and flowers. Due to their benefits for human health and aesthetic appeal, anthocyanin content in crops affects consumer preference. Rapid, low-cost, and non-destructive phenotyping of anthocyanins is not well developed. Here, we introduce the normalized difference anthocyanin index (NDAI), which is based on the optical properties of anthocyanins: high absorptance in the green and low absorptance in the red part of the spectrum. NDAI is determined as (Ired - Igreen)/(Ired + Igreen), where I is the pixel intensity, a measure of reflectance. To test NDAI, leaf discs of two red lettuce (Lactuca sativa) cultivars ‘Rouxai’ and ‘Teodore’ with wide range of anthocyanin concentrations were imaged using a multispectral imaging system and the red and green images were used to calculate NDAI. NDAI and other commonly used indices for anthocyanin quantification were evaluated by comparing to with the measured anthocyanin concentration (n = 50). Statistical results showed that NDAI has advantages over other indices in terms of prediction of anthocyanin concentrations. Canopy NDAI, obtained using multispectral canopy imaging, was correlated (n = 108, R2 = 0.73) with the anthocyanin concentrations of the top canopy layer, which is visible in the images. Comparison of canopy NDAI from multispectral images and RGB images acquired using a Linux-based microcomputer with color camera, showed similar results in the prediction of anthocyanin concentration. Thus, a low-cost microcomputer with a camera can be used to build an automated phenotyping system for anthocyanin content.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Image-based phenotyping to estimate anthocyanin concentrations in lettuce

Kim

Iersel

2023

Front. Plant Sci.

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“…Espectros 1D e HSI 3D 380 a 1030 nm Se obtuvo un rendimiento de 84% para la identificación de madurez (Su et al, 2021); (Shao et al, 2020) Se utilizó HSI para predecir el P3G de las fresas para dos tipos de madurez de cosecha Sensor hiperespectral OCI-F 400 a 1000 nm HSI podría usarse para la predicción de P3G con diferente madurez de cosecha. (Cho, Lim, Park, Choi, & Ok, 2021) HSI y análisis de datos con varias técnicas de regresión.…”

Section: Reconocimiento Clasificación Y Predicción De Calidad En Dife...unclassified

Uso de las imágenes hiperespectrales e imágenes digitales en bayas: Anomalías, enfermedades, daños mecánicos, firmeza, madurez y morfometría

Sánchez¹,

Reyes²

2023

Manglar

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There are different types of berries, one of the best-known, nutritious and important is the blueberry. The modern processing of these fruits guarantees high quality, better marketing of the product and an estimate of its useful life. The aim of this review was to provide scientific information on the physicochemical characteristics of different berries using hyperspectral imaging technology and digital imaging. These technologies present trends with satisfactory results in various technological and research fields. The findings obtained show that hyperspectral imaging technology and digital imaging technology have been of great interest in recent years, because they are non-destructive technologies, which allow good predictions in the detection of anomalies in berries, considering them Robust, reliable tools with high potential for use in the large industry in evaluating the quality of berries, making it possible to offer more suitable products for the consumer. With the advancement of technology, new possibilities for future studies are presented to obtain models that are faster to process and with greater statistical precision.

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“…Hyperspectral imaging based optical system was developed by Saputro et al (2018) to detect the ripening of banana by predicting the amount of chlorophyll pigment in the peel of fruit. Cho et al (2021) identified 400 -1700 nm as the suitable wavelength range for hyperspectral imaging of strawberry fruits to predict anthocyanin content at different stages of maturity. A non-destructive method of hyperspectral imaging in lettuce was developed by Simko et al (2016) to determine chlorophyll and anthocyanin content.…”

Section: Application Of Ai In Identification Of Produce Maturitymentioning

confidence: 99%

Application of Artificial Intelligence in Horticulture: A Review

Sebastian,

Balakrishnan,

Koshy

et al. 2023

EEC

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India, with its diverse soil and climate conditions and varied agro ecological regions, provides a possibility to grow many horticultural crops, which significantly contribute to the Indian economy by enhancing farm output, generating employment, and providing raw materials to various food-processing businesses. The amount of land allotted for horticulture is minimal, but the demand for the production of horticulture crops is high. Therefore, meeting the demand with minimum resources is a bit challenging, nevertheless can be achieved by the introduction of Artificial Intelligence (AI) to the field. Application of AI in horticultural crop production by predicting adverse climate conditions, providing suitable micro-environment through remote controlled drip irrigation, and its application in providing automated irrigation, determining crop genotype, disease diagnosis, pest and weed management, maturity identification of fruits and vegetables, yield forecasting, automated harvesting using robots, reducing post-harvest losses and its role in fruit quality assessment and in processing industry is elaborated in this review.

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Prediction of pelargonidin-3-glucoside in strawberries according to the postharvest distribution period of two ripening stages using VIS-NIR and SWIR hyperspectral imaging technology

Cited by 17 publications

References 33 publications

Image-based phenotyping to estimate anthocyanin concentrations in lettuce

Image-based phenotyping to estimate anthocyanin concentrations in lettuce

Uso de las imágenes hiperespectrales e imágenes digitales en bayas: Anomalías, enfermedades, daños mecánicos, firmeza, madurez y morfometría

Application of Artificial Intelligence in Horticulture: A Review

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