Predictive Modeling of a Leaf Conceptual Midpoint Quasi-Color (CMQ) Using an Artificial Neural Network

Šimko, Ivan

doi:10.3390/s20143938

Cited by 13 publications

(11 citation statements)

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“…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%

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%

Image-based phenotyping to estimate anthocyanin concentrations in lettuce

Kim

Iersel

2023

Front. Plant Sci.

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“…As our understanding of genes involved in lettuce coloration improves, the development of lettuce germplasm with customized leaf color (Simko, 2020b) will become possible. A growing number of resistance genes from wild Lactuca species (Lebeda et al, 2014) will be introgressed into cultivated lettuce.…”

Section: Future Outlookmentioning

confidence: 99%

“…The molecular and functional genomics research in spinach has progressed in recent years. Studies have focused on investigations of the cultivated and wild genetic resources using molecular markers to determine genetic diversity, population structure, and developing fingerprinting assays (Shi et al, 2017;van Treuren et al, 2020;Ribera et al, 2020a;2020b;Bhattarai et al, 2021). Regarding plant disease resistance, genetic research has focused primarily on DM, the disease that causes the largest economic losses in spinach production.…”

Section: Marker-assisted Selection and Genomic Selectionmentioning

confidence: 99%

“…In 2019 alone, more than 20 studies were published that reported identification of new candidate genes for simply inherited mutants or QTLs in cucumber. These publications covered a variety of traits, such as resistance to cucumber vein yellowing virus (CVYV), powdery mildew and angular leaf spot (Pujol et al, 2019;Zhang et al, 2020a;2020b;2021), tolerance to abiotic stresses (heat and cold) (Dong et al, 2019;Wang et al, 2019b), plant architecture (Wen et al, 2019a;Njogu et al, 2020), flowering time, fruit size, shape, skin features, pedicel direction, and internal quality attributes (Rett-Cadman et al, 2019;Sun et al, 2019;Zhang et al, 2019Zhang et al, , 2020bGao et al, 2020;Sheng et al, 2020;Song et al, 2020;Pan et al, 2020b;Wang et al, 2020a;2020b;, and leaf color and shape mutations (Ding et al, 2019;Liu et al, 2019;Hu et al, 2020).…”

Section: B Mapped Genes and Qtls And Markerassisted Selectionmentioning

confidence: 99%

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Genomics and Marker-Assisted Improvement of Vegetable Crops

Šimko

Jia

Venkatesh

et al. 2021

Critical Reviews in Plant Sciences

Self Cite

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Vegetables are an integral part of the human diet worldwide. Traditional breeding approaches have been used extensively to develop new cultivars of vegetables with desirable characteristics, including resistance/tolerance to biotic and abiotic stresses, high yield, and an elevated content of compounds beneficial to human health. The technological progress since the early 1980s has revolutionized our ability to study and manipulate genetic variation in crop plants. The development of high-throughput sequencing platforms and accompanying analytical methods have led to sequencing and assembly of a large number of plant genomes, construction of dense and ultra-dense molecular linkage maps, identification of structural variants, and application of molecular markers in breeding programs. Linkage mapping and genome-wide association mapping studies have been used to identify chromosomal locations of genes and QTLs associated with plant phenotypic variations important for crop improvement. This review provides up-to-date information on the status of genomics and marker-assisted improvement of vegetable crops with the focus on tomato, pepper, eggplant, lettuce, spinach, cucumber, and chicory. For each vegetable crop, we present the most recent information on genetic resources, mapping populations, genetic maps, genome sequences, mapped genes and QTLs, the status of marker-assisted selection and genomic selection, and discuss future research prospects and application of novel techniques and approaches.

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“…When focusing on human visual perception, the color spaces that can directly represent colors by using hue, chroma, and lightness, such as the HSI [14][15][16][17][18] and CIELAB [19][20][21][22][23][24][25] color spaces, are more useful than the RGB color space. Previously, we proposed an image enhancement method in the CIELAB color space, which is one of the uniform color spaces.…”

Section: Introductionmentioning

confidence: 99%

Chroma Enhancement in CIELAB Color Space Using a Lookup Table

Azetsu

Suetake

2021

Designs

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In this study, we present a method of chroma enhancement in the CIELAB color space and compare it with that in the RGB color space. Color image enhancement using the CIELAB color space has the disadvantage that the color gamut problem occurs because the conversion to the RGB color space is necessary to display the image. However, since the CIELAB color space is based on human visual perception, the quality of the resulting images is expected to be higher than that of the RGB color space. In the method using the CIELAB color space, we introduce a lookup table to reduce the calculation costs. Experiments comparing image enhancement results obtained from two color spaces are performed using several digital images.

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Predictive Modeling of a Leaf Conceptual Midpoint Quasi-Color (CMQ) Using an Artificial Neural Network

Cited by 13 publications

References 50 publications

Image-based phenotyping to estimate anthocyanin concentrations in lettuce

Image-based phenotyping to estimate anthocyanin concentrations in lettuce

Genomics and Marker-Assisted Improvement of Vegetable Crops

Chroma Enhancement in CIELAB Color Space Using a Lookup Table

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