Measuring and evaluating anthocyanin in lettuce leaf based on color information

Yang, Xiao; Jing-jin, Zhang; Guo, Doudou; Xiong, Xin; Chang, Liu; Niu, Qingliang; Huang, Danfeng

doi:10.1016/j.ifacol.2016.10.018

Cited by 18 publications

(19 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of a machine learning regression model accounts for the complex relationship between each component of a color space and the actual anthocyanin content of the leaf. This multivariate machine learning approach demonstrates an improved accuracy in the estimation of anthocyanin accumulation from a digital color image compared with single‐variable regression methods (Murakami et al., ; Yang et al., ). When compared with hyperspectral imaging–based methods for estimating anthocyanin accumulation, the digital color imaging–based method developed in this study provides a comparable accuracy in controlled conditions, without the need for more expensive hyperspectral imaging equipment (Gitelson et al., ; Qin et al., ).…”

Section: Discussionmentioning

confidence: 99%

A noninvasive, machine learning–based method for monitoring anthocyanin accumulation in plants using digital color imaging

et al. 2019

View full text Add to dashboard Cite

Premise When plants are exposed to stress conditions, irreversible damage can occur, negatively impacting yields. It is therefore important to detect stress symptoms in plants, such as the accumulation of anthocyanin, as early as possible. Methods and Results Twenty‐two regression models in five color spaces were trained to develop a prediction model for plant anthocyanin levels from digital color imaging data. Of these, a quantile random forest regression model trained with standard red, green, blue (sRGB) color space data most accurately predicted the actual anthocyanin levels. This model was then used to noninvasively monitor the spatial and temporal accumulation of anthocyanin in Arabidopsis thaliana leaves. Conclusions The digital imaging–based nature of this protocol makes it a low‐cost and noninvasive method for the detection of plant stress. Applying a similar protocol to more economically viable crops could lead to the development of large‐scale, cost‐effective systems for monitoring plant health.

show abstract

Section: Discussionmentioning

confidence: 99%

A noninvasive, machine learning–based method for monitoring anthocyanin accumulation in plants using digital color imaging

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Previous studies also show that overexpression of PAP1 results in anthocyanin accumulation 20,21 . The average ratio of blue to green signal in images of leaves is an established proxy for anthocyanin accumulation 23 . We observed a significant increase in the average ratio of blue to green signal in images of rosette leaves of plants treated with on-target sgRNAs compared to controls ( Figure 2C).…”

Section: Sgrna Scaffolds Delivered Via Vin Vectors Simultaneously Crementioning

confidence: 99%

“…The box plot on the left summarizes the expression of the PAP1 gene, normalized to the PP2A housekeeping gene, in rosette leaves. The box plot on the right summarizes the average blue signal normalized to the green signal, which is an established proxy for anthocyanin concentration 23 , from images of rosette leaves. Each dot of the same color represents data from leaves of independent biological replicates (n=5 per treatment for expression and n=4 per treatment for leaf color).…”

Section: Figure 2 Vin Vectors Deliver Sgrna Scaffolds To Plant Linesmentioning

confidence: 99%

“…Representative pictures of rosettes of plants treated with on-target (B) or off-target (C) sgRNAs at the time of phenotyping. D) Box plot summarizing the average blue signal normalized to the green signal, which is an established proxy for anthocyanin concentration 23 , from images of rosette leaves of the plants described in panel A that were treated with ViN vectors encoding on-target (light grey) or off-target (dark grey) sgRNAs. Each dot of the same color represents data from leaves of independent biological replicates (n=3 per treatment).…”

Section: Figure 2 Vin Vectors Deliver Sgrna Scaffolds To Plant Linesmentioning

confidence: 99%

“…To estimate the anthocyanin concentration in the rosette leaves the images were then analyzed in imageJ and the mean Blue and green signals from three different rosette leaves per plant were recorded. These were then used to calculate the Blue to green ratio, which has been established as a proxy that correlates with anthocyanin accumulation 23 . To phenotype the size of the rosette leaves, the rosettes were dissected and placed sequentially on a scanner and imaged.…”

Section: Phenotyping Arabidopsis Rosette Leaf Size and Colormentioning

confidence: 99%

See 2 more Smart Citations

VipariNama: RNA vectors to rapidly reprogram plant morphology and metabolism

Khakhar

Wang

Swanson

et al. 2020

Preprint

View full text Add to dashboard Cite

Synthetic transcription factors have great promise as tools to explore biological processes. By allowing precise alterations in gene expression, they can help elucidate relationships between gene expression and plant morphology or metabolism. However, the years-long timescales, high cost, and technical skill associated with plant transformation have dramatically slowed their use. In this work, we developed a new platform technology called VipariNama (ViN) in which RNA vectors are used to rapidly deploy synthetic transcription factors and reprogram gene expression in planta. We demonstrate how ViN vectors can direct activation or repression of multiple genes, systemically and persistently over several weeks, and in multiple plant species. We also show how this transcriptional reprogramming can create predictable changes to metabolic and morphological phenotypes in the model plants Nicotiana benthamiana and Arabidopsis thaliana in a matter of weeks. Finally, we show how a model of gibberellin signaling can guide ViN vectorbased reprogramming to rapidly engineer plant size in both model species as well as the crop Solanum lycopersicum (tomato). In summary, using VipariNama accelerates the timeline for generating phenotypes from over a year to just a few weeks, providing an attractive alternative to transgenesis for synthetic transcription factor-enabled hypothesis testing and crop engineering.

show abstract

A non-destructive, low cost and high throughput colorimetric method for chlorophyll estimation in rice (Oryza sativa L.)

Shafi,

Zaffar,

Riyaz

et al. 2024

Discov. Plants

View full text Add to dashboard Cite

Leaf chlorophyll is an important biochemical parameter used to assess plant health under stress. Chlorophyll is invariably estimated using destructive method of acetone or ethanol extraction. Reflectance spectroscopy such as IR, VIS–NIR, combined with chemometric, has been widely used in plant leaf chemical analysis. Here we report a cheap and reliable spectroscopic method of chlorophyll estimation using a low-cost handheld spectrometer. We standardized the method in a rice diversity panel of 264 genotypes using L*, a* and b* parameters using a randomly taken calibration and validation sample. The parameters used for fitting the regression analysis were L*, a*, b* and δ E that indicates deviation of L*, a* and b* values of sample from the chlorophyll solution of a known concentration. We fitted the regression of total chlorophyll in calibration sample with L*, a*, b* and δ E separately as well as multiple regression with all parameters. The best R2 value (0.671) was observed for a* and the lowest for L* (0.436). Using multiple regression, the R2 value increased to 0.778 (p < 0.005). We used the regression equation for validation sample and obtained R2 value of 0.724 indicating reliable explanatory potential of variables. The parametric test for model reliability indicates good model fit of the observed relationship between chlorophyll content and L*, a*, b* and δ E. The method is cost effective, non-destructive and takes less time (< 10 s) and can be conveniently used for screening large diversity panels especially in case of repeated measurements across growth stages under stress.

show abstract

Measuring and evaluating anthocyanin in lettuce leaf based on color information

Cited by 18 publications

References 16 publications

A noninvasive, machine learning–based method for monitoring anthocyanin accumulation in plants using digital color imaging

A noninvasive, machine learning–based method for monitoring anthocyanin accumulation in plants using digital color imaging

VipariNama: RNA vectors to rapidly reprogram plant morphology and metabolism

A non-destructive, low cost and high throughput colorimetric method for chlorophyll estimation in rice (Oryza sativa L.)

Contact Info

Product

Resources

About