Quantitative dissection of color patterning in the foliar ornamental coleus

Li, Mao; Coneva, Viktoriya; Robbins, Kelly R.; Clark, David G.; Chitwood, Daniel H.; Frank, Margaret H.

doi:10.1093/plphys/kiab393

Cited by 10 publications

(6 citation statements)

References 62 publications

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“…This method can help researchers in fields outside of agriculture, such as in animal husbandry—by improving the breeding efficiency and increasing yield—and in computer vision—by improving the training and feature extraction by deep learning networks. Among these indicators, the color, shape, and texture features of crops are important indicators of their appearance, which can quantify the appearance of crops in an objective, differentiated, accurate, and direct manner [ 36 , 37 ]. In the field of image translation, researchers have focused more on improving models.…”

Section: Methodsmentioning

confidence: 99%

EasyDAM_V3: Automatic Fruit Labeling Based on Optimal Source Domain Selection and Data Synthesis via a Knowledge Graph

Zhang,

Liu,

Zheng

et al. 2023

Plant Phenomics

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Although deep learning-based fruit detection techniques are becoming popular, they require a large number of labeled datasets to support model training. Moreover, the manual labeling process is time-consuming and labor-intensive. We previously implemented a generative adversarial network-based method to reduce labeling costs. However, it does not consider fitness among more species. Methods of selecting the most suitable source domain dataset based on the fruit datasets of the target domain remain to be investigated. Moreover, current automatic labeling technology still requires manual labeling of the source domain dataset and cannot completely eliminate manual processes. Therefore, an improved EasyDAM_V3 model was proposed in this study as an automatic labeling method for additional classes of fruit. This study proposes both an optimal source domain establishment method based on a multidimensional spatial feature model to select the most suitable source domain, and a high-volume dataset construction method based on transparent background fruit image translation by constructing a knowledge graph of orchard scene hierarchy component synthesis rules. The EasyDAM_V3 model can automatically obtain fruit label information from the dataset, thereby eliminating manual labeling. To test the proposed method, pear was used as the selected optimal source domain, followed by orange, apple, and tomato as the target domain datasets. The results showed that the average precision of annotation reached 90.94%, 89.78%, and 90.84% for the target datasets, respectively. The EasyDAM_V3 model can obtain the optimal source domain in automatic labeling tasks, thus eliminating the manual labeling process and reducing associated costs and labor.

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Section: Methodsmentioning

confidence: 99%

EasyDAM_V3: Automatic Fruit Labeling Based on Optimal Source Domain Selection and Data Synthesis via a Knowledge Graph

Zhang,

Liu,

Zheng

et al. 2023

Plant Phenomics

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“…RGB and CIEL*a*b* values have been directly used to detect quantitative trait loci (QTL) associated with colour variations in grape [38] or salvia [39]. Li et al [40] used Gaussian estimation of pixel distributions to study colour patterning in the foliar ornamental coleus. Deep learning techniques have also been used to overcome limitations of pixel colour information (confounding effects and continuity of colours) in the case of complex plant vegetation segmentation [41].…”

Section: Introductionmentioning

confidence: 99%

Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity

Bouillon,

Fanciullino,

Belin

et al. 2024

Plant Methods

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Background The genetic basis of colour development in red-flesh apples (Malus domestica Borkh) has been widely characterised; however, current models do not explain the observed variations in red pigmentation intensity and distribution. Available methods to evaluate the red-flesh trait rely on the estimation of an average overall colour using a discrete class notation index. However, colour variations among red-flesh cultivars are continuous while development of red colour is non-homogeneous and genotype-dependent. A robust estimation of red-flesh colour intensity and distribution is essential to fully capture the diversity among genotypes and provide a basis to enable identification of loci influencing the red-flesh trait. Results In this study, we developed a multivariable approach to evaluate the red-flesh trait in apple. This method was implemented to study the phenotypic diversity in a segregating hybrid F1 family (91 genotypes). We developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images and compared the efficiency of RGB and CIEL*a*b* colour spaces in discriminating genotypes previously classified with a visual notation. Chemical destructive methods, including targeted-metabolite analysis using ultra-high performance liquid chromatography with ultraviolet detection (UPLC-UV), were performed to quantify major phenolic compounds in fruits’ flesh, as well as pH and water contents. Multivariate analyses were performed to study covariations of biochemical factors in relation to colour expression in CIEL*a*b* colour space. Our results indicate that anthocyanin, flavonol and flavanol concentrations, as well as pH, are closely related to flesh pigmentation in apple. Conclustion Extraction of colour descriptors combined to chemical analyses helped in discriminating genotypes in relation to their flesh colour. These results suggest that the red-flesh trait in apple is a complex trait associated with several biochemical factors.

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“…Coleus (Solenostemon scutellarioides) is a variegated plant with a unique leaf color consisting of two or more color combinations, such as red, pink, and purple colors for anthocyanin and green color for chlorophyll [19][20][21]. The area ratio of each leaf color is sensitive to changes in the light environment [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Growth and Leaf Color of Coleus under Light Conditions Modified by Translucent Agrivoltaic Panels and Light-Emitting Diodes in a Greenhouse

Park,

Kim,

2024

Horticulturae

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Dye-sensitized solar cells (DSSCs) can be used as greenhouse glazing materials in agrivoltaic systems because they are translucent, have different colors, and can produce electricity. However, the light quality of DSSCs differs from that of sunlight, and the visible light transmittance is low. Therefore, we compared the plant shape, growth, and leaf color of coleus, a highly photosensitive plant, under transparent glass and red-colored DSSCs. Coleus ‘Highway Rose’ was grown in transparent (T, the control), shaded (S), and DSSC (D) chambers maintained at 23 ± 2 °C. The DSSC chambers were additionally illuminated with blue (B), green (G), white (W), B+G, and R+B+W light-emitting diodes (LEDs) (D+L) at 60 μmol·m−2·s−1 photosynthetic photon flux density for 15 h from 05:00 to 20:00. The coleus generally exhibited good growth under the T treatment. However, the light quality of DSSCs differed from that of sunlight, and the visible light transmittance decreased. Coleus exhibited increased growth and leaf color characteristics under the supplemental B lighting treatments (D+L(RBW), D+L(B), D+L(BG), and D+L(W)). Supplemental lighting with B LEDs using DSSCs improved plant morphology growth and leaf color. On the other hand, supplemental G lighting reinforced the shade avoidance syndrome. Moreover, DSSCs could aid in reducing the energy required to control the environment.

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Quantitative dissection of color patterning in the foliar ornamental coleus

Cited by 10 publications

References 62 publications

EasyDAM_V3: Automatic Fruit Labeling Based on Optimal Source Domain Selection and Data Synthesis via a Knowledge Graph

EasyDAM_V3: Automatic Fruit Labeling Based on Optimal Source Domain Selection and Data Synthesis via a Knowledge Graph

Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity

Growth and Leaf Color of Coleus under Light Conditions Modified by Translucent Agrivoltaic Panels and Light-Emitting Diodes in a Greenhouse

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