Evaluating and Mapping Grape Color Using Image-Based Phenotyping

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

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.

A high-density linkage map construction in guava (Psidium guajava L.) using genotyping by sequencing and identification of QTLs for leaf, peel, and pulp color in an intervarietal mapping population

Psidium guajava L. is an important fruit crop in the tropical and subtropical regions of the world. The advanced breeding methods are not employed for important commercial traits like peel and pulp color, seed hardiness, fruit size, etc., due to the scarcity of genome-wide molecular markers and high-density linkage maps. In this study, we employed single-nucleotide polymorphism (SNP) markers and identified quantitative trait loci (QTL) regions that are associated with color traits of leaf, peel, and pulp in the guava intervarietal mapping population. The mapping population was developed from the contrasting genotypes of fruit and leaf color. Variations in color among the segregating hybrids were recorded both visually and using a Color reader. A high-density linkage map of guava was constructed using the SNP markers from genotyping by sequencing (GBS) of 150 hybrid individuals of the cross 'Arka Poorna' (green) x 'Purple Local' (purple). The integrated linkage map consisted of 1426 SNPs mapped on 11 linkage groups (LG), spanning a total distance of around 730 cM with an average of 129.6 markers per LG. Through QTL analysis for color traits, a minor QTL region was identified for visually scored leaf color and peel color on LG1, whereas a major QTL was detected for pulp color in LG4. The Hunter color values (L* and, a*) also had major QTLs with overlapping marker intervals for leaf and peel colors, establishing the association of SNP markers to the trait. The QTLs harbored genes and transcription factors involved in lycopene and anthocyanin pigment biosynthesis. This is the first report of a high-density linkage map based on SNP markers in guava and QTL mapping for color characters in leaf, fruit peel and pulp. The genotyping information generated in this study can aid in genetic engineering and marker-assisted breeding in guava.

Leaf Color Classification and Expression Analysis of Photosynthesis-Related Genes in Inbred Lines of Chinese Cabbage Displaying Minor Variations in Dark-Green Leaves

The leaves of the Chinese cabbage which is most widely consumed come in a wide variety of colors. Leaves that are dark green can promote photosynthesis, effectively improving crop yield, and therefore hold important application and cultivation value. In this study, we selected nine inbred lines of Chinese cabbage displaying slight differences in leaf color, and graded the leaf color using the reflectance spectra. We clarified the differences in gene sequences and the protein structure of ferrochelatase 2 (BrFC2) among the nine inbred lines, and used qRT-PCR to analyze the expression differences of photosynthesis-related genes in inbred lines with minor variations in dark-green leaves. We found expression differences among the inbred lines of Chinese cabbage in photosynthesis-related genes involved in the porphyrin and chlorophyll metabolism, as well as in photosynthesis and photosynthesis-antenna protein pathway. Chlorophyll b content was significantly positively correlated with the expression of PsbQ, LHCA1_1 and LHCB6_1, while chlorophyll a content was significantly negatively correlated with the expression PsbQ, LHCA1_1 and LHCA1_2. Our results provide an empirical basis for the precise identification of candidate genes and a better understanding of the molecular mechanisms responsible for the production of dark-green leaves in Chinese cabbage.

Image-Based High-Throughput Phenotyping in Horticultural Crops

Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.

Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform

The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for plant breeding and management. However, it is difficult to align the point cloud data and extract accurate phenotypic traits of plant populations. In this study, high-throughput, time-series raw data of field maize populations were collected using a field rail-based phenotyping platform with light detection and ranging (LiDAR) and an RGB (red, green, and blue) camera. The orthorectified images and LiDAR point clouds were aligned via the direct linear transformation algorithm. On this basis, time-series point clouds were further registered by the time-series image guidance. The cloth simulation filter algorithm was then used to remove the ground points. Individual plants and plant organs were segmented from maize population by fast displacement and region growth algorithms. The plant heights of 13 maize cultivars obtained using the multi-source fusion data were highly correlated with the manual measurements (R² = 0.98), and the accuracy was higher than only using one source point cloud data (R² = 0.93). It demonstrates that multi-source data fusion can effectively improve the accuracy of time series phenotype extraction, and rail-based field phenotyping platforms can be a practical tool for plant growth dynamic observation of phenotypes in individual plant and organ scales.

Image-based phenotyping to estimate anthocyanin concentrations in lettuce

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 (I_red - I_green)/(I_red + I_green), 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.

Zinc Biofortification in Vitis vinifera: Implications for Quality and Wine Production

Nowadays, there is a growing concern about micronutrient deficits in food products, with agronomic biofortification being considered a mitigation strategy. In this context, as Zn is essential for growth and maintenance of human health, a workflow for the biofortification of grapes from the Vitis vinifera variety Fernão Pires, which contains this nutrient, was carried out considering the soil properties of the vineyard. Additionally, Zn accumulation in the tissues of the grapes and the implications for some quality parameters and on winemaking were assessed. Vines were sprayed three times with ZnO and ZnSO₄ at concentrations of 150, 450, and 900 g ha^-1 during the production cycle. Physiological data were obtained through chlorophyll a fluorescence data, to access the potential symptoms of toxicity. At harvest, treated grapes revealed significant increases of Zn concentration relative to the control, being more pronounced for ZnO and ZnSO₄ in the skin and seeds, respectively. After winemaking, an increase was also found regarding the control (i.e., 1.59-fold with ZnSO₄-450 g ha^-1). The contents of the sugars and fatty acids, as well as the colorimetric analyses, were also assessed, but significant variations were not found among treatments. In general, Zn biofortification increased with ZnO and ZnSO₄, without significantly affecting the physicochemical characteristics of grapes.

Inheritance and QTL Mapping for Flower Color in Salvia miltiorrhiza Bunge

Salvia miltiorrhiza Bunge is an outcross-pollinated plant with diverse flower colors, ranging from white to purple. To clarify the genetic basis of S. miltiorrhiza flower color, we crossed white-flowered S. miltiorrhiza f. alba with dark violet-flowered S. miltiorrhiza, and selfed F1 to obtain an F2 population. The RGB color system was used to describe the flower color of the parents, F1 progeny, and F2 individuals. Afterward, we used genotyping-by-sequencing (GBS) technology to construct a high-density linkage map of S. miltiorrhiza based on the F2 population. Finally, the linkage map was used to locate the QTLs of the genes that control flower color in S. miltiorrhiza. Through measurement and cluster analysis of the R, G, and B values of flowers from the parents, F1, and F2 individuals, it was found that the purple flower color of S. miltiorrhiza is a quantitative trait controlled by two loci of major genes. The genetic map contained 605 SNPs with a total length of 738.3 cM in eight linkage groups (LGs), and the average distance between two markers was 1.22 cM. Based on the constructed genetic map and the flower R, G, B, and R+G+B values, two QTLs were detected for flower color, located on LG4 and LG5. The results of this study lay the foundation for cloning genes that control flower color and studying the molecular mechanism of flower color regulation in S. miltiorrhiza.

Candidate resistance genes to foliar phylloxera identified at Rdv3 of hybrid grape

The foliage of the native grape species Vitis riparia and certain cold-hardy hybrid grapes are particularly susceptible to the insect pest phylloxera, Daktulosphaira vitifoliae Fitch. A previous study using a cold-hardy hybrid grape biparental F1 population (N~125) detected the first quantitative trait locus (QTL) for foliar resistance on chromosome 14, designated as resistance to Daktulosphaira vitifoliae 3 (Rdv3). This locus spans a ~7-Mbp (10-20 cM) region and is too wide for effective marker-assisted selection or identification of candidate genes. Therefore, we fine mapped the QTL using a larger F1 population, GE1783 (N~1023), and genome-wide rhAmpSeq haplotype markers. Through three selective phenotyping experiments replicated in the greenhouse, we screened 184 potential recombinants of GE1783 using a 0 to 7 severity rating scale among other phylloxera severity traits. A 500-kb fine mapped region at 4.8 Mbp on chromosome 14 was identified. The tightly linked rhAmpSeq marker 14_4805213 and flanking markers can be used for future marker-assisted breeding. This region contains 36 candidate genes with predicted functions in disease resistance (R genes and Bonzai genes) and gall formation (bifunctional 3-dehydroquinate dehydratase/shikimate dehydrogenase). Disease resistance genes suggest a traditional R-gene-mediated resistance mechanism often accompanied by a hypersensitive response, which has been widely studied in the plant pathology field. A novel resistance mechanism, non-responsiveness to phylloxera gall formation is proposed as a function of the bifunctional dehydratase gene, which plays a role in gallic acid biosynthesis and is important in gall formation. This study has implications for improvement of foliar phylloxera resistance in cold-hardy hybrid germplasm and is a starting place to understand the mechanism of resistance in crops to gall-forming insects.

Quantitative dissection of color patterning in the foliar ornamental coleus

Coleus (Coleus scutellarioides) is a popular ornamental plant that exhibits a diverse array of foliar color patterns. New cultivars are currently hand selected by both amateur and experienced plant breeders. In this study, we reimagine breeding for color patterning using a quantitative color analysis framework. Despite impressive advances in high-throughput data collection and processing, complex color patterns remain challenging to extract from image datasets. Using a phenotyping approach called "ColourQuant," we extract and analyze pigmentation patterns from one of the largest coleus breeding populations in the world. Working with this massive dataset, we can analyze quantitative relationships between maternal plants and their progeny, identify features that underlie breeder-selections, and collect and compare public input on trait preferences. This study is one of the most comprehensive explorations into complex color patterning in plant biology and provides insights and tools for exploring the color pallet of the plant kingdom.

Metabolic engineering in woody plants: challenges, advances, and opportunities

Woody plant species represent an invaluable reserve of biochemical diversity to which metabolic engineering can be applied to satisfy the need for commodity and specialty chemicals, pharmaceuticals, and renewable energy. Woody plants are particularly promising for this application due to their low input needs, high biomass, and immeasurable ecosystem services. However, existing challenges have hindered their widespread adoption in metabolic engineering efforts, such as long generation times, large and highly heterozygous genomes, and difficulties in transformation and regeneration. Recent advances in omics approaches, systems biology modeling, and plant transformation and regeneration methods provide effective approaches in overcoming these outstanding challenges. Promises brought by developments in this space are steadily opening the door to widespread metabolic engineering of woody plants to meet the global need for a wide range of sustainably sourced chemicals and materials.

Evaluation of Biochemical Juice Attributes and Color-Related Traits in Muscadine Grape Population

Biochemical juice attributes and color-related traits of muscadine grape genotypes have been investigated. For this study, 90 muscadine genotypes, including 21 standard cultivars, 60 breeding lines, and 9 Vitis x Muscadinia hybrids (VM), were evaluated. The biochemical properties of total soluble solids (TSS), titratable acidity, and TSS/Acid (T/A) ratio showed modest diversity among genotypes with a range of 10.3 °Brix, 2.1 mg tartaric acid/L, and 4.6, respectively. Nonetheless, the pH trait exhibited a tight range of 0.74 among the population with a minimum and maximum pH of 3.11 ± 0.12 and 3.85 ± 0.12. Color-related traits showed more deviation between individuals. Total anthocyanin content (TAC), luminosity index (L*), hue angle (h°), and chroma index (C*) displayed a range of 398 µg/g DW, 33.2, 352.1, and 24, respectively. The hierarchical clustering map classified the population into two large groups of colored and non-colored grapes based on L* and h°, suggesting the predominance of these two characters among the population. The colored berries genotypes clade was further divided into several sub-clades depending on C*, TAC, and TSS levels. The principal component analysis (PCA) separated the four-color characteristics into two groups with a negative correlation between them, L* and C* versus TAC and h°. Further, PCA suggested the positive influence of acidity in enhancing the different nutraceutical components. Despite the nature of anthocyanins as a member of phenolic compounds, a lack of significant correlation between TAC and nutraceutical-related traits was detected. The dissimilatory matrix analysis highlighted the muscadine individuals C11-2-2, E16-9-1, O21-13-1, and Noble as particular genotypes among the population due to enhanced color characteristics.