High-throughput Phenotyping Tool Research Articles

Host plant resistance in wheat to barley yellow dwarf viruses and their aphid vectors: a review.

Cereal aphids are vectors of at least 11 species of Barley Yellow Dwarf Viruses (BYDV) in wheat that alone and/or in combination can cause between 5%-80% grain yield losses. They establish complex virus-vector interactions, with variations in specificity and transmission efficiency that need to be considered for control purposes. In general, these viruses and vectors have a global distribution, however, BYDV-PAV is the most prevalent and abundant virus species worldwide, likely due to its vectoring efficiency and the wide distribution of its primary vector Rhopalosiphum padi. Host plant resistance (HPR) is an environmentally friendly, efficient and cost-effective tool to reduce crop losses to biotic stressors such as aphids and viruses. Finding resistance sources is paramount to breed for HPR. Currently, most of the resistance identified for aphids and BYDV derives from wheat related and wild relative species. However, breeding for HPR to BYDV and its vectors has additional challenges besides the source identification, for example, the lack of selection tools for certain aphid species, which likely prevents the development of elite wheat germplasm carrying resistance to these constraints. Nonetheless, modern technologies such as high-throughput phenotyping, genomic and advanced statistical tools can contribute to make HPR to aphids and BYDV more efficient. In the present review we describe the main sources of resistance, discuss the challenges and opportunities for incorporating the resistance in wheat breeding programs and present a workflow to breed for BYDV and its vectors in wheat.

ScreenSeed as a novel high throughput seed germination phenotyping method

A high throughput phenotyping tool for seed germination, the ScreenSeed technology, was developed with the aim of screening genotype responsiveness and chemical drugs. This technology was presently used with Arabidopsis thaliana seeds to allow characterizing seed samples germination behavior by incubating seeds in 96-well microplates under defined conditions and detecting radicle protrusion through the seed coat by automated image analysis. This study shows that this technology provides a fast procedure allowing to handle thousands of seeds without compromising repeatability or accuracy of the germination measurements. Potential biases of the experimental protocol were assessed through statistical analyses of germination kinetics. Comparison of the ScreenSeed procedure with commonly used germination tests based upon visual scoring displayed very similar germination kinetics.

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean.

Recent substantial advances in high-throughput field phenotyping have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits at early growth stages. Nevertheless, the implementation of large datasets generated by high-throughput phenotyping tools such as hyperspectral reflectance in cultivar development programs is still challenging due to the essential need for intensive knowledge in computational and statistical analyses. In this study, the robustness of three common machine learning (ML) algorithms, multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were evaluated for predicting soybean (Glycine max) seed yield using hyperspectral reflectance. For this aim, the hyperspectral reflectance data for the whole spectra ranged from 395 to 1005 nm, which were collected at the R4 and R5 growth stages on 250 soybean genotypes grown in four environments. The recursive feature elimination (RFE) approach was performed to reduce the dimensionality of the hyperspectral reflectance data and select variables with the largest importance values. The results indicated that R5 is more informative stage for measuring hyperspectral reflectance to predict seed yields. The 395 nm reflectance band was also identified as the high ranked band in predicting the soybean seed yield. By considering either full or selected variables as the input variables, the ML algorithms were evaluated individually and combined-version using the ensemble–stacking (E–S) method to predict the soybean yield. The RF algorithm had the highest performance with a value of 84% yield classification accuracy among all the individual tested algorithms. Therefore, by selecting RF as the metaClassifier for E–S method, the prediction accuracy increased to 0.93, using all variables, and 0.87, using selected variables showing the success of using E–S as one of the ensemble techniques. This study demonstrated that soybean breeders could implement E–S algorithm using either the full or selected spectra reflectance to select the high-yielding soybean genotypes, among a large number of genotypes, at early growth stages.

Haplotype analysis from unmanned aerial vehicle imagery of rice MAGIC population for the trait dissection of biomass and plant architecture.

Unmanned aerial vehicles (UAVs) are popular tools for high-throughput phenotyping of crops in the field. However, their use for evaluation of individual lines is limited in crop breeding because research on what the UAV image data represent is still developing. Here, we investigated the connection between shoot biomass of rice plants and the vegetation fraction (VF) estimated from high-resolution orthomosaic images taken by a UAV 10 m above a field during the vegetative stage. Haplotype-based genome-wide association studies of multi-parental advanced generation inter-cross (MAGIC) lines revealed four quantitative trait loci (QTLs) for VF. VF was correlated with shoot biomass, but the haplotype effect on VF was better correlated with that on shoot biomass at these QTLs. Further genetic characterization revealed the relationships between these QTLs and plant spreading habit, final shoot biomass and panicle weight. Thus, genetic analysis using high-throughput phenotyping data derived from low-altitude, high-resolution UAV images during early stages of rice growing in the field provides insights into plant growth, architecture, final biomass, and yield.

Using Near-infrared reflectance spectroscopy (NIRS) to predict glucobrassicin concentrations in cabbage and brussels sprout leaf tissue

BackgroundGlucobrassicin (GBS) and its hydrolysis product indole-3-carbinol are important nutritional constituents implicated in cancer chemoprevention. Dietary consumption of vegetables sources of GBS, such as cabbage and Brussels sprouts, is linked to tumor suppression, carcinogen excretion, and cancer-risk reduction. High-performance liquid-chromatography (HPLC) is the current standard GBS identification method, and quantification is based on UV-light absorption in comparison to known standards or via mass spectrometry. These analytical techniques require expensive equipment, trained laboratory personnel, hazardous chemicals, and they are labor intensive. A rapid, nondestructive, inexpensive quantification method is needed to accelerate the adoption of GBS-enhancing production systems. Such an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties. Near-infrared reflectance spectroscopy (NIRS) paired with partial least squares regression (PLSR) could be a useful tool to develop such a method.ResultsHere we demonstrate that GBS concentrations of freeze-dried tissue from a wide variety of cabbage and Brussels sprouts can be predicted using partial least squares regression from NIRS data generated from wavelengths between 950 and 1650 nm. Cross-validation models had R2 = 0.75 with RPD = 2.3 for predicting µmol GBS·100 g−1 fresh weight and R2 = 0.80 with RPD = 2.4 for predicting µmol GBS·g−1 dry weight. Inspections of equation loadings suggest the molecular associations used in modeling may be due to first overtones from O–H stretching and/or N–H stretching of amines.ConclusionsA calibration model suitable for screening GBS concentration of freeze-dried leaf tissue using NIRS-generated data paired with PLSR can be created for cabbage and Brussels sprouts. Optimal NIRS wavelength ranges for calibration remain an open question.

From lab to field: Open tools facilitating the translation of maize root traits

Abstract Variation of root traits is associated with soil resource acquisition under abiotic stresses. However, plant breeding programs targeting beneficial root traits are hampered by lacking appropriate phenotyping tools. The availability of high-throughput root phenotyping technologies for seedlings as well as for mature plants grown in the field enabled us to investigate trait translation from the lab to the field. Here we used the established phenotyping techniques Rhizoslides, Shovelomics, and the image-based root phenotyping software, Digital Imaging of Root Traits (DIRT) to phenotype root traits of thirty seedlings and mature maize donor lines from the Drought Tolerance Maize for Africa (DTMA) project. The experiments were carried out in a controlled-environment and a field trial under well-watered and drought conditions. We found significant correlation between seedlings seminal and crown root number in the field (r = 0.68). Primary root branching of seedlings was negatively correlated with crown root branching of field-grown plants (r=-0.54). Our study showed indirect translation of traits between primary root length (r = 0.29) and primary root branching (r = 0.20) with plant performance in the field under drought. Our results suggest that root traits of maize seedlings are predictive for mature maize root traits and plant performance in the field. Our findings suggest that using open tools for high-throughput root phenotyping to screen in maize seedling could be beneficial for breeding programs and paves a cost-efficient way for plant improvement and breeding programs in developing countries.

Rapid On-Site Phenotyping via Field Fluorimeter Detects Differences in Photosynthetic Performance in a Hybrid-Parent Barley Germplasm Set.

Crop productivity can be expressed as the product of the amount of radiation intercepted, radiation use efficiency and harvest index. Genetic variation for components of radiation use efficiency has rarely been explored due to the lack of appropriate equipment to determine parameters at the scale needed in plant breeding. On the other hand, responses of the photosynthetic apparatus to environmental conditions have not been extensively investigated under field conditions, due to the challenges posed by the fluctuating environmental conditions. This study applies a rapid, low-cost, and reliable high-throughput phenotyping tool to explore genotypic variation for photosynthetic performance of a set of hybrid barleys and their parents under mild water-stress and unstressed field conditions. We found differences among the genotypic sets that are relevant for plant breeders and geneticists. Hybrids showed lower leaf temperature differential and higher non-photochemical quenching, resembling closer the male parents. The combination of traits detected in hybrids seems favorable, and could indicate improved photoprotection and better fitness under stress conditions. Additionally, we proved the potential of a low-cost, field-based phenotyping equipment to be used routinely in barley breeding programs for early screening for stress tolerance.

A feasibility study of OCT for anatomical and vascular phenotyping of mouse embryo

The embryo phenotyping of genetic murine model is invaluable when investigating functions of genes underlying embryonic development and birth defect. Although traditional imaging technologies such as ultrasound are very useful for evaluating phenotype of murine embryos, the use of advanced techniques for phenotyping is desirable to obtain more information from genetic research. This letter tests the feasibility of optical coherence tomography (OCT) as a high-throughput phenotyping tool for murine embryos. Three-dimensional OCT imaging is performed for live and cleared mouse embryos in the late developmental stage (embryonic day 17.5). By using a dynamic focusing method and OCT angiography (OCTA) approach, our OCT imaging of the embryo exhibits rapid and clean visualization of organ structures deeper than 5 mm and complex microvasculature of perfused blood vessels in the murine embryonic body. This demonstration suggests that OCT imaging can be useful for comprehensively assessing embryo anatomy and angiography of genetically engineered mice.

Phenomics of elite heirlooms of peppers (Capsicum annuum L.) from the Spanish centre of diversity: Conventional and high-throughput digital tools towards varietal typification

Spain is a relevant secondary centre of diversity for Capsicum annuum peppers, especially for the bell types known as Pimiento Morrón or Pimiento de Morro. Thus, a myriad of highly regarded landraces adapted to a wide range of conditions can be found throughout the country, as a result of centuries of farmers breeding. Despite that, these materials lack of proper characterization, of paramount importance for farmers, breeders and germplasm management. In this regard, in addition to internationally accepted conventional descriptors, high-throughput digital phenotyping tools like Tomato Analyzer, a software originally developed to process scanned images of cut tomato fruits and to record a range of morphological parameters, may provide an important help towards exhaustive germplasm characterization. With this aim, 32 conventional and 35 Tomato Analyzer digital traits were used herein to characterize a large collection of C. annuum accessions from all Spanish regions, including Protected Designations of Origin and Protected Geographical Indications, with emphasis on Morrón peppers, in order to assess the diversity within Spanish elite germplasm and to test the efficiency of those methods to differentiate varietal types and closely related materials. A considerable amount of variation was found using both conventional and digital traits, even within Morrón pepper groups, reflecting the diversity of Spanish peppers in terms of plant and fruit morphology, essential for future breeding programs. Both conventional descriptors and digital parameters were able to distinguish varietal groups. However, on the whole, digital phenotyping was able to discriminate in a more accurate way. Most digital parameters were able to discriminate varietal groups into higher numbers of categories (≥4) than conventional traits (usually 2–4). In addition, the number of significant pairwise differences among varietal groups was considerably higher for digital parameters than for conventional descriptors, enabling a powerful separation, particularly relevant for closely related groups such as Morrón peppers. Likewise, as revealed by Principal Components Analysis, digital phenotyping allowed a more powerful intra-varietal separation compared to conventional descriptors. Finally, a subset of 4 conventional descriptors and 13 Tomato Analyzer traits were identified as the most discriminant to distinguish among closely related C. annuum accessions, explaining 81.81 % of total variance found by Principal Components Analysis. Fruit traits explained the highest percentage of variance for our collection.

Method for Characterization of Root Traits in Chickpea Germplasm for Legume Genomics and Breeding.

A semi-hydroponic phenotyping platform was constructed using inexpensive and easily obtained materials for characterizing root trait variability in a large set of chickpea (Cicer arietinum) germplasm. The system was designed to accommodate a large number of plants in a small area allowing relatively deeper root development, and thus serves as a high-throughput phenotyping tool for studying root dynamic growth. The root trait quantitative platform could provide accurate phenotyping data for parameterizing root models and for genome-wide association analysesor mapping studies of quantitative trait loci.

Hyppo-X: A Scalable Exploratory Framework for Analyzing Complex Phenomics Data.

Phenomics is an emerging branch of modern biology that uses high throughput phenotyping tools to capture multiple environmental and phenotypic traits, often at massive spatial and temporal scales. The resulting high dimensional data represent a treasure trove of information for providing an in-depth understanding of how multiple factors interact and contribute to the overall growth and behavior of different genotypes. However, computational tools that can parse through such complex data and aid in extracting plausible hypotheses are currently lacking. In this article, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits. We model the problem as one of unsupervised structure discovery, and use emerging principles from algebraic topology and graph theory for discovering higher-order structures of complex phenomics data. We present an open source software which has interactive visualization capabilities to facilitate data navigation and hypothesis formulation. We test and evaluate Hyppo-X on two real-world plant (maize) data sets. Our results demonstrate the ability of our approach to delineate divergent subpopulation-level behavior. Notably, our approach shows how environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. To the best of our knowledge, this effort provides one of the first approaches to systematically formalize the problem of hypothesis extraction for phenomics data. Considering the infancy of the phenomics field, tools that help users explore complex data and extract plausible hypotheses in a data-guided manner will be critical to future advancements in the use of such data.

Application of high-throughput phenotyping tool Tomato Analyzer to characterize Balkan Capsicum fruit diversity

Application of high-throughput phenotyping tool Tomato Analyzer to characterize Balkan Capsicum fruit diversity

Application of high-throughput phenotyping tool Tomato Analyzer to characterize Balkan Capsicum fruit diversity

For years, the main emphasis of industrial pepper production has been the creation of large-fruited high-yielding varieties. This has led to the exploitation of pepper (Capsicum annuum L.) varieties with a significantly narrow genetic background. The Balkan region has a range of valuable local landraces that are noticeably decreasing in prevalence and may be lost. In this study, we utilized Tomato Analyzer (TA) to characterize the intra- and inter-varietal type fruit diversity using 50 different fruit shape, size, and color descriptors of a Balkan pepper collection. The collection comprised of 168 diverse accessions and collected from 62 locations and 31 districts in six different Balkan countries. Fruit shape was the main trait used for visual accession grouping into 5 different varietal groupings of elongated, round, conical, bell, and pumpkin shape, respectively. Beyond visual grouping, hierarchical cluster analysis grouped the evaluated accessions into eight distinct clusters based on fruit shape, size, surface, and color. In total, 47 TA descriptors were identified to contribute to the total variation, with first two components explaining 54% of variation, and 90% of variation contained in 12 components. Among TA descriptors, fruit size and proximal/distal fruit end shape contributed to component 1 variation whereas fruit perimeter, area and color contributed to component 2. We hope that this research will assist pepper breeding and genetic resources communities to better understand Balkan pepper fruit diversity and develop pepper varieties with desirable fruit traits. Quantification of fruit diversity could be crucial for further investigation into the genetic determinants of fruit shape and size by a genome-wide association study.

Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality

BackgroundThe advances of hyperspectral technology provide a new analytic means to decrease the gap of phenomics and genomics caused by the fast development of plant genomics with the next generation sequencing technology. Through hyperspectral technology, it is possible to phenotype the biochemical attributes of rice seeds and use the data for GWAS.ResultsThe results of correlation analysis indicated that Normalized Difference Spectral Index (NDSI) had high correlation with protein content (PC) with RNDSI2 = 0.68. Based on GWAS analysis using all the traits, NDSI was able to identify the same SNP loci as rice protein content that was measured by traditional methods. In total, hyperspectral trait NDSI identified all the 43 genes that were identified by biochemical trait PC. NDSI identified 1 extra SNP marker on chromosome 1, which annotated extra 22 genes that were not identified by PC. Kegg annotation results showed that traits NDSI annotated 3 pathways that are exactly the same as PC. The cysteine and methionine metabolic pathway identified by both NDSI and PC was reported important for biosynthesis and metabolism of some of amino acids/protein in rice seeds.ConclusionThis study combined hyperspectral technology and GWAS analysis to dissect PC of rice seeds, which was high throughput and proven to be able to apply to GWAS as a new phenotyping tool. It provided a new means to phenotype one of the important biochemical traits for the determination of rice quality that could be used for genetic studies.

Physiological, genetic and molecular basis of drought resilience in sorghum [Sorghum bicolor (L.) Moench

Developing drought resistant cereal crops continues to be a major challenge in the climate change scenario. Sorghum is a C4 crop and its yield levels are highly affected by terminal drought stress under rainfed conditions. Efficient screening methods coupled with high throughput phenotyping techniques are required for breeding climate resilient crops. Plant adaptation to drought stress is manifested by several modifications at morphological, anatomical, physiological levels. Several mechanisms including osmotic adjustments, stay green, leaf rolling, waxyness on stem, root morphology and its architecture, transpiration efficiency, secretion of soluble solutes are known to play important role in bringing drought tolerance. Several breeding methods have been proposed and a single method may not hold good for all the crop species. Recombination, mutation and molecular breeding methods are mainly used for breeding drought resistant plants. At molecular level, several QTL’s governing pre- and post flowering drought tolerance have been identified in cereals and specifically in sorghum. At genome level, genes involved in proline metabolism, transmembrane ion transporter, abscisic acid metabolism, etc., are known to be upregulated under water deficit conditions. Recent studies on transcriptome analysis showed that genes which are expressed in sensitive plants are related to stress responses and oxy reduction reactions. Recently high throughput phenotyping tools have been developed to associate drought responsive traits to genomic regions governing quantitative traits. In this review, detailed mechanisms and breeding approaches related to drought stress in cereals and specifically in sorghum are discussed.

Automated phenotyping for early vigour of field pea seedlings in controlled environment by colour imaging technology.

Early vigour of seedlings is a beneficial trait of field pea (Pisum sativum L.) that contributes to weed control, water use efficiency and is likely to contribute to yield under certain environments. Although breeding is considered the most effective approach to improve early vigour of field pea, the absence of a robust and high-throughput phenotyping tool to dissect this complex trait is currently a major obstacle of genetic improvement programs to address this issue. To develop this tool, separate trials on 44 genetically diverse field pea genotypes were conducted in the automated plant phenotyping platform of Plant Phenomics Victoria, Horsham and in the field, respectively. High correlation between estimated plant parameters derived from the automated phenotyping platform and important early vigour traits such as shoot biomass, leaf area and plant height indicated that the derived plant parameters can be used to predict vigour traits in field pea seedlings. Plant growth analysis demonstrated that the “broken-stick” model fitted well with the growth pattern of all field pea genotypes and can be used to determine the linear growth phase. Further analysis suggested that the estimated plant parameters collected at the linear growth phase can effectively differentiate early vigour across field pea genotypes. High correlation between normalised difference vegetation indices captured from the field trial and estimated shoot biomass and top-view area confirmed the consistent performance of early vigour field pea genotypes under controlled and field environments. Overall, our results demonstrated that this robust screening tool is highly applicable and will enable breeding programs to rapidly identify early vigour traits and utilise germplasm to contribute to the genetic improvement of field peas.

Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor.

Hyperspectral sensors, especially the close-range hyperspectral camera, have been widely introduced to detect biological processes of plants in the high-throughput phenotyping platform, to support the identification of biotic and abiotic stress reactions at an early stage. However, the complex geometry of plants and their interaction with the illumination, severely affects the spectral information obtained. Furthermore, plant structure, leaf area, and leaf inclination distribution are critical indexes which have been widely used in multiple plant models. Therefore, the process of combination between hyperspectral images and 3D point clouds is a promising approach to solve these problems and improve the high-throughput phenotyping technique. We proposed a novel approach fusing a low-cost depth sensor and a close-range hyperspectral camera, which extended hyperspectral camera ability with 3D information as a potential tool for high-throughput phenotyping. An exemplary new calibration and analysis method was shown in soybean leaf experiments. The results showed that a 0.99 pixel resolution for the hyperspectral camera and a 3.3 millimeter accuracy for the depth sensor, could be achieved in a controlled environment using the method proposed in this paper. We also discussed the new capabilities gained using this new method, to quantify and model the effects of plant geometry and sensor configuration. The possibility of 3D reflectance models can be used to minimize the geometry-related effects in hyperspectral images, and to significantly improve high-throughput phenotyping. Overall results of this research, indicated that the proposed method provided more accurate spatial and spectral plant information, which helped to enhance the precision of biological processes in high-throughput phenotyping.

Ensemble Feature Selection for Plant Phenotyping: A Journey From Hyperspectral to Multispectral Imaging

Hyperspectral imaging is becoming an increasingly popular tool for high-throughput plant phenotyping, because it provides remarkable insights about the health status of plants. Feature selection is a key component in a hyperspectral image analysis, largely because a significant portion of spectral features are redundant and/or irrelevant, depending on the desired application. This paper presents an ensemble feature selection method to identify the most informative spectral features for practical applications in plant phenotyping. The hyperspectral data set contained the images of four wheat lines, each with a control and a salt (NaCl) treatment. To rank spectral features, six feature selection methods were used as the base for the ensemble: correlation-based feature selection, ReliefF, sequential feature selection, support vector machine-recursive feature elimination (SVM-RFE), LASSO logistic regression, and random forest. The best results were achieved by the ensemble of ReliefF, SVM-RFE, and random forest, which drastically reduced the dimension of the hyperspectral data set from 215 to 15 features, while improving the accuracy in classifying the salt-treated vegetation pixels from the control pixels by 8.5%. To transform the hyperspectral data set into a multispectral data set, six wavelengths as the center of broad multispectral bands around the most prominent features were determined by a clustering algorithm. The result of salt tolerance assessment of the four wheat lines using the derived multispectral data set was similar to that of the hyperspectral data set. This demonstrates that the proposed feature selection pipeline can be utilized for determining the most informative features and can be a valuable tool in the development of tailored multispectral cameras.

Case study: Estimation of sorghum biomass using digital image analysis with Canopeo

High-throughput plant phenotyping has revealed its importance by illuminating complex questions about plant growth, development, and response to the environment. Recently emerging technologies including un-maned vehicle as an image acquiring technology and machine learning as image analyzing technology have enabled the use of high-throughput phenotyping of plants for basic and applied sciences. Digital image analysis is one of methods for high throughput phenotyping. However, those techniques are not accessible because of high costs in general and the lack of man power to efficiently operate them. Here, we propose an easily accessible high-throughput phenotyping tool for biomass for many researchers. Four sorghum cultivars were evaluated for their height, a variable well documented for its high correlation with biomass, with both hand-collection and image taking in order to determine if image analysis for biomass in sorghum could replace hand data collection of plant height. Vertical plant images were obtained using a digital camera at one-week interval for 5 weeks. Plant height and node height were hand-collected at the same time. Images were analyzed using a tool, called Canopeo (http://www.canopeoapp.com). Biomass was measured indirectly as percentage units by counting the green pixels in each image. Strong and significant correlations between the percentage of green color and plant height and between the percentage of green color and node height were found for each cultivar and measurement. The results of this proof-of-concept study strongly indicate that digital image analysis could replace the intense labor needed to collect data related to biomass.

Quantitative comparison of individual sugars in cultivars and hybrids of taro [Colocasia esculenta (L.) Schott]: implications for breeding programs

Taro [Colocasia esculenta (L.) Schott] is cultivated for its starchy corm consumed baked or boiled and processed in snacks (chips or French fries) to satisfy growing urban markets. Most consumers prefer non sweet taros. High content of sucrose (non-reducing sugar), glucose and fructose (reducing sugars) represent undesirable characteristics because they cause browning of the snacks. Breeding taro for improved corm quality is complex and phenotypic recurrent selection is impaired by the long growth cycle and the low vegetative propagation ratio. New high-throughput phenotyping tools are needed to select suitable hybrids in early clonal generations. The aim of the present study was to develop an HPTLC protocol for the quantitation of sugars in the fresh corm (FW). The individual sugars values in 60 hybrids released by four different breeding programs were compared with 300 cultivars from six different countries. Sucrose, glucose, fructose, maltose and ribose were quantitated. Mean total sugars varied from 1.83 to 6.28%FW in hybrids and from 1.32 to 7.69%FW in cultivars. The ratio sucrose/reducing sugars varied from 0.06 to 4.34 in hybrids and from 0.04 to 4.82 in cultivars. The protocol developed in this study is rapid, cost efficient, environment-friendly and more accurate than previous techniques based on dry matter because sample preparation is known to affect chemical composition of individual sugars. This technique can be used in taro breeding programmes for the early detection of undesirable hybrids with high levels of reducing sugars.