The Power of Phenomics: Improving Genebank Value and Utility.

Genebanks are considered the “crown jewels” of research organizations, as they safeguard valuable plant genetic resources and address the emerging challenges posed by climate-related constraints (Anglin et al., 2018Anglin N.L. Amri A. Kehel Z. Ellis D. A case of need: linking traits to genebank accessions.Biopreserv. Biobanking. 2018; 16: 337-349https://doi.org/10.1089/bio.2018.0033Crossref PubMed Scopus (30) Google Scholar). Currently, 7.4 million accessions have been conserved in 1750 gene banks worldwide. Of these, only 10% are being used for crop improvement (Commission on Genetic Resources for Food and Agriculture, 2010). The limited availability of passport data and the challenge of characterizing trait attributes of accessions at large scale are some of the challenges to relate their true value (Tadesse et al., 2019Tadesse W. Sanchez-Garcia M. Assefa S.G. Amri A. Bishaw Z. Ogbonnaya F.C. Baum M. Genetic gains in wheat breeding and its role in feeding the world.Crop Breed. Genet. Genom. 2019; 1e190005https://doi.org/10.20900/cbgg20190005Crossref Google Scholar). Obtaining traits such as agronomic or nutritional characteristics requires extensive field phenotyping and multiple site testing over several seasons. This will create value with accurate evaluation of genotype-by-environmental variation in a given trait. The use of low-cost high-throughput phenotyping (HTP) methods to characterize the genotypic and phenotypic representations of genebank genetic resources, otherwise known as genebank phenomics, will help in understanding crops’ potential for use in breeding programs with desirable traits. Technologies such as imaging, spectroscopy, and robotics enable rapid and non-destructive measurements of observable characteristics such as plant height, leaf area, seed size, disease resistance, and abiotic stress tolerance. This information can help breeders better understand the genetic diversity and relationships among accessions, inform conservation strategies, and support the development of predictive models for crop performance under different environmental conditions. Genebank phenomics can increase the productivity and profitability of smallholder farming systems by capturing information about germplasm material that requires less resource (such as water or fertilizers), thereby contributing to global food security. A good example is the peanut accession PI 203396, which was acquired from Brazil in 1952 and added to the genebank of US Department of Agriculture (Griffin, GA, USA). It was not documented to confer resistance to tomato spotted wilt virus but only to leaf spot, making it challenging for new peanut breeders to recognize its value (Anglin et al., 2018Anglin N.L. Amri A. Kehel Z. Ellis D. A case of need: linking traits to genebank accessions.Biopreserv. Biobanking. 2018; 16: 337-349https://doi.org/10.1089/bio.2018.0033Crossref PubMed Scopus (30) Google Scholar). However, its incorporation into commercial peanut varieties has contributed over $200 million annually to the US economy, demonstrating the importance of characterizing and documenting poorly represented genebank accessions for valuable traits in crop improvement. The CGIAR centers IPGRI (Nguyen and Norton, 2020Nguyen G.N. Norton S.L. Genebank phenomics: a strategic approach to enhance value and utilization of crop germplasm.Plants. 2020; 9: 817Crossref Scopus (31) Google Scholar) and Bioversity International (now the Alliance of Bioversity International and CIAT) have published phenotypic descriptors for over 100 crops to ensure consistent and comparable data across genebanks. HTP methods using sensors, LiDAR (light detection and ranging), or RGB cameras mounted on a ground-based or unmanned aerial vehicle (UAV) accurately measure plant traits such as plant height, biomass, yield potential, and lodging resistance in greenhouse and field settings (Ghamkhar et al., 2019Ghamkhar K. Irie K. Hagedorn M. Hsiao J. Fourie J. Gebbie S. Hoyos-Villegas V. George R. Stewart A. Inch C. et al.Real-time, non-destructive and in-field foliage yield and growth rate measurement in perennial ryegrass (Lolium perenne L.).Plant Methods. 2019; 15: 72https://doi.org/10.1186/s13007-019-0456-2Crossref PubMed Scopus (18) Google Scholar; Yang et al., 2020Yang W. Feng H. Zhang X. Zhang J. Doonan J.H. Batchelor W.D. Xiong L. Yan J. Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives.Mol. Plant. 2020; 13: 187-214https://doi.org/10.1016/j.molp.2020.01.008Abstract Full Text Full Text PDF PubMed Scopus (330) Google Scholar). HTP can also predict grain yield indirectly using traits such as early vigor, height, canopy properties, and biomass. Automated HTP platforms are being used to measure traits of a diverse range of perennial ryegrass genotypes and an in vitro collection of (1617) banana accessions at our organizations, AgResearch in New Zealand, and the International Musa Germplasm Transit Centre, respectively. The International Musa Germplasm Transit Centre employs a quick phenotyping platform called “Bananatainer” that can simulate diverse climatic conditions to cultivate up to 504 plants for their ability to withstand abiotic stress (Van den houwe et al., 2020Van den houwe I. Chase R. Sardos J. Ruas M. Kempenaers E. Guignon V. Massart S. Carpentier S. Panis B. Rouard M. et al.Safeguarding and using global banana diversity: a holistic approach.CABI Agric. Biosci. 2020; 1: 15https://doi.org/10.1186/s43170-020-00015-6Crossref Scopus (21) Google Scholar). Deep learning methods including convolutional neural networks have been successfully applied in the HTP platform. In a recent study, researchers used multivariate clustering to identify maize accessions with homogeneous or heterogeneous phenotypes using an extensive image collection of 19 867 maize cobs obtained from 3449 images of 2484 accessions from the Peruvian maize genebank at Universidad Nacional Agraria La Molina. This successful implementation of deep learning in genebank phenomics demonstrates its high potential for future applications in plant breeding and agriculture (Kienbaum et al., 2021Kienbaum L. Correa Abondano M. Blas R. Schmid K. DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics.Plant Methods. 2021; 17: 91https://doi.org/10.1186/s13007-021-00787-6Crossref PubMed Scopus (5) Google Scholar). Genebanks evaluate the physical characteristics of seeds such as size, shape, color, vigor, and varietal identification as a part of their quality assurance activities. Automated systems for rapid, accurate,and cost-effective phenotyping of seeds, such as imaging or using cameras or sensors, will enable genebanks to efficiently measure characters of hundreds of seeds, with more accuracy than human eyes, which can be useful in plant breeding and genetic research. Algorithms can be trained to recognize and analyze images, allowing for rapid and accurate characterization of seed traits. For example, a portable conveyer-based imaging system provides a rapid and accurate means of phenotyping large numbers of seeds. Imaging systems and computer-based seed phenotyping programs have been utilized to characterize seed morphological traits of 589 soybean accessions and genotypes (53 909 seeds) at the National Agrobiodiversity Center Jeonju, (Korea) based on the guidelines of International Union for the Protection of New Varieties of Plants. The International Rice Research Institute (IRRI) maintains the International Rice genebank, which houses over 132 000 rice accessions. IRRI uses advanced phenotyping tools such as Videometer (https://videometer.com/) and germination Scanalyzer to identify and compare new seed samples and perform seed analysis and viability monitoring (Lee et al., 2020Lee J.S. Chebotarov D. Platten J.D. McNally K. Kohli A. Advanced strategic research to promote the use of rice genetic resources.Agronomy. 2020; 10: 1629https://doi.org/10.3390/agronomy10111629Crossref Scopus (6) Google Scholar). The use of UAVs equipped with thermal imaging for drought tolerance screening and field-risk management in IRRI is also a promising tool for preserving the genetic diversity of seed samples through the regeneration cycle of genebank accessions. Phenomics can also be used to analyze plant content and compound traits such as pigment concentration, nutrient content, and overall quality. Biofortification is a process that aims to increase the bioavailability of micronutrients in crops with desirable pigment traits, such as high carotenoid or chlorophyll content, to benefit the human population. Biofortification has been achieved in staple crops like wheat, rice, maize, cassava, pearl millet, beans, cassava, and sweet potato in Asia and Africa. In a study that compared traditional laboratory-based methods with high-throughput image-based phenotyping to measure anthocyanin, chlorophyll, and carotenoid content in 30 red lettuce genotypes, the image-based method offered a better alternative for selecting plants with high pigments to develop biofortified crops (Maciel et al., 2019Mascarenhas Maciel G. de Araújo Gallis R.B. Barbosa R.L. Pereira L.M. Siquieroli A.C.S. Vitória Miranda Peixoto J. Image phenotyping of inbred red lettuce lines with genetic diversity regarding carotenoid levels.Int. J. Appl. Earth Obs. Geoinf. 2019; 81: 154-160https://doi.org/10.1016/j.jag.2019.05.016Crossref Scopus (19) Google Scholar). Another study used red, green, blue camera image analysis to predict total carotenoid content in cassava roots. Digital images of 228 cassava genotypes from the Embrapa Cassava Germplasm Bank were analyzed using colorimetric indices to extract the intensity of yellow color and lightness. The study found that digital image analysis is a cost-effective and efficient alternative for developing total carotenoid content phenotyping tools in cassava roots with high predictive ability (de Carvalho et al., 2022de Carvalho R.R.B. Marmolejo Cortes D.F. Bandeira e Sousa M. de Oliveira L.A. de Oliveira E.J. Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction.PLoS One. 2022; 17e0263326https://doi.org/10.1371/journal.pone.0263326Crossref Scopus (4) Google Scholar). Technologies such as spectral imaging and sensor technologies have not yet been fully utilized in plant science, but they hold potential for investigating plant nutrients, including vitamins and macronutrients. Plant phenomics can also measure forage and crop quality, such as flavor and digestibility, and eventually metabolizable energy. Ground truthing, such as sensory assessments or fruit firmness measurements, may be required before standardization of data across genebanks. Phenomics is also utilized in screening germplasm for plant disease resistance. Imaging sensors and machine learning (ML) models are used to identify links between resistance to diseases and other plant traits in order to pick strategies to manage germplasm collections with a disease-based focus. For example, a relevant study in this field, the combination of spectral vegetation indices in random forest ML models, was used to predict yellow rust scores in wheat genotypes obtained from the Nordic Genebank (Koc et al., 2022Koc A. Odilbekov F. Alamrani M. Henriksson T. Chawade A. Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning.Plant Methods. 2022; 18: 30https://doi.org/10.1186/s13007-022-00868-0Crossref PubMed Scopus (9) Google Scholar). Numerous studies have reported using ML approaches to identify different levels of stress such as the aflatoxin level in maize (Yao et al., 2013Yao H. Hruska Z. Kincaid R. Brown R.L. Bhatnagar D. Cleveland T.E. Detecting maize inoculated with toxigenic and atoxigenic fungal strains with fluorescence hyperspectral imagery.Biosyst. Eng. 2013; 115: 125-135https://doi.org/10.1016/j.biosystemseng.2013.03.006Crossref Scopus (73) Google Scholar), leaf rust in wheat (Ashourloo et al., 2014Ashourloo D. Mobasheri M.R. Huete A. Developing two spectral disease indices for detection of wheat leaf rust (Pucciniatriticina).Rem. Sens. 2014; 6: 4723-4740Crossref Scopus (109) Google Scholar), or powdery mildew in cucumber (Lin et al., 2019Lin K. Gong L. Huang Y. Liu C. Pan J. Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network.Front. Plant Sci. 2019; 10: 155https://doi.org/10.3389/fpls.2019.00155Crossref PubMed Scopus (128) Google Scholar). Developing labeled, broad-spectrum plant disease stress datasets across various plant species will allow for the development of disease-resistance libraries and prevent data duplication. In taking this approach, Selvaraj et al., 2019Selvaraj M.G. Vergara A. Ruiz H. Safari N. Elayabalan S. Ocimati W. Blomme G. AI-powered banana diseases and pest detection.Plant Methods. 2019; 15 (92–11)https://doi.org/10.1186/s13007-019-0475-zCrossref PubMed Scopus (179) Google Scholar have developed a smartphone application called Tumaini that uses deep learning-based models to classify five major banana pests and diseases. Crop phenomics using aerial vehicles or satellites help genebanks to screen and record crop growth from above rather than on the ground. UAVs or aircraft equipped with high-resolution cameras and sensors can capture images and data of crops and vegetation from above. Satellite-based phenomics, on the other hand, gather data on plant growth and development over large areas (Figure 1). Although these methods may sound expensive for genebanks, in the very near future, its advantages to ground-based imaging for screening broad ranges of trait statuses within germplasm collections of the same species will be pronounced. Phenomics technologies are crucial for unlocking the potential of genebanks to address global crop production challenges. Phenotypic characterization enables early and efficient identification of germplasm with specific traits of interest, enhancing the value of breeding programs. Collaboration among genebanks through facilitators such as Divseek International can enable the affordability and standardization of phenomics technologies. Scaling up germplasm screening using aerial and satellite technologies is the next stage for genebank materials characterization in the 21st century. Thanks to the Crops for Nutrition and Health at the Alliance of Bioversity International and CIAT for encouraging this writeup. No conflict of interest is declared.