Accelerating Crop Breeding in the 21st Century: A Comprehensive Review of Next Generation Phenotyping Techniques and Strategies

Year 2023, Volume: 9 Issue: 2, 160 - 171, 01.08.2023

Renu Munjal Jogender Benıwal Arjoo Dhundwal Alisha Goyal Anita Kumarı Rishi K. Behl

Abstract

Biotic and abiotic stress factors significantly impede crop productivity and lead to substantial economic losses. Given the
projected human population of 9 billion by 2050 and the necessity to double current food production to meet the demands
of this growing populace, enhancing crop productivity has become a pressing concern. In recent years, substantial progress
has been made in the field of high-throughput phenotyping technologies, enabling precise measurements of desired traits
and efficient screening of large plant populations under diverse environmental conditions. These advancements involve
the integration of advanced robotics, high-tech sensors, imaging systems, and computing power to unravel the genetic
basis of complex traits associated with plant growth and development. Furthermore, advanced bioinformatics tools
have emerged to analyze the vast amounts of multi-dimensional, high-resolution data collected through phenotyping
at both the genetic and whole-plant levels, considering specific environmental conditions and management practices.
The integration of genotyping and phenotyping approaches facilitates a comprehensive understanding of gene functions
and their responses to various environmental stimuli. This integrated approach holds significant promise for identifying
solutions to the major constraints limiting crop production. This review focuses on the recent breakthroughs in plant
phenomics and various imaging techniques, emphasizing the applications of different high-throughput technologies in
both controlled and natural field conditions. These advancements are crucial steps towards addressing the challenges
posed by stress factors and ultimately achieving sustainable and increased crop yields to meet the demands of the growing
global population.

Keywords

Genotyping, high throughput, imaging, phenotyping, sensors, stress

References

• Ahmed HG, Zeng Y, Fiaz S and Rashid AR, (2023). Applications of high-throughput phenotypic phenomics. Sustainable Agriculture in the Era of the OMICs Revolution. (pp. 119-134). Cham: Springer International Publishing.
• Anonymous, (2017). United Nations Department of Economic and Social Affairs Population Division. Available online: http://www.unpopulation.org.
• Arend D, Junker A, Scholz U, Schüler D, Wylie J and Lange M, (2016). PGP repository: A plant phenomics and genomics data publication infrastructure. Database (Oxford).
• Atkinson J A, Jackson RJ, Bentley AR, Ober E and Wells DM, (2018). Field phenotyping for the future. Annu. Plant Rev. Online, 1:1-18.
• Basak JK, Qasim W, Okyere FG, Khan F, Lee YJ, Park J and Kim HT, (2019). Regression analysis to estimate morphology parameters of pepper plant in a controlled greenhouse system. Journal of Biosystems Engineering, 44:57-68.
• Benamar A, Pierart A, Baecker V, Avelange-Macherel MH, Rolland A, Gaudichon S, di Gioia L and Macherel D, (2013). Simple system using natural mineral water for high-throughput phenotyping of Arabidopsis thaliana seedlings in liquid culture. Int. J. High Throughput Screen, 4:1-15.
• Bilder RM, Sabb FW, Cannon TD, London ED, Jentsch JD, Parker DS, Poldrack RA, Evans C and Freimer NB, (2009). Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience, 164:30-42.
• Bogard M, Ravel C, Paux E, Bordes J, Balfourier F, Chapman SC, Le Gouis J and Allard V, (2014). Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model. Journal of Experimental Botany, 65:5849-65.
• Close T, Riverside UC and Last R, (2011). National science foundation phenomics: Genotype to phenotype, a report of the NIFA-NSF phenomics workshop. Cobb JN, Declerck G, Greenberg A, Clark R and McCouch S, (2013). Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype phenotype relationships and its relevance to crop improvement. Theoretical and Applied Genetics, 126:867-87.
• Coppens F, Wuyts N, Inze D and Dhondt S, (2017). Unlocking the potential of plant phenotyping data through integration and data driven approaches. Current Opinion in Plant Biology, 4:58-63.
• Crossa J, Fritsche-Neto R, Montesinos-Lopez OA, Costa-Neto G, Dreisigacker S, Montesinos- Lopez A and Bentley AR, (2021). The modern plant breeding triangle: Optimizing the use of genomics, phenomics, and enviromics data. Frontiers in Plant Science, 12:651480.
• Deery DM, Rebetzke GJ, Jimenez-Berni JA, James RA, Condon AG, Bovill WD and Furbank RT, (2016). Methodology for high-throughput field phenotyping of canopy temperature using airborne thermography. Frontiers in Plant Science, 7:1808.
• Flavel RJ, GuppyCN TM, Watt M, McNeill A and Young IM, (2012). Non-destructive quantification of cereal roots in soil using high-resolution X-ray tomography. Journal of Experimental Botany, 63:2503-2511.
• Freimer N and Sabatti C, (2003). The human phenome project. Nature Genetics, 34:15-21.
• Furbank RT and Tester M, (2011). Phenomicstechnologies to relieve the phenotyping bottleneck. Trends in Plant Science, 16:635-644.
• Golzarian MR, Frick RA, Rajendran K, Berger B, Roy S, Tester M and Lun DS, (2011). Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods, 7:2. Gosa SC, Lupo Y and Moshelion M, (2019). Quantitative and comparative analysis of wholeplant performance for functional physiological traits phenotyping: new tools to support prebreeding and plant stress physiology studies. Plant science, 282:49-59.
• Guo WCM, Singh A, Swetnam TL, Merchant NS, Soumik S, Asheesh K and Baskar G, (2021). UAS-Based plant phenotyping for research and breeding applications. Plant Phenomics, 11:253- 269.
• Halperin O, Gebremedhin A, Wallach R and Moshelion M, (2017). High‐throughput physiological phenotyping and screening system for the characterization of plant-environment interactions. The Plant Journal, 89(4):839-850. Hartmann A, Czauderna T, Hoffmann R, Stein N and Schreiber F, (2011). HTPheno: An image analysis pipeline for high-throughput plant phenotyping. BMC Bioinformatics, 12(1):1-9.
• Houle D, Govindaraju DR and Omholt S, (2010). Phenomics: The next challenge. Nature Reviews Genetics, 11:855-866.
• Huang JR, Liao HJ, Zhu YB, Sun JY, Sun QH and Liu XD, (2012). Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis). Computers and Electronics in Agriculture, 82:100-107.
• Kumar J, Pratap A. and Kumar S, (2015). Plant Phenomics: An overview. Phenomics in crop plants: Trends, options and limitations, 1-10.
• Kwon TR, Kim KH, Yoon HJ, Lee SK, Kim BK and Siddiqui ZS, (2015). Phenotyping of plants for drought and salt tolerance using infra-red thermography. Plant Breed. Biotech., 3(4):299-307.
• Li M, Xu J, Zhang N, Shan J, and Yao S, (2016). Study on the factors affecting grain yield measurement system. 2016 International Conference on Service Science, Technology and Engineering, 566-572.
• Li Y, Xiao J, Chenn L, Huang X, Cheng Z, Han B, Zhang Q and Wu C, (2018). Rice functional genomics research: past decade and future. Molecular Plant, 11:359-380.
• Liu ZY, Shi JJ, Zhang LW and Huang JF, (2010). Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification. Journal of Zheijang University SCIENCE-Biomedicine & Biotechnology, 11:71-78. Matias FI, Caraza-Harter MV and Endelman JB, (2020). FIELDimageR: An R package to analyze orthomosaic images from agricultural field trials. Plant Phenome J., 3:1-6.
• Montesinos-López OA, Montesinos-López A, Crossa J, Toledo FH, PérezHernández O and Eskridge KM, (2016). A genomic Bayesian multitrait and multi-environment model. G3 Genes/Genomes/ Genetics, 6:2725-2744.
• Morisse M, Wells DM, Millet EJ, Lillemo M, Fahrner S, Cellini F, Lootens P, Muller O, Herrera JM, Bentley AR and Janni M, (2022). A European perspective on opportunities and demands for field-based crop phenotyping. Field Crops Research, 276:108371.
• Nguyen HT and Lee BW, (2006). Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. European Journal of Agronomy, 24: 349-356.
• Omari MK, Lee J, Faqeerzada MA, Joshi R, Park E and Cho BK, (2020). Digital image-based plant phenotyping: A review. Korean Journal of Agricultural Science, 47(1):119-130.
• Perez-Sanz F, Navarro PJ and Egea-Cortines M, (2017). Plant phenomics: An overview of image acquisition technologies and image data analysis algorithms. GigaScience, 6(11):1-18.
• Poorter H, Bühler J, van Dusschoten D, Climent J and Postma JA, (2012). Pot size matters: A metaanalysis of the effects of rooting volume on plant growth. Functional Plant Biology, 39(11):839-850.
• Rebetzke GJ, Condon AG, Richards RA and Farquhar GD, (2013). Selection for reduced carbon isotope discrimination increases aerial biomass and grain yield of rainfed bread wheat. Crop Sci., 42:739- 745.
• Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, Boote KJ, Folberth C, Glotter M, Khabarov N and Neumann K, (2014). Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences, 111(9): 3268-3273.
• Saoirse R, Tracy KA, Nagel JA, Postma HF, Anton W and Michelle W, (2020). Crop improvement from phenotyping roots: Highlights reveal expanding opportunities. Trends in Plant Science, 25(1):105- 118.
• Shi C, Zhao L, Zhang X, Lv G, Pan Y and Chen F, (2019). Gene regulatory network and abundant genetic variation play critical roles in heading stage of polyploidy wheat. BMC Plant Biology, 19(1):1-16.
• Stahl A, Wittkop B and Snowdon RJ, (2020). Highresolution digital phenotyping of water uptake and transpiration efficiency. Trends in Plant Science, 25(5):429-433.
• Tardieu F and Tuberosa R, (2010). Dissection and modelling of abiotic stress tolerance in plants. Current Opinion in Plant Biology, 13(2):206-212.
• Tardieu F and Schurr U, (2009). ‘White paper’ on plant phenotyping. The main outcome of the EPSO workshop on plant phenotyping, Ju¨lich, 1-4.
• Tardieu F, Cabrera-Bosquet L, Pridmore T and Bennett M, (2017). Plant phenomics, from sensors to knowledge. Current Biology, 27(15): R770-R783.
• Vadez V, Kholova J, Medina Kakkera A and Anderberg H, (2014). Transpiration efficiency: new insights into an old story. Journal of Experimental Botany, 65(21):6141-6153.
• Walter A, Silk WK and Schurr U, (2009). Environmental effects on spatial and temporal patterns of leaf and root growth. Annual Review of Plant Biology, 60:279-304.