Abstract
The success of plant breeding is critical to meet planetary challenges of food and water security for the world’s growing population. Plant breeders need to continuously develop new sustainable varieties with stable high yields despite changing climate with high resource use efficiency and pest/disease tolerance. In the current era of Breeding 4.0 where specific parts in the genome can be targeted, technological advances along with the data revolution greatly enhance the capacity of researchers to develop sustainable systems around the world. The evolution of breeding over the years correlates with the advancements in data analytics, and Breeding 4.0 uses prescriptive analytics to make informed decisions. The success of these modern breeding initiatives depends on intentional and standardized data management which not only ensures harmonization of multidimensional data (like genomics, phenotypic, and environment) from an organization but also facilitates community integration for sharing of resources. In modern digital agriculture, machine learning technologies will continue to play an important role in advancing research and product development within the agricultural industry. In the next phase of Breeding 5.0, unleashing the power of integrated technologies and big data will enable crop production systems to identify genotypes with an optimized phenotype for each different environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. In: BTW (Workshops), pp 79–88
Anten NPR, Vermeulen PJ (2016) Tragedies and crops: understanding natural selection to improve cropping systems. Trends Ecol Evol 31(6):429–439. https://doi.org/10.1016/j.tree.2016.02.010
Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE (2018) Translating high-throughput phenotyping into genetic gain. Trends Plant Sci 23(5):451–466. https://doi.org/10.1016/j.tplants.2018.02.001
Arivazhagan S, Shebiah RN, Ananthi S, Varthini SV (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int 15(1):211–217
Arnaud E, Cooper L, Shrestha R, Menda N, Nelson RT, Matteis L, Skofic M, Bastow R, Jaiswal P, Mueller LA (2012) Towards a reference plant trait ontology for modeling knowledge of plant traits and phenotypes. In: KEOD, pp 220–225
Atabay HA (2017) Deep residual learning for tomato plant leaf disease identification. J Theor Appl Inf Technol 95(24):6800–6808
Bai G, Jenkins S, Yuan W, Graef GL, Ge Y (2018) Field-based scoring of soybean iron deficiency chlorosis using RGB imaging and statistical learning. Front Plant Sci 9:1002. https://doi.org/10.3389/fpls.2018.01002
Baye TM, Abebe T, Wilke RA (2011) Genotype-environment interactions and their translational implications. Per Med 8(1):59–70. https://doi.org/10.2217/pme.10.75
Bell G, Hey T, Szalay A (2009) Beyond the data deluge. Science 323(5919):1297–1298. https://doi.org/10.1126/science.1170411
Ben-Ari G, Lavi U (2012) 11 - Marker-assisted selection in plant breeding. In: Altman A, Hasegawa PM (eds) Plant biotechnology and agriculture. Academic, San Diego, pp 163–184. https://doi.org/10.1016/B978-0-12-381466-1.00011-0
Bingol K (2018) Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High Throughput 7(2):9. https://doi.org/10.3390/ht7020009
Bolger AM, Poorter H, Dumschott K, Bolger ME, Arend D, Osorio S, Gundlach H, Mayer KFX, Lange M, Scholz U, Usadel B (2019) Computational aspects underlying genome to phenome analysis in plants. Plant J 97(1):182–198. https://doi.org/10.1111/tpj.14179
Brodsky A, Shao G, Krishnamoorthy M, Narayanan A, Menascé D, Ronay A (2017) Analysis and optimization based on reusable knowledge base of process performance models. Int J Adv Manuf Technol 88:337–357. https://doi.org/10.1007/s00170-00016-08761-00177
Bustos-Korts D, Malosetti M, Chapman S, van Eeuwijk F (2016) Modelling of genotype by environment interaction and prediction of complex traits across multiple environments as a synthesis of crop growth modelling, genetics and statistics. In: Yin X, Struik PC (eds) Crop systems biology: narrowing the gaps between crop modelling and genetics. Springer, Cham, pp 55–82. https://doi.org/10.1007/978-3-319-20562-5_3
Chen K, Wang Y, Zhang R, Zhang H, Gao C (2019) CRISPR/Cas genome editing and precision plant breeding in agriculture. Annu Rev Plant Biol 70(1):667–697. https://doi.org/10.1146/annurev-arplant-050718-100049
Cobb JN, Juma RU, Biswas PS, Arbelaez JD, Rutkoski J, Atlin G, Hagen T, Quinn M, Ng EH (2019) Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder’s equation. Theor Appl Genet 132(3):627–645. https://doi.org/10.1007/s00122-019-03317-0
Collard BCY, Mackill DJ (2008) Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos Trans R Soc Lond B Biol Sci 363(1491):557–572. https://doi.org/10.1098/rstb.2007.2170
Cortes E (2017) Plant disease classification using convolutional networks and generative adverserial networks. Stanford University Reports, Stanford
Crick F (1970) Central dogma of molecular biology. Nature 227(5258):561
Crossa J, Perez-Rodriguez P, Cuevas J, Montesinos-Lopez O, Jarquin D, de Los CG, Burgueno J, Gonzalez-Camacho JM, Perez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X, Gowda M, Roorkiwal M, Rutkoski J, Varshney RK (2017) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22(11):961–975. https://doi.org/10.1016/j.tplants.2017.08.011
Dhaygude SB, Kumbhar NP (2013) Agricultural plant leaf disease detection using image processing. Int J Adv Res Electrical Electron Instrum Eng 2(1):599–602
Duncan EJ, Gluckman PD, Dearden PK (2014) Epigenetics, plasticity, and evolution: How do we link epigenetic change to phenotype? J Exp Zool B Mol Dev Evol 322(4):208–220
Duvick DN (2001) Biotechnology in the 1930s: the development of hybrid maize. Nat Rev Genet 2(1):69
Eriksson D (2019) The evolving EU regulatory framework for precision breeding. Theor Appl Genet 132(3):569–573. https://doi.org/10.1007/s00122-018-3200-9
Evenson RE, Gollin D (2003) Assessing the impact of the green revolution, 1960 to 2000. Science 300(5620):758–762. https://doi.org/10.1126/science.1078710
Fuentes A, Yoon S, Kim SC, Park DS (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors (Basel) 17(9). https://doi.org/10.3390/s17092022
Furbank RT, Jimenez-Berni JA, George-Jaeggli B, Potgieter AB, Deery DM (2019) Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops. New Phytologist, 223(4), 1714–1727.
Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, Charcosset A, Clarke JD, Graner E-M, Hansen M, Joets J, Le Paslier M-C, McMullen MD, Montalent P, Rose M, Schön C-C, Sun Q, Walter H, Martin OC, Falque M (2011) A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PLoS One 6(12):e28334–e28334. https://doi.org/10.1371/journal.pone.0028334
Ghosal S, Blystone D, Singh AK, Ganapathysubramanian B, Singh A, Sarkar S (2018) An explainable deep machine vision framework for plant stress phenotyping. Proc Natl Acad Sci U S A 115(18):4613–4618. https://doi.org/10.1073/pnas.1716999115
Godøy Ø, Saadatnejad B (2017) ACCESS climate data management. Ambio 46(suppl 3):464–474. https://doi.org/10.1007/s13280-017-0963-1
Gore MA, Chia J-M, Elshire RJ, Sun Q, Ersoz ES, Hurwitz BL, Peiffer JA, McMullen MD, Grills GS, Ross-Ibarra J (2009) A first-generation haplotype map of maize. Science 326(5956):1115–1117
Gratani L (2014) Plant phenotypic plasticity in response to environmental factors. Adv Bot 2014:17. https://doi.org/10.1155/2014/208747
Gray J, Szalay A (2007) eScience-a transformed scientific method. Presentation to the Computer Science and Technology Board of the National Research Council, Mountain View
Gulve PP, Tambe SS, Pandey MA, Kanse MS (2015) Leaf disease detection of cotton plant using image processing techniques. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), Special Issue on Innovation in engineering science and technology (NCIEST-2015), 2:50–54.
Hasan MM, Chopin JP, Laga H, Miklavcic SJ (2018) Detection and analysis of wheat spikes using convolutional neural networks. Plant Methods 14:100. https://doi.org/10.1186/s13007-018-0366-8
Hogers RC, de Ruiter M, Huvenaars KH, van der Poel H, Janssen A, van Eijk MJ, van Orsouw NJ (2018) SNPSelect: a scalable and flexible targeted sequence-based genotyping solution. PLoS One 13(10):e0205577
Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nat Rev Genet 11:855. https://doi.org/10.1038/nrg2897
Hu A, Noble WS, Wolf-Yadlin A (2016) Technical advances in proteomics: new developments in data-independent acquisition. F1000Res 5:F1000 Faculty Rev-1419. https://doi.org/10.12688/f1000research.7042.1
James C (2007) Global status of commercialized biotech/GM crops: 2007. ISAAA Briefs No 37 International Service for the Acquisition of Agri-Biotech Applications, Ithaca
Kakade NR, Ahire DD (2015) Real time grape leaf disease detection. Int J Adv Res Innov Ideas Educ (IJARIIE) 1(4):1
Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90
Köhl K, Gremmels J (2015) A software tool for the input and management of phenotypic data using personal digital assistants and other mobile devices. Plant Methods 11:25–25. https://doi.org/10.1186/s13007-015-0069-3
Kovalev MS, Igolkina AA, Samsonova MG, Nuzhdin SV (2018) A pipeline for classifying deleterious coding mutations in agricultural plants. Front Plant Sci 9:1734. https://doi.org/10.3389/fpls.2018.01734
Krajewski P, Chen D, Ćwiek H, van Dijk ADJ, Fiorani F, Kersey P, Klukas C, Lange M, Markiewicz A, Nap JP, van Oeveren J, Pommier C, Scholz U, van Schriek M, Usadel B, Weise S (2015) Towards recommendations for metadata and data handling in plant phenotyping. J Exp Bot 66(18):5417–5427. https://doi.org/10.1093/jxb/erv271
Li H, Hearne S, Bänziger M, Li Z, Wang J (2010) Statistical properties of QTL linkage mapping in biparental genetic populations. Heredity 105:257. https://doi.org/10.1038/hdy.2010.56
Li H, Rasheed A, Hickey LT, He Z (2018) Fast-forwarding genetic gain. Trends Plant Sci 23(3):184–186
Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: a review. Sensors (Basel) 18(8). https://doi.org/10.3390/s18082674
Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333(6042):616–620. https://doi.org/10.1126/science.1204531
Ma C, Zhang HH, Wang X (2014) Machine learning for Big Data analytics in plants. Trends Plant Sci 19(12):798–808. https://doi.org/10.1016/j.tplants.2014.08.004
Ma W, Qiu Z, Song J, Li J, Cheng Q, Zhai J, Ma C (2018) A deep convolutional neural network approach for predicting phenotypes from genotypes. Planta 248(5):1307–1318. https://doi.org/10.1007/s00425-018-2976-9
McGilvray D (2008) Executing data quality projects: Ten steps to quality data and trusted information (TM). Elsevier, Amsterdam
Mir RR, Zaman-Allah M, Sreenivasulu N, Trethowan R, Varshney RK (2012) Integrated genomics, physiology and breeding approaches for improving drought tolerance in crops. Theor Appl Genet 125(4):625–645. https://doi.org/10.1007/s00122-012-1904-9
Mochida K, Koda S, Inoue K, Hirayama T, Tanaka S, Nishii R, Melgani F (2019) Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective. Gigascience 8(1). https://doi.org/10.1093/gigascience/giy153
Mohanty SP, Hughes DP, Salathe M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419. https://doi.org/10.3389/fpls.2016.01419
Montesinos-López OA, Montesinos-López A, Crossa J, Gianola D, Hernández-Suárez CM, Martín-Vallejo J (2018) Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits. G3 (Betheseda) 8(12):3829–3840
Montesinos-Lopez OA, Martin-Vallejo J, Crossa J, Gianola D, Hernandez-Suarez CM, Montesinos-Lopez A, Juliana P, Singh R (2019) A benchmarking between deep learning, support vector machine and Bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding. G3 (Bethesda) 9(2):601–618. https://doi.org/10.1534/g3.118.200998
Moose SP, Mumm RH (2008) Molecular plant breeding as the foundation for 21st century crop improvement. Plant Physiol 147(3):969–977. https://doi.org/10.1104/pp.108.118232
Nagasubramanian K, Jones S, Singh AK, Singh A, Ganapathysubramanian B, Sarkar S (2018) Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps. arXiv preprint arXiv:180408831
Naik SI, Kanandreddy V, Sannakki S (2014) Plant disease diagnosis system for improved crop yield. Int J Innov Eng Technol 4:198–204
Naik HS, Zhang J, Lofquist A, Assefa T, Sarkar S, Ackerman D, Singh A, Singh AK, Ganapathysubramanian B (2017) A real-time phenotyping framework using machine learning for plant stress severity rating in soybean. Plant Methods 13:23. https://doi.org/10.1186/s13007-017-0173-7
Nicotra AB, Atkin OK, Bonser SP, Davidson AM, Finnegan E, Mathesius U, Poot P, Purugganan MD, Richards CL, Valladares F (2010) Plant phenotypic plasticity in a changing climate. Trends Plant Sci 15(12):684–692
Odilbekov F, Armoniene R, Henriksson T, Chawade A (2018) Proximal phenotyping and machine learning methods to identify Septoria Tritici Blotch disease symptoms in wheat. Front Plant Sci 9:685. https://doi.org/10.3389/fpls.2018.00685
Pachore KA, Kishore R, Bhawar S (2016) Leaf disease recognition system. Int. J. Comput. Appl, Proceedings on national conference on digital image and signal processing NCDISP 2016(2):31–34
Parent B, Tardieu F (2012) Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species. New Phytol 194(3):760–774. https://doi.org/10.1111/j.1469-8137.2012.04086.x
Patel DA, Zander M, Dalton-Morgan J, Batley J (2015) Advances in plant genotyping: where the future will take us. Methods Mol Biol 1245:1–11. https://doi.org/10.1007/978-1-4939-1966-6_1
Pieruschka R, Schurr U (2019) Plant phenotyping: past, present, and future. Plant Phenomics 2019:6. https://doi.org/10.1155/2019/7507131
Pineda M, Perez-Bueno ML, Baron M (2018) Detection of bacterial infection in melon plants by classification methods based on imaging data. Front Plant Sci 9:164. https://doi.org/10.3389/fpls.2018.00164
Poorter H, Niinemets Ü, Walter A, Fiorani F, Schurr U (2010) A method to construct dose–response curves for a wide range of environmental factors and plant traits by means of a meta-analysis of phenotypic data. J Exp Bot 61(8):2043–2055. https://doi.org/10.1093/jxb/erp358
Poorter H, Niklas KJ, Reich PB, Oleksyn J, Poot P, Mommer L (2012) Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol 193(1):30–50. https://doi.org/10.1111/j.1469-8137.2011.03952.x
Potgieter AB, Watson J, Eldridge M, Laws K, George-Jaeggli B, Hunt C, Borrell A, Mace E, Chapman SC, Jordan DR (2018) Determining crop growth dynamics in sorghum breeding trials through remote and proximal sensing technologies. In: IGARSS 2018-2018 IEEE international geoscience and remote sensing symposium, 2018. IEEE, pp 8244–8247
Ramcharan A, Baranowski K, McCloskey P, Ahmed B, Legg J, Hughes DP (2017) Deep learning for image-based cassava disease detection. Front Plant Sci 8:1852. https://doi.org/10.3389/fpls.2017.01852
Ramstein GP, Jensen SE, Buckler ES (2019) Breaking the curse of dimensionality to identify causal variants in breeding 4. Theor Appl Genet 132(3):559–567. https://doi.org/10.1007/s00122-018-3267-3
Rathore A, Singh VK, Pandey SK, Rao CS, Thakur V, Pandey MK, Anil Kumar V, Das RR (2018) Current status and future prospects of next-generation data management and analytical decision support tools for enhancing genetic gains in crops. In: Varshney RK, Pandey MK, Chitikineni A (eds) Plant genetics and molecular biology. Springer, Cham, pp 277–292. https://doi.org/10.1007/10_2017_56
Report (2019) Global Industry Perspective, Comprehensive Analysis, and Forecast, 2018–2025. Genotyping Assay Market by Products & Services (Reagents & Kits, Genotyping Services, Instruments, and Bioinformatics), by Technology (PCR, Microarrays, Sequencing, Capillary Electrophoresis, MALDI-Tof MS, and Others), by Application (Pharmacogenomics, Diagnostics & Personalized Medicine, Animal Genetics, Agricultural Biotechnology, and Others), and by End-User (Pharmaceutical & Biopharmaceutical Companies, Diagnostic & Research Laboratories, Academic Institutes, and Others): Global Industry Perspective, Comprehensive Analysis, and Forecast, 2018–2025
Revathi P, Hemalatha M (2014) Identification of cotton diseases based on cross information gain deep forward neural network classifier with PSO feature selection. Int J Eng Technol 5(6):4637–4642
Ribaut J-M, Ragot M (2019) Modernising breeding for orphan crops: tools, methodologies, and beyond. Planta. https://doi.org/10.1007/s00425-019-03200-8
Sansone S-A, McQuilton P, Rocca-Serra P, Gonzalez-Beltran A, Izzo M, Lister AL, Thurston M, the FC (2019) FAIRsharing as a community approach to standards, repositories and policies. Nat Biotechnol 37(4):358–367. https://doi.org/10.1038/s41587-019-0080-8
Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA, Minx P, Reily AD, Courtney L, Kruchowski SS, Tomlinson C, Strong C, Delehaunty K, Fronick C, Courtney B, Rock SM, Belter E, Du F, Kim K, Abbott RM, Cotton M, Levy A, Marchetto P, Ochoa K, Jackson SM, Gillam B, Chen W, Yan L, Higginbotham J, Cardenas M, Waligorski J, Applebaum E, Phelps L, Falcone J, Kanchi K, Thane T, Scimone A, Thane N, Henke J, Wang T, Ruppert J, Shah N, Rotter K, Hodges J, Ingenthron E, Cordes M, Kohlberg S, Sgro J, Delgado B, Mead K, Chinwalla A, Leonard S, Crouse K, Collura K, Kudrna D, Currie J, He R, Angelova A, Rajasekar S, Mueller T, Lomeli R, Scara G, Ko A, Delaney K, Wissotski M, Lopez G, Campos D, Braidotti M, Ashley E, Golser W, Kim H, Lee S, Lin J, Dujmic Z, Kim W, Talag J, Zuccolo A, Fan C, Sebastian A, Kramer M, Spiegel L, Nascimento L, Zutavern T, Miller B, Ambroise C, Muller S, Spooner W, Narechania A, Ren L, Wei S, Kumari S, Faga B, Levy MJ, McMahan L, Van Buren P, Vaughn MW, Ying K, Yeh C-T, Emrich SJ, Jia Y, Kalyanaraman A, Hsia A-P, Barbazuk WB, Baucom RS, Brutnell TP, Carpita NC, Chaparro C, Chia J-M, Deragon J-M, Estill JC, Fu Y, Jeddeloh JA, Han Y, Lee H, Li P, Lisch DR, Liu S, Liu Z, Nagel DH, McCann MC, SanMiguel P, Myers AM, Nettleton D, Nguyen J, Penning BW, Ponnala L, Schneider KL, Schwartz DC, Sharma A, Soderlund C, Springer NM, Sun Q, Wang H, Waterman M, Westerman R, Wolfgruber TK, Yang L, Yu Y, Zhang L, Zhou S, Zhu Q, Bennetzen JL, Dawe RK, Jiang J, Jiang N, Presting GG, Wessler SR, Aluru S, Martienssen RA, Clifton SW, McCombie WR, Wing RA, Wilson RK (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326(5956):1112–1115. https://doi.org/10.1126/science.1178534
Shakoor N, Northrup D, Murray S, Mockler TC (2019) Big Data driven agriculture: big data analytics in plant breeding, genomics, and the use of remote sensing technologies to advance crop productivity. Plant Phenome J 2(1). https://doi.org/10.2135/tppj2018.12.0009
Shekoofa A, Emam Y, Shekoufa N, Ebrahimi M, Ebrahimie E (2014) Determining the most important physiological and agronomic traits contributing to maize grain yield through machine learning algorithms: a new avenue in intelligent agriculture. PLoS One 9(5):e97288. https://doi.org/10.1371/journal.pone.0097288
Shi S, Yuan N, Yang M, Du Z, Wang J, Sheng X, Wu J, Xiao J (2018) Comprehensive assessment of genotype imputation performance. Hum Hered 83(3):107–116. https://doi.org/10.1159/000489758
Shrestha R, Matteis L, Skofic M, Portugal A, McLaren G, Hyman G, Arnaud E (2012) Bridging the phenotypic and genetic data useful for integrated breeding through a data annotation using the Crop Ontology developed by the crop communities of practice. Front Physiol 3(326). https://doi.org/10.3389/fphys.2012.00326
Singh A, Ganapathysubramanian B, Singh AK, Sarkar S (2016) Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 21(2):110–124. https://doi.org/10.1016/j.tplants.2015.10.015
Singh AK, Ganapathysubramanian B, Sarkar S, Singh A (2018) Deep learning for plant stress phenotyping: trends and future perspectives. Trends Plant Sci 23(10):883–898
Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci 2016:3289801. https://doi.org/10.1155/2016/3289801
Torres LG, Rodrigues MC, Lima NL, Trindade TFH, Silva FF, Azevedo CF, RO DL (2018) Multi-trait multi-environment Bayesian model reveals G x E interaction for nitrogen use efficiency components in tropical maize. PLoS One 13(6):e0199492. https://doi.org/10.1371/journal.pone.0199492
Varshney RK, Singh VK, Hickey JM, Xun X, Marshall DF, Wang J, Edwards D, Ribaut J-M (2016) Analytical and decision support tools for genomics-assisted breeding. Trends Plant Sci 21(4):354–363. https://doi.org/10.1016/j.tplants.2015.10.018
Veeramani B, Raymond JW, Chanda P (2018) DeepSort: deep convolutional networks for sorting haploid maize seeds. BMC Bioinformatics 19(suppl 9):289. https://doi.org/10.1186/s12859-018-2267-2
Wallace JG, Rodgers-Melnick E, Buckler ES (2018) On the road to Breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics. Annu Rev Genet 52(1):421–444. https://doi.org/10.1146/annurev-genet-120116-024846
Wang G, Sun Y, Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci 2017:2917536
Wang X, Xu Y, Hu Z, Xu C (2018) Genomic selection methods for crop improvement: current status and prospects. Crop J 6(4):330–340. https://doi.org/10.1016/j.cj.2018.03.001
Wiesner-Hanks T, Stewart EL, Kaczmar N, DeChant C, Wu H, Nelson RJ, Lipson H, Gore MA (2018) Image set for deep learning: field images of maize annotated with disease symptoms. BMC Res Notes 11(1):440. https://doi.org/10.1186/s13104-018-3548-6
Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, ‘t Hoen PAC, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone S-A, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3:160018. https://doi.org/10.1038/sdata.2016.18
Xu Y (2016) Envirotyping for deciphering environmental impacts on crop plants. Theor Appl Genet 129(4):653–673. https://doi.org/10.1007/s00122-016-2691-5
Yang X, Guo T (2017) Machine learning in plant disease research. Eur J Biomed Res 3(1):6–9
Yu X, Li X, Guo T, Zhu C, Wu Y, Mitchell SE, Roozeboom KL, Wang D, Wang ML, Pederson GA, Tesso TT, Schnable PS, Bernardo R, Yu J (2016) Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nat Plants 2:16150. https://doi.org/10.1038/nplants.2016.150
Zadokar AR, Bhagat DP, Nayase AA, Mhaske SS (2017) Leaf disease detection of cotton plant using image processing techniques: a review. International journal of electronics, communication and soft computing science & engineering (IJECSCSE) special issue-IETE zonal seminar “Recent Trends in Engineering &Technology”, 53–55.
Zhang J, Song Q, Cregan PB, Jiang G-L (2016) Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max). Theor Appl Genet 129(1):117–130. https://doi.org/10.1007/s00122-015-2614-x
Zhao J, Bodner G, Rewald B (2016) Phenotyping: using machine learning for improved pairwise genotype classification based on root traits. Front Plant Sci 7:1864. https://doi.org/10.3389/fpls.2016.01864
Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J (2019) Crop phenomics: current status and perspectives. Front Plant Sci 10(714). https://doi.org/10.3389/fpls.2019.00714
Acknowledgments
The authors thank John Vicini (Bayer) for his critical review of the document and Chaitra N (ex-intern at Monsanto) for her help in compiling Table 4.7.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kuriakose, S.V., Pushker, R., Hyde, E.M. (2020). Data-Driven Decisions for Accelerated Plant Breeding. In: Gosal, S., Wani, S. (eds) Accelerated Plant Breeding, Volume 1. Springer, Cham. https://doi.org/10.1007/978-3-030-41866-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-41866-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41865-6
Online ISBN: 978-3-030-41866-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)