Acta Physiologiae Plantarum

, 41:156 | Cite as

Applying an artificial neural network approach for drought tolerance screening among Iranian wheat landraces and cultivars grown under well-watered and rain-fed conditions

  • Yousef Rahimi
  • Mohammad Reza BihamtaEmail author
  • Alireza Taleei
  • Hadi Alipour
  • Pär K. Ingvarsson
Original Article


In the current study, an α-lattice design was used to investigate 320 Iranian bread wheat cultivars and landraces under non-stressed and rain-fed conditions, according to phenological, morphological and physiological parameters. An artificial neural network (ANN) was trained to evaluate the relative importance of different drought tolerance indices (DTIs) using a multilayer perceptron model. Our findings suggest that the Iranian wheat germplasm harbors large genetic diversity for all the studied traits. Correlation analyses highlighted the important role of seed number per spike, thousand kernel weight, leaf greenness and canopy temperature in predicting grain yield under both non-stressed and rain-fed conditions. Moreover, correlations between stressed-yield (Ys) and yield index (YI, r = 1**), harmonic mean (HM, r = 0.94**), geometric mean productivity (GMP, r = 0.86**), and stress tolerance index (STI, r = 0.86**) were all large, which was further confirmed by the results of ANN and a principal component analysis. A hierarchical clustering, visualized using a heatmap plot, classified cultivars and landraces into four separate groups, where high-yielding and drought-tolerant genotypes clustered in the same group. The result of ANN indicated that MP and YI had the highest relative importance for screening compatible genotypes for well-watered and rain-fed conditions, respectively. Overall, the selection of genotypes according to agronomic and physiological traits in association with an appropriate DTI can identify favorable wheat genotypes in a field trial to breed for well-watered and water-limited environments. Furthermore, the ANN successfully evaluated the relative importance of different DTIs in wheat.


Artificial neural network Drought tolerance indices Multilayer perceptron Principal component analysis Triticum aestivum 



We kindly acknowledge the University of Tehran and Iran National Science Foundation for their support of this research.

Compliance with ethical standards

Conflict of interest

This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. All the authors have approved the manuscript and agree with the submission. The authors declare no conflicts of interest.

Supplementary material

11738_2019_2946_MOESM1_ESM.docx (24 kb)
Supplementary material 1 (DOCX 23 kb)
11738_2019_2946_MOESM2_ESM.xlsx (47 kb)
Supplementary material 2 (XLSX 47 kb)
11738_2019_2946_MOESM3_ESM.xlsx (21 kb)
Supplementary material 3 (XLSX 21 kb)


  1. Abdolshahi R, Nazari M, Safarian A, Sadathossini T, Salarpour M, Amiri H (2015) Integrated selection criteria for drought tolerance in wheat (Triticum aestivum L.) breeding programs using discriminant analysis. Field Crops Res 174:20–29CrossRefGoogle Scholar
  2. Aghaie P, Tafreshi SAH, Ebrahimi MA, Haerinasab M (2018) Tolerance evaluation and clustering of fourteen tomato cultivars grown under mild and severe drought conditions. Sci Hortic 232:1–12CrossRefGoogle Scholar
  3. Ajith A (2005) Artificial neural networks. In: Peter H (ed) Handbook of measuring system design. Sydenham and Richard Thorn. Wiley, Hoboken. ISBN: 0-470-02143-8Google Scholar
  4. Ali MB, El-Sadek AN (2016) Evaluation of drought tolerance indices for wheat (Triticum aestivum L.) under irrigated and rainfed conditions. Commun Biometry Crop Sci 11:77–89Google Scholar
  5. Alvarez R (2009) Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach. Eur J Agron 30:70–77CrossRefGoogle Scholar
  6. Álvaro F, Isidro J, Villegas D, García del Moral LF, Royo C (2008) Breeding effects on grain filling, biomass partitioning, and remobilization in Mediterranean durum wheat. Agron J 100:361–370CrossRefGoogle Scholar
  7. Avila GA, Davidson M, Van Helden M, Fagan L (2019) The potential distribution of the Russian wheat aphid (Diuraphis noxia): an updated distribution model including irrigation improves model fit for predicting potential spread. Bull Entomol Res 109:90–101CrossRefGoogle Scholar
  8. Barakat M, El-Hendawy S, Al-Suhaibani N, Elshafei A, Al-Doss A, Al-Ashkar I, Ahmed E, Al-Gaadi K (2016) The genetic basis of spectral reflectance indices in drought-stressed wheat. Acta Physiol Plant 38:227CrossRefGoogle Scholar
  9. Battenfield SD, Guzmán C, Gaynor RC, Singh RP, Peña RJ, Dreisigacker S, Fritz AK, Poland JA (2016) Genomic selection for processing and end-use quality traits in the CIMMYT spring bread wheat breeding program. Plant Genome. CrossRefPubMedGoogle Scholar
  10. Bouslama M, Schapaugh W (1984) Stress tolerance in soybeans. I. Evaluation of three screening techniques for heat and drought tolerance 1. Crop Sci 24:933–937CrossRefGoogle Scholar
  11. Budak H, Hussain B, Khan Z, Ozturk NZ, Ullah N (2015) From genetics to functional genomics: improvement in drought signaling and tolerance in wheat. Front Plant Sci 6:1012CrossRefGoogle Scholar
  12. Byerlee D, de Polanco EH (1983) Wheat in the world food economy: increasing role in developing countries. Food Policy 8:67–75CrossRefGoogle Scholar
  13. Çelik Ö, Teke A, Yıldırım HB (2016) The optimized artificial neural network model with Levenberg–Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey. J Clean Prod 116:1–12CrossRefGoogle Scholar
  14. Chakraborty D, Nagarajan S, Aggarwal P, Gupta VK, Tomar RK, Garg RN, Sahoo RN, Sarkar A, Chopra UK, Sarma KS, Kalra N (2008) Effect of mulching on soil and plant water status, and the growth and yield of wheat (Triticum aestivum L.) in a semi-arid environment. Agric Water Manag 95:1323–1334CrossRefGoogle Scholar
  15. Chatrath R, Mishra B, Ferrara GO, Singh S, Joshi A (2007) Challenges to wheat production in South Asia. Euphytica 157:447–456CrossRefGoogle Scholar
  16. Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3–4):244–257CrossRefGoogle Scholar
  17. del Pozo A, Yáñez A, Matus IA, Tapia G, Castillo D, Sanchez-Jardón L, Araus JL (2016) Physiological traits associated with wheat yield potential and performance under water–stress in a Mediterranean environment. Front Plant Sci 7:987PubMedPubMedCentralGoogle Scholar
  18. Devos KM, Doležel J, Feuillet C (2009) Genome organization and comparative genomics. In: Carver BF (ed) Wheat science and trade. Wiley-Blackwell, pp 327–367Google Scholar
  19. Drikvand R, Doosty B, Hosseinpour T (2012) Response of rainfed wheat genotypes to drought stress using drought tolerance indices. J Agric Sci 4:126Google Scholar
  20. Eivazi A, Mohammadi S, Rezaei M, Ashori S, Pour F (2013) Effective selection criteria for assessing drought tolerance indices in barley (Hordeum vulgare L.) accessions. Int J Agron Plant Prod 4:813–821Google Scholar
  21. El-Hendawy SE, Hassan WM, Al-Suhaibani NA, Schmidhalter U (2017) Spectral assessment of drought tolerance indices and grain yield in advanced spring wheat lines grown under full and limited water irrigation. Agric Water Manag 182:1–12CrossRefGoogle Scholar
  22. FAO (2018) Food and Agriculture Organization of the United Nations: FAOSTAT.
  23. Farshadfar E, Mohammadi R, Farshadfar M, Dabiri S (2013) Relationships and repeatability of drought tolerance indices in wheat-rye disomic addition lines. Aust J Crop Sci 7:130Google Scholar
  24. Fehér I, Lehota J, Lakner Z, Kende Z, Bálint C, Vinogradov S, Fieldsend A (2017) Kazakhstan’s wheat production potential. In: The eurasian wheat belt and food security. Springer, New York, pp 177–194Google Scholar
  25. Fernandez GC (1993) Effective selection criteria for assessing plant stress tolerance. Adaptation of food crops to temperature and water stress 13-181992257270Google Scholar
  26. Fischer R, Maurer R (1978) Drought resistance in spring wheat cultivars. I. Grain yield responses. Aust J Agric Res 29:897–912CrossRefGoogle Scholar
  27. Fleury D, Jefferies S, Kuchel H, Langridge P (2010) Genetic and genomic tools to improve drought tolerance in wheat. J Exp Bot 61:3211–3222CrossRefGoogle Scholar
  28. Foulkes M, Sylvester-Bradley R, Weightman R, Snape J (2007) Identifying physiological traits associated with improved drought resistance in winter wheat. Field crops Res 103:11–24CrossRefGoogle Scholar
  29. Fu D, Uauy C, Distelfeld A, Blechl A, Epstein L, Chen X, Sela H, Fahima T, Dubcovsky J (2009) A kinase-START gene confers temperature-dependent resistance to wheat stripe rust. Science 323:1357–1360CrossRefGoogle Scholar
  30. Gardner MW, Dorling S (1998) Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 32:2627–2636CrossRefGoogle Scholar
  31. Gavuzzi P, Rizza F, Palumbo M, Campanile R, Ricciardi G, Borghi B (1997) Evaluation of field and laboratory predictors of drought and heat tolerance in winter cereals. Can J Plant Sci 77:523–531CrossRefGoogle Scholar
  32. Hammer O, Harper DA, Ryan PD (2001) Palaeontological statistics software package for education and data analysis. Palaeontol Electron 4:1Google Scholar
  33. Hefny MM, Metwali EMR, Mohamed AI (2013) Assessment of genetic diversity of sorghum (‘Sorghum bicolor’ L. Moench) genotypes under saline irrigation water based on some selection indices. Aust J Crop Sci 7:1935Google Scholar
  34. Hill CB, Li C (2016) Genetic architecture of flowering phenology in cereals and opportunities for crop improvement. Front Plant Sci 7:1906CrossRefGoogle Scholar
  35. Hussain B, Khan AS, Ali Z (2015) Genetic variation in wheat germplasm for salinity tolerance at seedling stage: improved statistical inference. Turk J Agric For 39:182–192CrossRefGoogle Scholar
  36. Isidro J, Álvaro F, Royo C, Villegas D, Miralles DJ, García del Moral LF (2011) Changes in duration of developmental phases of durum wheat caused by breeding in Spain and Italy during the 20th century and its impact on yield. Ann Bot 107:1355–1366CrossRefGoogle Scholar
  37. Jensen ME (1974) Consumptive use of water and irrigation water requirements. ASCEGoogle Scholar
  38. Jha UC, Basu P, Shil S, Singh NP (2016) Evaluation of drought tolerance selection indices in chickpea genotypes. Int J Bio-Resour Stress Manag 7:1244–1248CrossRefGoogle Scholar
  39. Kalaji HM, Jajoo A, Oukarroum A, Brestic M, Zivcak M, Samborska IA, Cetner MD, Łukasik I, Goltsev V, Ladle RJ (2016) Chlorophyll a fluorescence as a tool to monitor physiological status of plants under abiotic stress conditions. Acta Physiol Plant 38:102CrossRefGoogle Scholar
  40. Kang S, Gu B, Du T, Zhang J (2003) Crop coefficient and ratio of transpiration to evapotranspiration of winter wheat and maize in a semi-humid region. Agric Water Manag 59:239–254CrossRefGoogle Scholar
  41. Kaul M, Hill RL, Walthall C (2005) Artificial neural networks for corn and soybean yield prediction. Agric Syst 85:1–18CrossRefGoogle Scholar
  42. Kirigwi F, Van Ginkel M, Brown-Guedira G, Gill B, Paulsen GM, Fritz A (2007) Markers associated with a QTL for grain yield in wheat under drought. Mol Breed 20:401–413CrossRefGoogle Scholar
  43. Kişi Ö (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539CrossRefGoogle Scholar
  44. Kumar S, Tiwari MK, Chatterjee C, Mishra A (2015) Reservoir inflow forecasting using ensemble models based on neural networks, wavelet analysis and bootstrap method. Water Resour Manag 29(13):4863–4883CrossRefGoogle Scholar
  45. Liu Y, Zhang X, Tran H, Shan L, Kim J, Childs K, Ervin EH, Frazier T, Zhao B (2015) Assessment of drought tolerance of 49 switchgrass (Panicum virgatum) genotypes using physiological and morphological parameters. Biotechnol Biofuels 8:152CrossRefGoogle Scholar
  46. Lopes MS, Reynolds MP (2012) Stay-green in spring wheat can be determined by spectral reflectance measurements (normalized difference vegetation index) independently from phenology. J Exp Bot 63:3789–3798CrossRefGoogle Scholar
  47. Lopes MS, Royo C, Alvaro F, Sanchez-Garcia M, Ozer E, Ozdemir F, Karaman M, Roustaii M, Jalal-Kamali MR, Pequeno D (2018) Optimizing winter wheat resilience to climate change in rain fed crop systems of Turkey and Iran. Front Plant Sci 9:563CrossRefGoogle Scholar
  48. Mardeh AS-S, Ahmadi A, Poustini K, Mohammadi V (2006) Evaluation of drought resistance indices under various environmental conditions. Field Crops Res 98:222–229CrossRefGoogle Scholar
  49. Mason NM, Jayne T, Shiferaw B (2015) Africa’s rising demand for wheat: trends, drivers, and policy implications. Dev Policy Rev 33:581–613CrossRefGoogle Scholar
  50. Matsumura K, Gaitan CF, Sugimoto K, Cannon AJ, Hsieh WW (2015) Maize yield forecasting by linear regression and artificial neural networks in Jilin, China. J Agric Sci 153:399–410CrossRefGoogle Scholar
  51. Mickelbart MV, Hasegawa PM, Bailey-Serres J (2015) Genetic mechanisms of abiotic stress tolerance that translate to crop yield stability. Nat Rev Genet 16:237CrossRefGoogle Scholar
  52. 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 125:625–645CrossRefGoogle Scholar
  53. Mohammadi R (2016) Efficiency of yield-based drought tolerance indices to identify tolerant genotypes in durum wheat. Euphytica 211:71–89CrossRefGoogle Scholar
  54. Mondal S, Singh RP, Crossa J, Huerta-Espino J, Sharma I, Chatrath R, Singh GP, Sohu VS, Mavi GS, Sukuru VSP, Kalappanavar IK (2013) Earliness in wheat: a key to adaptation under terminal and continual high temperature stress in South Asia. Field Crops Res 151:19–26CrossRefGoogle Scholar
  55. Moré JJ (1978) The Levenberg–Marquardt algorithm: implementation and theory. In: Numerical analysis. Springer, New York, pp 105–116Google Scholar
  56. Mursalova J, Akparov Z, Ojaghi J, Eldarov M, Belen S, Gummadov N, Morgounov A (2015) Evaluation of drought tolerance of winter bread wheat genotypes underdrip irrigation and rain-fed conditions. Turk J Agric For 39:817–824CrossRefGoogle Scholar
  57. Mwadzingeni L, Shimelis H, Tesfay S, Tsilo TJ (2016) Screening of bread wheat genotypes for drought tolerance using phenotypic and proline analyses. Front Plant Sci 7:1276CrossRefGoogle Scholar
  58. Pantazi XE, Moshou D, Alexandridis T, Whetton R, Mouazen AM (2016) Wheat yield prediction using machine learning and advanced sensing techniques. Comput Electron Agric 121:57–65CrossRefGoogle Scholar
  59. Parry ML, Rosenzweig C, Iglesias A, Livermore M, Fischer G (2004) Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob Environ Change 14:53–67CrossRefGoogle Scholar
  60. Rahimikhoob A (2010) Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renew Energy 35(9):2131–2135CrossRefGoogle Scholar
  61. Ramya P, Singh GP, Jain N, Singh PK, Pandey MK, Sharma K, Kumar A, Prabhu KV (2016) Effect of recurrent selection on drought tolerance and related morpho-physiological traits in bread wheat. PLoS One 11:e0156869CrossRefGoogle Scholar
  62. Ravari S, Dehghani H, Naghavi H (2016) Assessment of salinity indices to identify Iranian wheat varieties using an artificial neural network. Ann Appl Biol 168:185–194CrossRefGoogle Scholar
  63. Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop production by 2050. PLoS One 8:e66428CrossRefGoogle Scholar
  64. Rehman SU, Bilal M, Rana RM, Tahir MN, Shah MKN, Ayalew H, Yan G (2016) Cell membrane stability and chlorophyll content variation in wheat (Triticum aestivum) genotypes under conditions of heat and drought. Crop Pasture Sci 67:712–718CrossRefGoogle Scholar
  65. Reynolds M, Tattaris M, Cossani CM, Ellis M, Yamaguchi-Shinozaki K, Saint Pierre C (2015) Exploring genetic resources to increase adaptation of wheat to climate change. In: Advances in wheat genetics: from genome to field. Springer, New York, pp 355–368CrossRefGoogle Scholar
  66. Rivero RM, Kojima M, Gepstein A, Sakakibara H, Mittler R, Gepstein S, Blumwald E (2007) Delayed leaf senescence induces extreme drought tolerance in a flowering plant. Proc Natl Acad Sci 104:19631–19636CrossRefGoogle Scholar
  67. Rizza F, Badeck F, Cattivelli L, Lidestri O, Di Fonzo N, Stanca A (2004) Use of a water stress index to identify barley genotypes adapted to rainfed and irrigated conditions. Crop Sci 44:2127–2137CrossRefGoogle Scholar
  68. Rosielle A, Hamblin J (1981) Theoretical aspects of selection for yield in stress and non-stress environment 1. Crop Sci 21:943–946CrossRefGoogle Scholar
  69. Saad ASI, Li X, Li HP, Huang T, Gao CS, Guo MW, Cheng W, Zhao GY, Liao YC (2013) A rice stress-responsive NAC gene enhances tolerance of transgenic wheat to drought and salt stresses. Plant Sci 203:33–40CrossRefGoogle Scholar
  70. Safa M, Samarasinghe S, Nejat M (2015) Prediction of wheat production using artiicial neural networks and investigating indirect factors afecting it: case study in Canterbury province, New Zealand. J Agr Sci Tech 17:791–803Google Scholar
  71. Sahar B, Ahmed B, Naserelhaq N, Mohammed J, Hassan O (2016) Efficiency of selection indices in screening bread wheat lines combining drought tolerance and high yield potential. J Plant Breed Crop Sci 8:72–86Google Scholar
  72. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117CrossRefGoogle Scholar
  73. Shan Q, Wang Y, Li J, Zhang Y, Chen K, Liang Z, Zhang K, Liu J, Xi JJ, Qi JL, Gao C (2013) Targeted genome modification of crop plants using a CRISPR-Cas system. Nat Biotechnol 31:686CrossRefGoogle Scholar
  74. Shavrukov Y, Kurishbayev A, Jatayev S, Shvidchenko V, Zotova L, Koekemoer F, de Groot S, Soole K, Langridge P (2017) Early flowering as a drought escape mechanism in plants: how can it aid wheat production? Front Plant Sci 8:1950CrossRefGoogle Scholar
  75. Singh RK, Chaudhary BD (1979) Biometrical methods in quantitative genetic analysis. Kalyani, LudhianaGoogle Scholar
  76. Thapa S, Jessup KE, Pradhan GP, Rudd JC, Liu S, Mahan JR, Devkota RN, Baker JA, Xue Q (2018) Canopy temperature depression at grain filling correlates to winter wheat yield in the US Southern High Plains. Field Crops Res 217:11–19CrossRefGoogle Scholar
  77. Van Ginkel M, Calhoun DS, Gebeyehu G, Miranda A, Tian-You C, Lara RP, Trethowan RM, Sayre K, Crossa J, Rajaram S (1998) Plant traits related to yield of wheat in early, late, or continuous drought conditions. Euphytica 100:109–121CrossRefGoogle Scholar
  78. Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244CrossRefGoogle Scholar
  79. Yegnanarayana B (2009) Artificial neural networks. PHI Learning Pvt. Ltd, New DelhiGoogle Scholar
  80. Zhang Z, Zhang Khelifi (2018) Multivariate time series analysis in climate and environmental research. Springer International Publishing, ChamCrossRefGoogle Scholar
  81. Zhang B, Li W, Chang X, Li R, Jing R (2014) Effects of favorable alleles for water-soluble carbohydrates at grain filling on grain weight under drought and heat stresses in wheat. PLoS One 9:e102917CrossRefGoogle Scholar

Copyright information

© Franciszek Górski Institute of Plant Physiology, Polish Academy of Sciences, Kraków 2019

Authors and Affiliations

  • Yousef Rahimi
    • 1
  • Mohammad Reza Bihamta
    • 1
    Email author
  • Alireza Taleei
    • 1
  • Hadi Alipour
    • 2
  • Pär K. Ingvarsson
    • 3
  1. 1.Department of Agronomy and Plant Breeding, Faculty of AgricultureUniversity of TehranKarajIran
  2. 2.Department of Plant Breeding and Biotechnology, Faculty of AgricultureUrmia UniversityUrmiaIran
  3. 3.Linnean Centre for Plant Biology, Department of Plant BiologySwedish University of Agricultural SciencesUppsalaSweden

Personalised recommendations