Document Type: Research paper


1 Department of Agronomy, Faculty of Agriculture, Tarbiat Modares University, Tehran, 14115-336, Iran

2 Department of Agronomy, Genetics and Agricultural Biotechnology Institute of Tabarestan, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.

3 Department of Agronomy, Faculty of Agriculture, Azarbaijan Shahid Madani University, 53714-161, Iran

4 Department of Agricultural, Forest and Food Sciences, VEGMAP, University of Turin, Grugliasco (TO) 10095, Italy


Despite recent development in producing chemical medicines, associated side effects have led to increased use of medicinal plants and natural compounds. Soil salinity, especially in arid and semi-arid regions, is a serious threat to global agriculture. Nowadays, efforts have been made to find benchmarks that can effectively select salt-tolerant or salt-resistant genotypes. In this regard, the use of computer software to predict the indices can help us for screening the most tolerant ecotypes. The objectives of the present study were to determine the best indicators of salinity tolerance using intelligent and regression models for eighteen commercial ecotypes of mint. The seedlings were planted in plastic pots and arranged in a split factorial experiment in a randomized complete block design with four replicates. The treatments consisted of four levels of salinity (0, 2.5, 5 and 7.5 dS m-1), two levels of harvesting time, and 18 ecotypes. The plants were grown until the flowering stage and then harvested. There was a significant difference between ecotypes in terms of calculated indices at all three levels of salinity. Indicators such as TOL, MP, GMP, YSI, STI and HM showed a significant positive correlation with YS and YP at all three levels of salinity. The cluster analysis divided the ecotypes into three distinct groups based on the calculated indices at all levels of salinity. The principal component analysis revealed that the YP, YS, TOL, MP, GMP, YSI, STI and HM were more suitable among others salt stress indices. The sensitivity analysis at 2.5 dS m-1 salinity level showed that the HM, STI, YSI, YI, SSI and MP indices were of higher importance than the others. At 5 dS m-1 salinity level, the HM, STI, YSI, YI, GMP and MP indices showed the highest importance whereas at 7.5 dS m-1 salinity level, the STI, YSI, YI, GMP and YP indices indicated the highest importance. In general, the results suggest that ANN(MLP) model (R2 = 0.999) is the best model to predict at all salinity levels. E13, E14, E15, E16 and E18 ecotypes are the most salt tolerant ecotypes which can be used for the future breeding program.


Abraha M, Shimelis H, Laing M, Assefa K. 2017. Selection of drought-tolerant tef (Eragrostis tef) genotypes using drought tolerance indices. South African Journal of Plant and Soil 34(4), 291-300. 2. Ashraf M, Wu L. 1994. Breeding for salinity tolerance in plants. Critical Reviews in Plant Sciences 13(1), 17-42. 3. Bi W, Dandy G, Maier. 2015. Improved genetic algorithm optimization of water distribution system design by incorporating domain knowledge. Environmental Modelling and Software 69, 370-381. 4. Blum A. 2005. Drought resistance, water-use efficiency, and yield potential are they compatible, dissonant, or mutually exclusive? Australian Journal of Agricultural Research 56(11), 1159-1168. 5. Bouslama M, Schapaugh W. 1984. Stress tolerance in soybeans. I. Evaluation of three screening techniques for heat and drought tolerance. Crop Science 24(5), 933-937. 6. Bridges N. 2016. Shaping Strong People: Napo una Therapeutic Narratives of Medicinal Plant Use. In Plants and Health 93-116.
7. Brahmi F, Khodir M, Mohamed C, Pierre D. 2017. Chemical composition and biological activities of Mentha species. Aromatic and Medicinal Plants-Back to Nature, 47-78. 8. Dagar J, Minhas P. 2016. Agroforestry for the management of waterlogged saline soils and poor-quality waters. Advances in Agroforestry 13, 1875-1199. 9. Dhanda S, Sethi G, Behl R. 2004. Indices of drought tolerance in wheat genotypes at early stages of plant growth. Journal of Agronomy and Crop Science 190(1), 6-12. 10. El-Hendawy S, Hassan W, Al-Suhaibani N, Schmidhalter U. 2017. Spectral assessment of drought tolerance indices and grain yield in advanced spring wheat lines grown under full and limited water irrigation. Agricultural Water Management 182, 1-12.
11. Fernandez G.C. 1992. Effective selection criteria for assessing plant stress tolerance. In Proceeding of the International Symposium on Adaptation of Vegetables and other Food Crops in Temperature and Water Stress, Shanhua, Taiwan, 257-270. 12. Fischer R, Maurer R. 1978. Drought resistance in spring wheat cultivars. I. Grain yield responses. Australian Journal of Agricultural Research 29(5), 897-912. 13. Flowers T, Yeo A. 1995. Breeding for salinity resistance in crop plants: where next? Functional Plant Biology 22(6), 875-884. 14. Flowers T, Garcia A, Koyama M, Yeo A. 1997. Breeding for salt tolerance in crop plants-the role of molecular biology. Acta Physiologiae Plantarum 19(4), 427-433. 15. 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. Canadian Journal of Plant Science 77(4), 523-531. 16. Gokceoglu C. 2002. A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition. Engineering Geology 66(1), 39-51. 17. Grima M, Babuška R. 1999. Fuzzy model for the prediction of unconfined compressive strength of rock samples. International Journal of Rock Mechanics and Mining Sciences 36(3), 339-349. 18. Hefny M, Metwali E, Mohamed A. 2013. Assessment of genetic diversity of sorghum ('Sorghum bicolor'L. Moench) genotypes under saline irrigation water based on some selection indices. Australian Journal of Crop Science 7(12), 1935. 19. Hoagland D, Arnon D. 1950. The water-culture method for growing plants without soil. Circular. California Agricultural Experiment Station 347(2nd edit). 20. Holland J. 1975. Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press.
136 Int. J. Hort. Sci. Technol; Vol. 7, No. 2; June 2020
21. Hossain A, Sears R, Cox T, Paulsen G. 1990. Desiccation tolerance and its relationship to assimilate partitioning in winter wheat. Crop Science 30(3), 622-627. 22. Hosseini M, Agereh S, Khaledian Y, Zoghalchali H, Brevik E, Naeini S. 2017. Comparison of multiple statistical techniques to predict soil phosphorus. Applied Soil Ecology 114, 123-131. 23. Hosseini M, Naeini S, Dehghani A, Khaledian Y. 2016. Estimation of soil mechanical resistance parameter by using particle swarm optimization, genetic algorithm and multiple regression methods. Soil and Tillage Research 157, 32-42. 24. Iphar M, Yavuz M, Ak H. 2008. Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environmental Geology 56(1), 97-107. 25. Izaddoost H, Samizadeh H, Rabiel B, Abdollahi S. 2013. Evaluation of salt tolerance in rice (Oryza sativa L.) cultivars and lines with emphasis on stress tolerance indices. Cereal Research 3, 167-180. 26. Jang J. 1993. ANFIS: “adaptive-network-based fuzzy inference system” in IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665-685. 27. Kachout S, Mansoura A, Jaffel K, Leclerc J, Rejeb M, Ouerghi Z. 2009. The effect of salinity on the growth of the halophyte Atriplex hortensis. Applied Ecology and Environmental Research 7(4), 319-332. 28. Keutgen A, Pawelzik E. 2009. Impacts of NaCl stress on plant growth and mineral nutrient assimilation in two cultivars of strawberry. Environmental and experimental botany 65(2), 170-176. 29. Khaledian Y, Kiani F, Ebrahimi S, Brevik E, Aitkenhead‐Peterson J. 2017. Assessment and monitoring of soil degradation during land use change using multivariate analysis. Land Degradation and Development 28(1), 128-141. 30. Khalvandi M, Amerian M, Pirdashti H, Keramati S, Hosseini J. 2019. Essential oil of peppermint in symbiotic relationship with Piriformospora indica and methyl-jasmonate application under saline condition. Industrial Crops and Products 127, 195-202.
31. Kumar P, Mishra S, Malik A, Satya S. 2011. Insecticidal properties of Mentha species: a
review. Industrial Crops and Products 34(1), 802-817. 32. Liu J, Goering C, Tian L. 2001. A neural network for setting target corn yields. Transactions of the ASAE 44(3), 705. 33. Mardeh A, Ahmadi A, Poustini K, Mohammadi V. 2006. Evaluation of drought resistance indices under various environmental conditions. Field Crops Research 98(2), 222-229. 34. Minasny B, McBratney A, Brough D, Jacquier D. 2011. Models relating soil pH measurements in water and calcium chloride that incorporate electrolyte concentration. European Journal of Soil Science 62(5), 728-732. 35. Mohkami Z, Ranjbar A, Bidarnamani F. 2014. Essential oil compositions and antibacterial properties of mint (Mentha longifolia L.) and rosemary (Rosmarinus officinalis). Annual Research and Review in Biology 4(17), 2675-2683.
36. Mozaffarian V. 2008. Flora of Iran. Farhang Moaser Publication, Tehran, Iran. 37. Padmini D, Ilamparuthi K, Sudheer K. 2008. Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neuro-fuzzy models. Computers and Geotechnics 35(1), 33-46. 38. Rad M, Fanaei H, Rad M. 2015. Application of artificial neural networks to predict the final fruit weight and random forest to select important variables in native population of melon (Cucumis melo L.). Scientia Horticulturae 181, 108-112. 39. Ravari S, Dehghani H, Naghavi H. 2016. Assessment of salinity indices to identify Iranian wheat varieties using an artificial neural network. Annals of Applied Biology 168(2), 185-194. 40. Reich M, Aghajanzadeh T, Helm J, Parmar S, Hawkesford M, De Kok L. 2017. Chloride and sulfate salinity differently affect biomass, mineral nutrient composition and expression of sulfate transport and assimilation genes in Brassica rapa. Plant and Soil 411(1-2), 319-332.
41. Romagosa I, Borràs-Gelonch G, Slafer G, van Eeuwijk F. 2013. Genotype by Environment Interaction and Adaptation. In: Sustainable Food Production. Springer, New York, NY 846-870. 42. Rosielle A, Hamblin J. 1981. Theoretical aspects of selection for yield in stress and non-
Assessment of Salinity Indices to Identify Mint Ecotypes using Intelligent and … 137
stress environment. Crop Science 21(6), 943-946. 43. Rosielle A, Hamblin J. 1981. Theoretical aspects of selection for yield in stress and non-stress environment. Crop Science 21(6), 943-946.
44. Salehi B, Stojanović-Radić Z, Matejić J, Sharopov F, Antolak H, Kręgiel D, Sharifi-Rad J. 2018. Plants of Genus Mentha: From farm to food factory. Plants (Basel), 7(3), 70. 45. Shannon M. 1997. Adaptation of plants to salinity. Advances in Agronomy 60, 75-120. 46. Singh T, Sinha S, Singh V. 2007. Prediction of thermal conductivity of rock through physico-mechanical properties. Building and Environment 42(1), 146-155. 47. Sukanya S, Sudisha J, Hariprasad P, Niranjana S, Prakash H, Fathima S. 2009. Antimicrobial activity of leaf extracts of Indian medicinal plants against clinical and phytopathogenic bacteria. African journal of biotechnology 8(23), 6677-6682.
48. Tucker A. 2007. Mentha: Economic uses. Mint: The Genus Mentha. CRC Press, Taylor and Francis Group; Boca Raton, FL, USA. 519–522. 49. Van Gorder R. 2017. On the utility of the homotopy analysis method for non-analytic and global solutions to nonlinear differential equations. Numerical Algorithms 76(1), 151-162. 50. Viscarra Rossel R, Lark R. 2009. Improved analysis and modelling of soil diffuse reflectance spectra using wavelets. European Journal of Soil Science 60(3), 453-464. 51. Yılmaz I, Yuksek A. 2008. An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mechanics and Rock Engineering 41(5), 781-795. 52. Yu X, Liang C, Chen J, Qi X, Liu Y, Li W. 2015. The effects of salinity stress on morphological characteristics, mineral nutrient accumulation and essential oil yield and composition in Mentha canadensis L. Scientia Horticulturae 197, 579-583.