Assessment of Salinity Indices to Identify Mint Ecotypes using Intelligent and Regression Models

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Hosseini Seyyed Jaber; Tahmasebi Sarvestani Zeinolabedin; PIRDASHTI HEMATOLLAH; Modarres Sanavy Seyyed Ali Mohammad; MOKHTASSI BIDGOLI ALI; Hazrati Saeid; Nicola Silvana

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Journal: INTERNATIONAL JOURNAL OF HORTICULTURAL SCIENCE AND TECHNOLOGY Year:2020 | Volume:7 | Issue:2 Page(s): 119-137

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Title

Assessment of Salinity Indices to Identify Mint Ecotypes using Intelligent and Regression Models

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Keywords

Mint

predict

regression model

salinity

Abstract

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.

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APA: Copy

Hosseini, Seyyed Jaber, Tahmasebi Sarvestani, Zeinolabedin, PIRDASHTI, HEMATOLLAH, Modarres Sanavy, Seyyed Ali Mohammad, MOKHTASSI BIDGOLI, ALI, Hazrati, Saeid, & Nicola, Silvana. (2020). Assessment of Salinity Indices to Identify Mint Ecotypes using Intelligent and Regression Models. INTERNATIONAL JOURNAL OF HORTICULTURAL SCIENCE AND TECHNOLOGY, 7(2), 119-137. SID. https://sid.ir/paper/347943/en

Vancouver: Copy

Hosseini Seyyed Jaber, Tahmasebi Sarvestani Zeinolabedin, PIRDASHTI HEMATOLLAH, Modarres Sanavy Seyyed Ali Mohammad, MOKHTASSI BIDGOLI ALI, Hazrati Saeid, Nicola Silvana. Assessment of Salinity Indices to Identify Mint Ecotypes using Intelligent and Regression Models. INTERNATIONAL JOURNAL OF HORTICULTURAL SCIENCE AND TECHNOLOGY[Internet]. 2020;7(2):119-137. Available from: https://sid.ir/paper/347943/en

IEEE: Copy

Seyyed Jaber Hosseini, Zeinolabedin Tahmasebi Sarvestani, HEMATOLLAH PIRDASHTI, Seyyed Ali Mohammad Modarres Sanavy, ALI MOKHTASSI BIDGOLI, Saeid Hazrati, and Silvana Nicola, “Assessment of Salinity Indices to Identify Mint Ecotypes using Intelligent and Regression Models,” INTERNATIONAL JOURNAL OF HORTICULTURAL SCIENCE AND TECHNOLOGY, vol. 7, no. 2, pp. 119–137, 2020, [Online]. Available: https://sid.ir/paper/347943/en

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