دوره 11، شماره 32 - ( زمستان 1398 )                   جلد 11 شماره 32 صفحات 73-59 | برگشت به فهرست نسخه ها


XML English Abstract Print


گروه زراعت دانشکده کشاورزی دانشگاه تربیت مدرس
چکیده:   (2473 مشاهده)
     سطح برگ یک شاخص کلیدی برای رشد و تولید محصولات گیاهیو همچنین عاملی تعیین ­کننده در کارایی مصرف نور محسوب می­گردد، لذا بررسی تنوع و همچنین تخمین سطح برگ در اکوتیپ ­های مختلف نعناع از اهمیت خاصی برخوردار است. از جمله روش­ های معمول برای تخمین سطح برگ تجزیه و تحلیل رگرسیونی می ­باشد که سطح برگ به­ عنوان متغیر مستقل، و طول و عرض برگ به­ عنوان متغیر وابسته می ­باشند. در این مطالعه سطح برگ 18 اکوتیپ گیاه دارویی نعناع با مدل ­های مختلف ANFIS، شبکه عصبی مصنوعی (MLP وRBFرگرسیون ­های خطی و غیرخطی با استفاده از دو ورودی طول و عرض برگ در چهار سطح تنش شوری (شاهد، 5/2، 5 و 5/7 دسی­زیمنس­بر­متر) و در دو مرحله برداشت تخمین زده شد. نتایج نشان داد که همبستگی بالایی بین طول و عرض با سطح برگ وجود دارد، به­ طوری که همبستگی عرض با سطح برگ بیشتر از طول برگ بود. از بین مدل ­های رگرسیونی در هر دو برداشت و در تمامی سطوح تنش، مدل NLR به­ عنوان بهترین و دقیق ­ترین مدل معرفی شده است. مدل ANFIS در هر دو مرحله برداشت و در تمامی سطوح تنش نسبت به سایر مدل­ ها از دقت بالاتری برخوردار بود و میزان خطای کمتری را به ­خود اختصاص داد. همچنین نتایج حاصل از تجزیه کلاستر نشان داد که تنوع مطلوبی بین اکوتیپ ­ها وجود دارد. علاوه ­بر این مقایسه میانگین بین گروه ­های مختلف حاصل از تجزیه کلاستر نشان داد که بیشترین مقدار طول، عرض و سطح برگ در هر دو مرحله برداشت و در تمام سطوح تنش شوری مربوط به اکوتیپ 18 (E18) بوده است.

 
متن کامل [PDF 1070 kb]   (804 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: اصلاح نباتات، بیومتری
دریافت: 1398/2/2 | ویرایش نهایی: 1398/11/16 | پذیرش: 1398/7/9 | انتشار: 1398/10/23

فهرست منابع
1. Aboukarim, A.M., H.A. Elsoury and M. Menyawi. 2015. Artificial neural network model for the prediction of the cotton crop leaf area. International Journal of Plant and Soil Science, 8(4): 1-13. [DOI:10.9734/IJPSS/2015/19686]
2. Ahmadian-Moghadam, H. 2012. Prediction of pepper (Capsicum annuum L.) leaf area using group method of data handling-type neural networks. International Journal of Agriculture Science, 2(11): 993-999.
3. Amiri, M.J. and A. Shabani. 2017. Application of an Adaptive Neural-Based Fuzzy Inference System Model for Predicting Leaf Area. Communications in Soil Science and Plant Analysis, 48(14): 1669-1683. [DOI:10.1080/00103624.2017.1373801]
4. Amiri, M.J., J. Abedi-Koupai, S.S. Eslamian, S.F. Mousavi and H. Hasheminejad. 2013. Modeling Pb (II) adsorption from aqueous solution by ostrich bone ash using adaptive neural-based fuzzy inference system. Journal of Environmental Science and Health, Part A 48: 543-558. [DOI:10.1080/10934529.2013.730451]
5. Aschonitis, V.G., D.M. Papamichail, A. Lithourgidis and E.A. Fano. 2014. Estimation of leaf area index and foliage area index of rice using an indirect gravimetric method. Communications in Soil Science and Plant Analysis, 45: 1726-1740. [DOI:10.1080/00103624.2014.907917]
6. Basbag, S., R. Ekinci and G. Oktay. 2008. Relationships between some physio-morphological traits and cotton (Gossypium hirsutum L.) yield. International Cotton Advisory Committee. Tenth Regional Meeting.
7. Blanco, F.F. and M.V. Folegatti. 2005. Estimation of leaf area for greenhouse cucumber by linear measurements under salinity and grafting. Scientia Agricola, 62(4): 305-309. [DOI:10.1590/S0103-90162005000400001]
8. Caldas, L.S., C. Bravo, H. Piccolo and C.R. Faria. 1992. Measurement of leaf area with a hand-scanner linked to a microcomputer. Revista Brasileria de Fisiologia Vegetal, 4: 17-20.
9. Cargnelutti Filho, A., M. Toebe, B. Mendonça Alves, C. Burin and J.A. Kleinpaul. 2015. Estimation of leaf area of canola by leaf size. Bragantia, 74(2): 139-148. [DOI:10.1590/1678-4499.0388]
10. Cargnelutti Filho, A., M. Toebe, C. Burin, A.L. Fick and G. Casarotto. 2012. Estimate of leaf area of forage turnip according to leaf dimensions. Bragantia, 71(1): 47-51. [DOI:10.1590/S0006-87052012000100008]
11. Carvalho, J.O., M. Toebe, F.L. Tartaglia, C.T. Bandeira and A.L. Tambara. 2017. Leaf area estimation from linear measurements in different ages of Crotalaria juncea plants. Anais da Academia Brasileira de Ciências, 89(3): 1851-1868. [DOI:10.1590/0001-3765201720170077]
12. Cristofori, V., Y. Rouphael, E. Mendoza-de Gyves and C. Bignami. 2007. A simple model for estimating leaf area of hazelnut from linear measurements. Scientia Horticalture, 113(2): 221-225. [DOI:10.1016/j.scienta.2007.02.006]
13. Daughtry, C.S.T. 1990. Direct measurements of canopy structure. International Journal of Remote Sensing Reviews, 5: 45-60. [DOI:10.1080/02757259009532121]
14. De Aquino, L.A., V.C. dos Santos Júnior, J.V. Santos Guerra and M. Moreira Costa. 2011. Estimates of sunflower leaf area by a non-destructive method. Bragantia, 70(4): 832-836. [DOI:10.1590/S0006-87052011000400015]
15. Ghanbari, S., A. Nooshkam, B.A. Fakheri and N. Mahdinezhad. 2019. Relationship between Yield and its Component in Soybean Genotypes (Glycine Max L.) using Multivariate Statistical Methods. Journal of Crop Breeding, 11(29): 85-92 (In Persian). [DOI:10.29252/jcb.11.29.85]
16. Ghorbani, H., H. Kashi, N. Hafezi Moghadas and S. Emamgholizadeh. 2015. Estimation of soil cation exchange capacity using multiple regression, artificial neural networks, and adaptive neuro-fuzzy inference system models in Golestan province, Iran. Communications in Soil Science and Plant Analysis, 46: 763-780. [DOI:10.1080/00103624.2015.1006367]
17. Granier, C., C. Massonnet, O. Turc, B. Muller, K. Chenu and F. Tardieu. 2002. Individual leaf development in Arabidopsis thaliana: a stable thermal-time-based programme. Annals of Botany, 89(5): 595-604. [DOI:10.1093/aob/mcf085]
18. Gupta, D.K., R. Prasad, P. Kumar, V. N. Mishra, P.K.S. Dikshit, S.B. Dwivedi and P.K. Srivastava. 2015. Crop variables estimation by adaptive neuro-fuzzy inference system using bistatic scatterometer data. In Microwave and Photonics (ICMAP), 2015 International Conference on 1-2 pp. [DOI:10.1109/ICMAP.2015.7408756]
19. Hinnah, F., A. Heldwein, I. Maldaner, L. Loose, D.D. Lucas and M. Bortoluzzi. 2014. Estimation of eggplant leaf area from leaf dimensions. Bragantia, 73(3): 213-218. [DOI:10.1590/1678-4499.0083]
20. Hosseini, M., S.A.M. Naeini, A.A. Dehghani and Y. Khaledian. 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. [DOI:10.1016/j.still.2015.11.004]
21. Hosseini, M.S., D. Samsampour, M. Ebrahimi and M. Khanahmadi. 2019. Study of Physiological and Biochemical Changes of Iraninan Licorice (Glycyrrhiza Glabra) under Salinity Stress in Filed Condition. Journal of Crop Breeding, 11(29): 193-201 (In Persian). [DOI:10.29252/jcb.11.29.193]
22. Hosseini, M., S.R. Agereh, Y. Khaledian, H.J. Zoghalchali, E.C. Brevik and S.A. Naeini. 2017. Comparison of multiple statistical techniques to predict soil phosphorus. Applied Soil Ecology, 114: 123-131. [DOI:10.1016/j.apsoil.2017.02.011]
23. Izadi, A., G. Ahmadvand, M. Asna Ashari and K. Piri. 2010. Effect of nitrogen and planting density on some growth characteristics, performance and essential oil in peppermint (Mentha piperita L.), Iranian Journal of Crop Research, 8(5): 824-836 (In Persian).
24. Kandiannan, K., U. Parthasarathy, K.S. Krishnamurthy, C.K. Thankamani and V. Srinivasan. 2009. Modeling individual leaf area of ginger (Zingiber officinale Roscoe) using leaf length and width. Scientia Horticulturae, 120(4): 532-537. [DOI:10.1016/j.scienta.2008.11.037]
25. Kazem Alvandi R., A. Sharifan, M. Aghazadeh Meshghi. 2010. Investigation of the chemical composition and antimicrobial effect of essential oil of peppermint (Mentha piperita L.). Scientific Journal of Pathobiology, 4: 355- 364.
26. Keramatlou, I., M. Sharifani, H. Sabouri, M. Alizadeh and B. Kamkar. 2015. A simple linear model for leaf area estimation in Persian walnut (Juglans regia L.). Scientia Horticulturae, 184: 36-39. [DOI:10.1016/j.scienta.2014.12.017]
27. Khaledian, Y., E.C. Brevik, P. Pereira, A. Cerdà, M.A. Fattah and H. Tazikeh. 2017. Modeling soil cation exchange capacity in multiple countries. Catena, 158: 194-200. [DOI:10.1016/j.catena.2017.07.002]
28. Khaledian, Y., J.N. Quinton, E.C. Brevik, P. Pereira and M. Zeraatpisheh. 2018. Developing global pedotransfer functions to estimate available soil phosphorus. Science of the Total Environment, 644: 1110-1116. [DOI:10.1016/j.scitotenv.2018.06.394]
29. Khoshnevisan, B., S. Rafiee, M. Omid and H. Mousazadeh. 2014. Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Information processing in agriculture, 1(1): 14-22. [DOI:10.1016/j.inpa.2014.04.001]
30. Kumar, K., S. Kumar, V. Sankar, T. Sakthivel, G. Karunakaran and P.C. Tripathi. 2017. Non-destructive estimation of leaf area of durian (Durio zibethinus)-An artificial neural network approach. Scientia horticulturae, 219: 319-325. [DOI:10.1016/j.scienta.2017.03.028]
31. Leroy, C., L. Saint-Andre and D. Auclair. 2007. Practical methods for non-destructive measurement of tree leaf area. Agroforestry systems, 71(2): 99-108. [DOI:10.1007/s10457-007-9077-2]
32. Laio, F., G. Di Baldassarre and A. Montanari. 2009. Model selection techniques for the frequency analysis of hydrological extremes. Water Resources Research, 45(7): W07416. [DOI:10.1029/2007WR006666]
33. Lizaso, J.I., W.D. Batchelor and M.E. Westgate. 2003. A leaf area model to simulate cultivar-specific expansion and senescence of maize leaves. Field Crops Research, 80: 1-17. [DOI:10.1016/S0378-4290(02)00151-X]
34. Mahmoudi, A. and M. Danesh. 2019. Assessment of Salinity Effects on Some Morphological and Physiological Traits and In Vitro Culture of Halophyte Plant (Salicornia Europaea). Journal of Crop Breeding, 11(29):161-168 (In Persian). [DOI:10.29252/jcb.11.29.161]
35. Marashi, M., A.M. Torkashvand, A. Ahmadi and M. Esfandyari. 2019. Adaptive neuro-fuzzy inference system: Estimation of soil aggregates stability. Acta Ecologica Sinica, 39(1): 95-101. [DOI:10.1016/j.chnaes.2018.05.002]
36. Mousavi, S.F. and M.J. Amiri. 2012. Modeling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system. Soil Water Research, 7(2): 73-83. [DOI:10.17221/46/2010-SWR]
37. Naderloo, L., R. Alimardani, M. Omid, F. Sarmadian, P. Javadikia, M.Y. Torabi and F. Alimardani. 2012. Application of ANFIS to predict crop yield based on different energy inputs. Measurement, 45(6): 1406-1413. [DOI:10.1016/j.measurement.2012.03.025]
38. Nyakwende, E., C.J. Paull and J.G. Atherton. 1997. Non-destructive determination of leaf area in tomato plants using image processing. Journal of Horticulture Science, 72(2): 225-262. [DOI:10.1080/14620316.1997.11515512]
39. Odabas, M.S., E. Ergun and F. Oner. 2013. Artificial neural network approach for the prediction of the corn (Zea mays L.) leaf area. Bulgarian Journal of Agricultural Science, 19(4): 766-769.
40. Olsoy, P.J., J.J. Mitchell, D.F. Levia, P.E. Clark and N.F. Glenn. 2016. Estimation of big sagebrush leaf area index with terrestrial laser scanning. Ecological indicators, 61: 815-821. [DOI:10.1016/j.ecolind.2015.10.034]
41. Rodríguez Padrón, R.A., S.J. Lopes, A. Swarowsky, R.R. Cerquera, C.U. Nogueira and M. Maffei. 2016. Nondestructive models to estimate leaf area on bell pepper crop. Ciência Rural, 46(11): 1938-1944. [DOI:10.1590/0103-8478cr20151324]
42. Ross, J., V. Ross and A. Koppel. 2000. Estimation of leaf area and its vertical distribution during growth period. Agricultural and Forest Meteorology, 101: 237-246. [DOI:10.1016/S0168-1923(00)00089-7]
43. Schwab, N.T., N.A. Streck, A. Rehbein, B.S. Ribeiro, L.O. Ulhmann, J.A. Langner and C.C. Becker. 2014. Linear dimensions of leaves and its use for estimating the vertical profile of leaf area in gladiolus. Bragantia, 73(2): 97-105. [DOI:10.1590/brag.2014.014]
44. Shabani, A., A.R. Sepaskhah and A.A. Kamgar-Haghighi. 2013. Growth and physiologic response of rapeseed (Brassica napus L.) to deficit irrigation, water salinity and planting method. International Journal of Plant Production, 7(3): 569-96.
45. Shabani, A., K.A. Ghaffary, A.R. Sepaskhah and A.A. Kamgar-Haghighi. 2017. Using the artificial neural network to estimate leaf area. Scientia Horticulturae, 216: 103-110. [DOI:10.1016/j.scienta.2016.12.032]
46. Taherian, M., M.R. Bihamta, S.A. Peyghambari, H. Alizadeh and A. Rasoulnia. 2019. Stability Analysis and Selection of Salinity Tolerant barley Genotypes. Journal of Crop Breeding, 11(29): 93-103 (In Persian). [DOI:10.29252/jcb.11.29.93]
47. Toebe, M., A. Cargnelutti Filho, L.H. Loose, A.B. Heldwein and A.J. Zanon. 2012. Leaf area of snap bean (Phaseolus vulgaris L.) according to leaf dimensions. Semina: Ciências Agrárias, 33(1): 2491-2500. [DOI:10.5433/1679-0359.2012v33Supl1p2491]
48. Vazquez-Cruz, M.A., R. Luna-Rubio, L.M. Contreras-Medina, I. Torres-Pacheco and R.G. Guevara-Gonzalez. 2012. Estimating the response of tomato (Solanum lycopersicum) leaf area to changes in climate and salicylic acid applications by means of artificial neural networks. Biosystems Engineering, 112(4): 319-327. [DOI:10.1016/j.biosystemseng.2012.05.003]
49. Yilmaz, I. and O. Kaynar. 2011. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications, 38: 5958-5966. [DOI:10.1016/j.eswa.2010.11.027]

بازنشر اطلاعات
Creative Commons License این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است.