Volume 8, Issue 2 ((Autumn & Winter) 2022)                   Iranian J. Seed Res. 2022, 8(2): 69-80 | Back to browse issues page

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Skandarnejad S, Gholipoor M, Makarian H. Evaluating the optimization of irradiation components of mung bean seeds with ultrasound for increased seedling vigor, using artificial neural network. Iranian J. Seed Res.. 2022; 8 (2) :69-80
URL: http://yujs.yu.ac.ir/jisr/article-1-176-en.html
Shahrood University , manouchehr.gholipoor@gmail.com
Abstract:   (1032 Views)

Extended Abstract 
 Introduction: A large number of experimental evidence indicates the positive effect of irradiating the seed with ultrasonic waves; so that irradiation causes the production of a more vigorous seedling. Conversely, inappropriate intensity and duration of irradiation can impose deleterious effects on seedlings by damaging the enzymatic activity. There are complex inter-and intra-relations between irradiation components (pre-soaking duration, temperature, and duration of irradiation) and response variables [seedling dry weight (SDW) and percent of abnormal seedlings (PAS)]. Therefore the balance values of the irradiation components cannot be precisely obtained by mean comparison. This study aimed to optimize (finding the balance values of) irradiation components for increased SDW, but diminished PAS of mung bean, using an artificial neural network.
 Materials and Methods: A factorial experiment was conducted based on a completely randomized design with three replications. The factors were six pre-soaking durations (2, 4, 6, 8, 10, and 12 hours), 5 irradiation durations (0, 3, 6, 9 and 12 minutes), and 4 irradiation temperatures (17, 22, 27, and 32 oC). The 25 seeds were chosen for each petri dish. The multi-layer perceptron neural network was used to quantify the relations between variables; the experimental factors were used as the input (regressors), and PAS and SDW as the output of the model (response variables).
Results: The analysis of variance results indicated that the simple and interactive effects of factors were significant on PAS and SDW. The structure 3:3:2 of the neural network, which is based on Secant Hyperbolic function, was suitable. The SDW and PAS were negligibly different for the contribution of the factors in determining their changes. In terms of relative contribution, the factors ranked from higher to lower as irradiation duration, irradiation temperature, and pre-soaking duration. The optimized values of components of irradiation by the neural network were irradiation temperature of 17.96 oC, irradiation duration of 5.3 minutes, and pre-soaking duration of 11.25 hours. For these components, SDW was 27% higher, and PAS tended to be 0.6% lower, compared to the best component combination gotten by mean comparison.
Conclusion: Due to the highly strong interaction of irradiation components on seedling growth, the effect of component (s) tends to be changed intensively with changing the quantity of each component. In terms of finding the best combination of irradiation components, the neural network was more efficient than the mean comparison. Therefore, the neural network could be used as a complementary procedure in such investigations.

1- Irradiation components including irradiation duration and temperature, and pre-soaking duration affected seedling growth.
2- Inappropriate irradiation components diminished seedling growth to the below of no-irradiation conditions.
3- The optimum (balanced) levels of irradiation components increased seedling growth remarkably.

Article number: 5
Full-Text [PDF 417 kb]   (137 Downloads)    
Type of Study: Research | Subject: Seed Physiology
Received: 2021/04/9 | Accepted: 2021/10/3

1. Alvandian, S., Vahedi, A. and Taghizadeh, R. 2013. Study of ultrasonic waves and low temperature effects on germination of myrtle (Myrtus communis). Journal of Seed Research, 3(3): 21-31. [In Persian with English Summary].
2. Babaei, A., Alebrahim, M.T., MacGregor, D.R., Khatami, A. and Hasani_Nasab, R. 2020. Evaluation of ultrasound technology to break seed dormancy of common lambsquarters (Chenopodium album). Food Science and Nutrition, 8(6): 2662-2669. [DOI:10.1002/fsn3.1547] [PMID] [PMCID]
3. Bar, R. 1988. Ultrasound-enhanced bioprocesses: cholesterol oxidation by Rhodococcus erythropolis. Biotechnology and Bioengineering, 32: 655- 663. [DOI:10.1002/bit.260320510] [PMID]
4. Barton, S., Bullock, C. and Weir, D. 1996. The effects of ultrasound on the activities of some glucosidase enzymes of industrial importance. Enzyme and Microbial Technology, 18(3): 190-194. [DOI:10.1016/0141-0229(95)00092-5]
5. Bewley, J.D. and Black, M. 1982. Physiology and biochemistry of seeds in relation to germination: volume 2: viability, dormancy, and environmental control. Springer Science & Business Media. [DOI:10.1007/978-3-642-68643-6_2]
6. Bommannan, D., Menon, G.K., Okuyama, H., Elias, P.M. and Guy, R.H. 1992. Sonophoresis: II Examination of the mechanism(s) of ultrasound enhanced transdermal drug delivery. Pharmaceutical Research, 9(8): 1043-1047. [DOI:10.1023/A:1015806528336] [PMID]
7. Bonto, A.P., Tiozon Jr, R.N., Sreenivasulu, N. and Camacho, D.H. 2021. Impact of ultrasonic treatment on rice starch and grain functional properties: A review. Ultrasonics Sonochemistry, 71: 105383. [DOI:10.1016/j.ultsonch.2020.105383] [PMID] [PMCID]
8. Chang, D.H. and Islam, S. 2000. Estimation of soil physical properties using remote sensing and artificial neural network. Remote Sensing of Environment, 74: 534-544. [DOI:10.1016/S0034-4257(00)00144-9]
9. Drummond, S.T., Sudduth, K.A., Joshi, A., Birrell, S.J. and Kitchen, N.R. 2003. Statistical and neural methods for site-specific yield prediction. Transactions of the American Society of Agricultural and Biological Engineers (ASABE), 46(1): 5-14. [DOI:10.13031/2013.12541]
10. Fariabi, A., Zarmanesh, H., Keshvari, M. and Abdoli, N. 2009. Effect of ultrasonic waves on physiological and morphological processes of germination in bell pepper (Capsicum annum) and radish (Rhaphnus sativus). Proceedings of first national symposium of seed science and technology, Gorgan, Iran. [In Persian with English Summary].
11. Gholipoor, M., Emamgholizadeh, S., Hassanpour, H., Shahsavani, D., Shahoseini, H., Baghi, M. and Karim, A. 2012. The optimization of root nutrient content for increased sugar beet productivity using an artificial neural network. International Journal of Plant Production, 6: 429-442.
12. Gholipoor, M., Rohani, A. and Torani, S. 2013. Optimization of traits to increasing barley grain yield using an artificial neural network. International Journal of Plant Production, 7(1): 1-18.
13. Goussous, S.J., Samarah, N.H., Alqudah, A.M. and Othman, M.O. 2010. Enhancing seed germination of four crops species using an ultrasonic technique. Experimental Agriculture, 46(2): 231-242. [DOI:10.1017/S0014479709991062]
14. Green, T.R., Salas, J.D., Martinez, A. and Erskine, R.H. 2007. Relating crop yield to topographic attributes using spatial analysis neural networks and regression. Geoderma, 139(1): 23-37. [DOI:10.1016/j.geoderma.2006.12.004]
15. Huang, Y., Lan, Y., Thomson, S.J., Fang, A., Hoffmann, W.C. and Lacey, R.E. 2010. Development of soft computing and applications in agricultural and biological engineering. Computer, Electronic and Agriculture, 71: 107-127. [DOI:10.1016/j.compag.2010.01.001]
16. Jin, Y.Q., and Liu, C. 1997. Biomass retrieval from high-dimensional active/passive remote sensing data by using artificial neural networks. International Journal of Remote Sensing 18(4): 971-979. [DOI:10.1080/014311697218863]
17. Kashi, H., Emamgholizadeh, S., Ghorbani, H. and Hashemi, A.I. 2013. Estimation of soil Infiltration in agricultural and pasture lands using artificial neural networks and multiple regressions. Journal of Environmental Erosion Researches, 9: 42-56. [In Persian with English Summary].
18. Kaul, M., Hill, R.L. and Walthall, C. 2005. Artificial neural networks for corn and soybean yield prediction. Agricultural Systems, 85(1): 1-18. [DOI:10.1016/j.agsy.2004.07.009]
19. Lib, Y., Pana, D., Caoa, J., Liua, L., Zhoucd, X. and Barbae, F.J. 2020. Characterizing physicochemical, nutritional and quality attributes of wholegrain Oryza sativa L. subjected to high intensity ultrasound-stimulated pre-germination. Food Control, 108: 106827. [DOI:10.1016/j.foodcont.2019.106827]
20. Liu, Y., Takatsuki, H., Yoshikoshi, A., Wang, B.C. and Sakanishi, A. 2003. Effects of ultrasound on the growth and vacuolar H+-ATPase activity of aloe arborescens callus cells. Colloids and Surfaces (Biointerfaces), 32(2): 105-116. [DOI:10.1016/S0927-7765(03)00150-4]
21. Machikowa, T., Kulrattanarak, T. and Wonprasaid, S. 2013. Effects of ultrasonic treatment on germination of synthetic sunflower seeds. International Journal of Agricultural, Biosystems Science and Engineering, 7(1): 1-3.
22. Miano, A.C., Sabadoti, V.D. and Augusto P.E.D. 2019. Combining ionizing irradiation and ultrasound technologies: effect on beans hydration and germination. Journal of Food Science, 84: 3179-3185. [DOI:10.1111/1750-3841.14819] [PMID]
23. Park, S.J., Hwang, C.S. and Vlek, P.L.G. 2005. Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions. Agricultural Systems, 85: 59-81. [DOI:10.1016/j.agsy.2004.06.021]
24. Pitt, W.G. and Ross, S.A. 2003. Ultrasound increases the rate of bacterial cell growth. Biotechnology Progress, 19(3): 1038-1044. [DOI:10.1021/bp0340685] [PMID] [PMCID]
25. Polachini, T.C., Mulet, A., Telis-Romero, J. and Cárcel, J.A. 2019. Influence of high-intensity ultrasound application on the kinetics of sugar release from acid suspensions of artichoke (Cynara scolymus) biomass. Chemical Engineering and Processing-Process Intensification, 145: 107-113. [DOI:10.1016/j.cep.2019.107681]
26. Rajabian, S. 2012. Effect of ultrasonic waves and pseudomonas bacteria on growth and growth and yield of corn. MSc thesis, Shahrood University of Technology, Shahrood, Iran. [In Persian with English Summary].
27. Rohani, A., Rangbar, A., Abbasporfard, M.H., Ajabshirchi, Y. and Valizadeh, M. 2009. Prediction of repair and maintenance costs for two-wheel drive tractor using artificial neural network and comparison with regression. Journal of Natural Resources and Agricultural Sciences, 16: 1-12 [In Persian with English Summary].
28. Salehzadeh, H., Gholipoor, M., Abbasdokht, H., Baradaran, M. 2016. Optimizing plant traits to increase yield quality and quantity in tobacco using artificial neural network. International Journal of Plant Production, 10: 97-108.
29. Wang, J., Bian, Z., Wang, S. and Zhang, L. 2020. Effects of ultrasonic waves, microwaves, and thermal stress treatment on the germination of Tartary buckwheat seeds. Journal of Food Process Engineering, 43: e13494. [DOI:10.1111/jfpe.13494]
30. Yaldagard, M., Mortazavi, S.A. and Tabatabaie, F. 2008a. Influence of ultrasonic on the germination of barley seed and its alpha-amylase. African Journal of Biotechnology, 7: 2456-2471.
31. Yaldagard, M., Mortazavi, S.A. and Tabatabaie, F. 2008b. The effect of ultrasound in combination with thermal treatment on the germinated barley's alpha-amylase activity. Korean Journal of Chemical Engineering, 25(3): 517-523. [DOI:10.1007/s11814-008-0087-1]
32. Zhang, W.J. and Barrion, A.T. 2006. Function approximation and documentation of sampling data using artificial neural networks. Environmental Monitoring and Assessment, 122(1): 185-201. [DOI:10.1007/s10661-005-9173-6] [PMID]

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