اخبار و اعلانات

 آمار

تعداد نشریات282
تعداد نشریات سازمان80
تعداد شماره‌ها3,168
تعداد مقالات35,197
تعداد مشاهده مقاله50,943,713
تعداد دریافت فایل اصل مقاله90,948,871
Alizadeh, Azadeh, Ghasemnezhad, Azim, Hezarjaribi, Aboutaleb, Aladdin, Mohammad Zaman. (1401). The Effect of Soil Physical and Chemical Properties on the Performance Indices of Artichoke’s Leaf using Artificial Neural Network (ANN). سامانه مدیریت نشریات علمی, 13(1), 123-135. doi: 10.22034/jmpb.2023.355310.1384
Azadeh Alizadeh; Azim Ghasemnezhad; Aboutaleb Hezarjaribi; Mohammad Zaman Aladdin. "The Effect of Soil Physical and Chemical Properties on the Performance Indices of Artichoke’s Leaf using Artificial Neural Network (ANN)". سامانه مدیریت نشریات علمی, 13, 1, 1401, 123-135. doi: 10.22034/jmpb.2023.355310.1384
Alizadeh, Azadeh, Ghasemnezhad, Azim, Hezarjaribi, Aboutaleb, Aladdin, Mohammad Zaman. (1401). 'The Effect of Soil Physical and Chemical Properties on the Performance Indices of Artichoke’s Leaf using Artificial Neural Network (ANN)', سامانه مدیریت نشریات علمی, 13(1), pp. 123-135. doi: 10.22034/jmpb.2023.355310.1384
Alizadeh, Azadeh, Ghasemnezhad, Azim, Hezarjaribi, Aboutaleb, Aladdin, Mohammad Zaman. The Effect of Soil Physical and Chemical Properties on the Performance Indices of Artichoke’s Leaf using Artificial Neural Network (ANN). سامانه مدیریت نشریات علمی, 1401; 13(1): 123-135. doi: 10.22034/jmpb.2023.355310.1384

The Effect of Soil Physical and Chemical Properties on the Performance Indices of Artichoke’s Leaf using Artificial Neural Network (ANN)

Journal of Medicinal plants and By-products
دوره 13، شماره 1، خرداد 2024، صفحه 123-135    اصل مقاله (997.9 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22034/jmpb.2023.355310.1384
نویسندگان
Azadeh Alizadeh1؛ Azim Ghasemnezhad* 1؛ Aboutaleb Hezarjaribi1؛ Mohammad Zaman Aladdin2
1Department of Horticultural, Gorgan University of Agriculture and Natural Resources, Gorgan, Iran
2Department of soil Sciences, Gorgan University of Agricultural Sciences and Natural Resources
چکیده
The present study aims to estimate the performance of artichoke via physic-chemical parameters of soil including soil texture, pH, and bulk density using the artificial neural network (ANN) method. Thus, the soils of sixty points across croplands and forests of Golestan province, Iran were sampled, and soil parameters were measured in the lab. Based on the obtained parameters the different models were performed. The experiment was conducted as a randomized complete block design with three replications. The results showed that ANN models were more efficient than the multivariate regression models (MR model). All ANN models were better to estimate plant weight performance compared with the MR model. Plants grown in the soil samples of the “Ahangar Mahalleh area” showed the highest level of yield performance. Based on the findings, model number 5 with a minimum input parameter was selected as an optimal model. All ANN models were better than the multivariate regression models in the estimation of plant weight. As model 5 had almost similar performance with a minimal number of inputs compared with the other models, this model can be selected as the best model.
کلیدواژه‌ها
Cynara scolymus؛ Easily Accessible Soil Parameters؛ Performance
مراجع
  1. Ebrahimi‐Mameghani M., Asghari‐Jafarabadi M., Rezazadeh K. TCF7L2-rs7903146 polymorphism modulates the effect of artichoke leaf extract supplementation on insulin resistance in metabolic syndrome: a randomized, double-blind, placebo-controlled trial. J. Integr. Med. 2018;16:329–334.
  2. Sabater C., Sabater V., Olano A., Montilla A., Corzo N. Ultrasound-assisted extraction of pectin from artichoke by-products. An artificial neural network approach to pectin characterization. Food Hydrocoll. 2020;98:105238.
  3. Bergmeir C., Benitez J.M. Neural networks in R using the stuttgart neural network simulator: RSNNS. J. Stat. Softw. 2012;46:1–26.
  4. Sabater C., Molina-Tijeras J.A., Vezza T., Corzo N., Montilla A., Utrillab P. Intestinal anti-inflammatory effects of artichoke pectin and modified pectin fractions in the dextran sulfate sodium model of mice colitis. Artificial neural network modelling of inflammatory markers. Food & Function. 2019;10(12):7793–7805
  5. Kumar N., Raghuwanshi N.S., Singh R., Wallender W.W., Pruitt W.O. Estimating evapotranspiration using artificial network. J. Irrig. Drain. Eng. 2002;128(4):224-233.
  6. Drummond S.T., Sudduth K.A., Joshi A., Birrell SL, Kitchen NR. Statistical and neural methods for site-specific yield prediction. Transactions of the ASAE. 2003;46(1):5-14.
  7. Cravener T.L., Roush W.B. Prediction of amino acid profiles in feed ingredients:genetic algorithm calibration of artificial neural networks. Anim. Feed Sci. Technol. 2001;90:131-141.
  8. Sabourifard H., Ghasemnezhad A., Hemmati K., Hezarjeribi A., Bahrami M., Nosrati M. Assessment of summer savory (Satureja hortensis L.) biomass by easily-attainable soil parameters and artificial network. J. Hortic. Sci. 2017;31(3):448-456.
  9. Page A., Miller R., Keeney D. Methods of soil analysis.2th ed. Part 2: chemical and biological properties. Soil Sci. Soc. Am. Inc. publisher. 1987.
  10. Movahedi Naeini S.A., Rezaei M. Soil physics (fundamentals and applications). Gorgan University of Agricultural Sciences and Natural Resources Publications, 474 p. 2008. (In Persian)
  11. Bremner J.S., Mulvaney C.S. Nitrogen-total. In A. L. Page (Ed.), Methods of soil analysis, Part 2. American Society of Agronomy (pp. 595-624). Madison, Wisconsin. 1982.
  12. Sajikumara N., Thandaveswra B. A non-linear rainfall- runoff model using an Artificial Neural Network. Hydrol. J. 1999;216:32-55.
  13. Hill M. Methods and guidelines for effective model calibration. U.S. Geological survey Water- Resources Investigations Rep. 1998;98-4005.
  14. Sabourifard H., Ghasemnezhad A., Hemmati K., Hezarjeribi A., Bahrami M., Nosrati M. Assessment of summer savory (Satureja hortensis L.) biomass by easily-attainable soil parameters and artificial network. J. Plant Prod. Sci. 2017;26(2):47-58.
  15. Schaap M., Leij F., van Genuchten M. Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Sci. Soc. Am. J. 1998;62:847-855.
  16. Schaap M., Leij F. Using neural networks to predict soil water retention and soil hydraulic conductivity. Soil Tillage Res. 1998;47:37-42.
  17. Moazenzadeh R., Ghahraman B., Fathalian F., Khoshnoodiyazdi A. Effect of type and number of input variables on estimation of moisture retention curve and saturated hydraulic conductivity. Journal of Water and Soil. 2009;23(3):57-70.
آمار
تعداد مشاهده مقاله: 325
تعداد دریافت فایل اصل مقاله: 636


counter
سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب