Advertisement

Modeling Earth Systems and Environment

, Volume 5, Issue 1, pp 13–20 | Cite as

Modelling soil compaction of agricultural soils using fuzzy logic approach and adaptive neuro-fuzzy inference system (ANFIS) approaches

  • Mohammadreza Abbaspour-Gilandeh
  • Yousef Abbaspour-GilandehEmail author
Original Article
  • 27 Downloads

Abstract

One of the most important physical properties of the soil is its mechanical strength. Increasing soil mechanical strength will lead to increments in the draft, fuel consumption, work duration and equipment wear, but will follow the reduction in the root growth. Awareness of the soil cone index (as a criterion of the arable soil compaction) in order to production management in connection with the soil physical properties have high importance, especially in precision farming. On the other hand, finding the methods and models that be able to create the best function or model to estimate the soil cone index at the least cost and use of available data are crucial for researchers. The aim of this study was predicting arable soils cone index values by effective parameters on the soil cone index, including bulk density, soil moisture content and soil electrical conductivity by using Fuzzy and neuro-fuzzy systems. In this study, for measurement and determination of the influencing factors on the soil cone index value, the experimental design was the factorial experiment based on a randomized complete block design with five replications. The experimental field had three types of loam, sandy loam and loamy sand soils. The modeling of soil cone index was performed by using effective parameters such as bulk density, moisture content and soil electrical conductivity in the fuzzy and adaptive neuro-fuzzy inference system (ANFIS) systems. For fuzzification of input and output parameters, the linguistic variables, including very low (VL), low (L), medium (M), high (H) and very high (VH) were used. Since determining the type and number of membership functions was conducted experimentally, the triangular membership function for both input and output variables was used due to the high accuracy and convenience in system design. In ANFIS model, 80% and 20% of total data were considered as training and test data, respectively. The numbers of membership functions were selected 5 for each input parameters. ANFIS training was done by the hybrid method. The average of coefficients of determination (R2) were obtained 80.1% and 97.9% for the fuzzy and ANFIS models, respectively. The obtained model through ANFIS presented the high accuracy of 2.54% rather than the fuzzy models with the accuracy of 9.68%. Also in comparison with regression models, ANFIS model has high accuracy and can be used to estimate the soil cone index in agricultural land.

Keywords

Soil mechanical resistance Moisture content Bulk density Electrical conductivity Membership function Prediction model 

Notes

Funding

Funding was provided by University of Mohaghegh Ardabili.

References

  1. Abbaspour-Gilandeh Y, Sedghi R (2015) Predicting soil fragmentation during tillage operation using fuzzy logic approach. J Terrramech 57(2015):61–69CrossRefGoogle Scholar
  2. Abbaspour-Gilandeh Y, Shaygani-Soltanpour AR (2014) Soil cone index prediction using artificial neural networks model and its comparison with regression models. J Soil Manag Sustain Prod 4(2):187–203 (in Persian) Google Scholar
  3. Abbaspour-Gilandeh Y, Alimardani R, Khalilian A, Keyhani AR, Sadati SH (2006) Energy requrement of site-specific and conventional tillage as affected by tractor speed and soil parameters. Int J Agric Biol 8(4):499–503Google Scholar
  4. Abbaspour-Gilandeh Y, Ahani M, Askari Asli-Ardeh E, Rasooli Sharabiani V, Sofalian O (2010) Design, construction and evaluation of a tractor-mounted soil cone penetrometer with multipleadjustable-probes. J Agric Eng Res 11(1):19–34 (in Persian) Google Scholar
  5. Abbaspour-Gilandeh Y, Khalilian A, Hasankhani F (2011) Use of soil EC data for zoning the production field by artificial neural network for applying the precision tillage. J Agric Mach Sci (Tarim Makinalari Bilimi Dergisi) 7(1):27–31Google Scholar
  6. Alimardani R, Solhjou AA (2004) Mathematical model to determine the cone index in a clay loam soil of ZARGHAN area of Fars. Iran Agric Sci J 10(2):135–144 (in Persian) Google Scholar
  7. Bayat H, Neyshabouri MR, Hajabbasi MA, Mahboubi AA, Mosaddeghi MR (2008) Comparing neural networks, linear and nonlinear regression techniques to model penetration resistance. Turk J Agric For 32 (2008):425–433Google Scholar
  8. Blouin VM, Schmidt MG, Bulmer CE, Krzic M (2008) Effects of compaction and water content on lodgepole pine seeding growth. For Ecol Manag 255:2444–2452CrossRefGoogle Scholar
  9. Busscher WJ (1990) Adjustment of flat-tipped penetrometer resistance data to common water content. Trans ASAE 33:519–524CrossRefGoogle Scholar
  10. Clark RL (1999) Evaluation of the potential to develop soil strength maps using a cone penetrometer. ASAE paper No. 99-3109, ASAE, St. Joseph, Mich 49085, USAGoogle Scholar
  11. Domzal H, Hodara J (1990) Soil compaction change in the layer 0.5 m depth caused by machine wheels. Zesz Probl Post NOUK ROLN 388:29–40Google Scholar
  12. Faure AG, Da Mata JDV (1994) Penetration resistance value along compaction curves. J Geotech Eng 120:46–59CrossRefGoogle Scholar
  13. Henderson C, Levett A, Lisle D (1988) The effect of soil water content and bulk density on the compactability and soil penetration resistance of some Western Australian sandy soils. Aust J Soil Res 26:391–400CrossRefGoogle Scholar
  14. Lapen DR, Topp GC, Edwards ME, Gregorich EG, Curnoe WE (2004) Combination cone penetration resistance/water content instrumentation to evaluate cone penetration–water content relationships in tillage research. Soil Till Res 62:27–40Google Scholar
  15. Pidgeon JD, Soane BD (1977) Effects of tillage and direct drilling on soil properties during the growing season in a long-term barley mono-culture system. J Agric Sci 88:431–442CrossRefGoogle Scholar
  16. Sabzi S, Abbaspour-Gilandeh Y, Javadikia H, Havaskhan H (2015) Automatic grading of Emperor apples based on image processing and ANFIS. Tarım Bilimleri Dergisi J Agric Sci 21(3):326–336CrossRefGoogle Scholar
  17. Santos FL, Mandes de Jesus VA, Valente DSM (2012) Modeling of soil penetration resistance using statistical analyses and artificial neural networks. Acta Sci Agron 34(2):219–224Google Scholar
  18. To J, Kay BD (2005) Variation in penetrometer resistance with soil properties: the contribution of effectivestress and implications for pedotransfer functions. Geoderma 126:261–276CrossRefGoogle Scholar
  19. Upadhyaya SK, Kemble LJ, Collins NE (1982) Cone index prediction equations for Delaware soils. Trans ASAE 82:1452–1456Google Scholar
  20. Voorhees ML, Walker PN (1977) Tractionability to a function of soil moisture. Trans ASAE 20(5):806–809CrossRefGoogle Scholar
  21. Wang LX (1997) A course in fuzzy system and control. Prentice-Hall, Upper Saddle RiverGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Biosystems Engineering, College of Agriculture and Natural ResourcesUniversity of Mohaghegh ArdabiliArdabilIran

Personalised recommendations