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Current IssueEditorial BoardOnline First ArticlesArchive

Research Article

volume 53 issue 1 (february 2019) : 78-82,   Doi: 10.18805/IJARe.A-5053

Machine learning model for automation of soil texture classification

K
K. Radhika
D
D. Madhavi Latha
1Department of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderbad-500 075, Telangna, India.
Cite article:- Radhika K., Latha Madhavi D. (2019). Machine learning model for automation of soil texture classification. Indian Journal of Agricultural Research. 53(1): 78-82. doi: 10.18805/IJARe.A-5053.

ABSTRACT

Soil formation is a long term process and diverse soils are formed in different localities due to various soil forming factors over the landscape. Soil classification plays critical role in various aspects of agricultural engineering. Physico-chemical parameters play an important role in soil classification. In this paper, we present a comprehensive classification model for soil texture classification by using Linear Discriminant Analysis (LDA). We took the Physico-chemical properties of the soil, which include soil moisture, temperature, electrical conductivity, pH, organic carbon, available nitrogen, available phosphorus and potassium as independent variables, while the soil type was taken as the dependent variable. Feature selection is employed using Boruta algorithm. The performance of the proposed classification model is evaluated and expressed in terms of overall accuracy and kappa coefficient. Results show that the average prediction accuracy and kappa coefficient of the proposed model are 96.3% and 0.944 respectively, indicating that the model can be used effectively for soil classification for a set of suitable dependent variables.

REFERENCES

  1. Behrens T, Förster H, Scholten T, Steinrücken U, Spies ED, Goldschmitt M. (2005). Digital soil mapping using artificial neural networks. Journal of Plant nutrition and Soil Science, 168: 21-33.
  2. Bhaskar B.P., S.V. Bobade, S.S. Gaikwad, Dipak Sarkar, S.G. Anantwar and Tapas Bhattacharyya (2015) Soil informatics for agricultural land suitability assessment in Seoni district, Madhya Pradesh, India. Indian Journal of Agricultural Research. 49: 315-320. 
  3. Dickson A.A., E.A. Allison-Oguru and N.O. lsirimah (2002). Fertility capability classification based land evaluation in relation to socio-economic conditions of small holder farmers in bayelsa state of nigeria, Indian Journal of Agricultural Research. 36: 10-16.
  4. El-Gamal, A.D. (1995). Systematical studies on the algae isolated from some cultivated areas and laboratory studies on the effect of light, temperature and humidity on three selected soil algae. Ph.D. Thesis, Fac. Of Sci., Al-Azhar University, Cairo, Egypt.
  5. Grinand C, Arrouays D, Laroche B, Martin MP (2008). Extrapolating regional soil landscapes from an existing soil map: sampling intensity, validation procedures, and integration of spatial context. Geoderma. 143:80-90.
  6. Hengl T, Toomanian N, Reuter HI, Malakouti MJ. (2007). Methods to interpolate soil categorical variables from profile observations: Lessons from Iran”, Geoderma, 140: 417-427.
  7. Jackson, M.L. (1977) Soil Chemical Analysis, Pentice Hall of India. Private Limited-New Delhi.
  8. Jafari A, Khademi H, Finke PA, Van de Wauw J, Ayoubi S. (2014). Spatial prediction of soil great groups by boosted regression trees using a limited point dataset in an arid region, southeastern Iran. Geoderma, 232:148-163.
  9. Massawe BH, Subburayalu SK, Kaaya AK, Winowiecki L, Slater BK (2018). Mapping numerically classified soil taxa in Kilombero Valley, Tanzania using machine learning, Geoderma, 311: 143-148.
  10. Muhr GR.et al. (1965) Soil Testing in India, USAID, New Delhi.pp.120.
  11. Olsen SR et al. (1954). Estimation of available phosphorus in soils by extraction with sodium bicarbonate. U.S. Dep. Agric.Washington, D.C. Circ.939.
  12. Rad MR, Toomanian N, Khormali F, Brungard CW, Komaki CB, Bogaert P. (2014). Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran. Geoderma. 232: 97-106.
  13. Ram R.L., P.K. Sharma and N. Ahmed (2013). Characterization and fertility assessment of soils of Markapur Mandal, Prakasam district, Andhra Pradesh for sustainable land use planning” Indian Journal of Agricultural Research, vol. Issue 2 127-133.
  14. Savalia S.G. and Gundalia J.D. (2010). Characterization and evaluation of soil-site suitability for groundnut in the soils of Uben Irrigation Command Area of Saurashtra region in Gujarat, Legume Res., 33 (2): 79 – 86.
  15. Subbaiah, B.V. and Asija, G.L. (1956). A rapid procedure for the estimation of available nitrogen in soils. Current Sci., 25: 259-260.
  16. Vågen TG, Winowiecki LA, Tondoh JE, Desta LT, Gumbricht T. (2016). Mapping of soil properties and land degradation risk in Africa using MODIS reflectance, Geoderma, 263: 216-225.
  17. Walkely, A and Black, I.A., (1934), An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method, Soil Science, 37:29-38. 
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Indian Journal of Agricultural Research
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    Research Article

    volume 53 issue 1 (february 2019) : 78-82,   Doi: 10.18805/IJARe.A-5053

    Machine learning model for automation of soil texture classification

    K
    K. Radhika
    D
    D. Madhavi Latha
    1Department of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderbad-500 075, Telangna, India.
    Cite article:- Radhika K., Latha Madhavi D. (2019). Machine learning model for automation of soil texture classification. Indian Journal of Agricultural Research. 53(1): 78-82. doi: 10.18805/IJARe.A-5053.

    ABSTRACT

    Soil formation is a long term process and diverse soils are formed in different localities due to various soil forming factors over the landscape. Soil classification plays critical role in various aspects of agricultural engineering. Physico-chemical parameters play an important role in soil classification. In this paper, we present a comprehensive classification model for soil texture classification by using Linear Discriminant Analysis (LDA). We took the Physico-chemical properties of the soil, which include soil moisture, temperature, electrical conductivity, pH, organic carbon, available nitrogen, available phosphorus and potassium as independent variables, while the soil type was taken as the dependent variable. Feature selection is employed using Boruta algorithm. The performance of the proposed classification model is evaluated and expressed in terms of overall accuracy and kappa coefficient. Results show that the average prediction accuracy and kappa coefficient of the proposed model are 96.3% and 0.944 respectively, indicating that the model can be used effectively for soil classification for a set of suitable dependent variables.

    REFERENCES

    1. Behrens T, Förster H, Scholten T, Steinrücken U, Spies ED, Goldschmitt M. (2005). Digital soil mapping using artificial neural networks. Journal of Plant nutrition and Soil Science, 168: 21-33.
    2. Bhaskar B.P., S.V. Bobade, S.S. Gaikwad, Dipak Sarkar, S.G. Anantwar and Tapas Bhattacharyya (2015) Soil informatics for agricultural land suitability assessment in Seoni district, Madhya Pradesh, India. Indian Journal of Agricultural Research. 49: 315-320. 
    3. Dickson A.A., E.A. Allison-Oguru and N.O. lsirimah (2002). Fertility capability classification based land evaluation in relation to socio-economic conditions of small holder farmers in bayelsa state of nigeria, Indian Journal of Agricultural Research. 36: 10-16.
    4. El-Gamal, A.D. (1995). Systematical studies on the algae isolated from some cultivated areas and laboratory studies on the effect of light, temperature and humidity on three selected soil algae. Ph.D. Thesis, Fac. Of Sci., Al-Azhar University, Cairo, Egypt.
    5. Grinand C, Arrouays D, Laroche B, Martin MP (2008). Extrapolating regional soil landscapes from an existing soil map: sampling intensity, validation procedures, and integration of spatial context. Geoderma. 143:80-90.
    6. Hengl T, Toomanian N, Reuter HI, Malakouti MJ. (2007). Methods to interpolate soil categorical variables from profile observations: Lessons from Iran”, Geoderma, 140: 417-427.
    7. Jackson, M.L. (1977) Soil Chemical Analysis, Pentice Hall of India. Private Limited-New Delhi.
    8. Jafari A, Khademi H, Finke PA, Van de Wauw J, Ayoubi S. (2014). Spatial prediction of soil great groups by boosted regression trees using a limited point dataset in an arid region, southeastern Iran. Geoderma, 232:148-163.
    9. Massawe BH, Subburayalu SK, Kaaya AK, Winowiecki L, Slater BK (2018). Mapping numerically classified soil taxa in Kilombero Valley, Tanzania using machine learning, Geoderma, 311: 143-148.
    10. Muhr GR.et al. (1965) Soil Testing in India, USAID, New Delhi.pp.120.
    11. Olsen SR et al. (1954). Estimation of available phosphorus in soils by extraction with sodium bicarbonate. U.S. Dep. Agric.Washington, D.C. Circ.939.
    12. Rad MR, Toomanian N, Khormali F, Brungard CW, Komaki CB, Bogaert P. (2014). Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran. Geoderma. 232: 97-106.
    13. Ram R.L., P.K. Sharma and N. Ahmed (2013). Characterization and fertility assessment of soils of Markapur Mandal, Prakasam district, Andhra Pradesh for sustainable land use planning” Indian Journal of Agricultural Research, vol. Issue 2 127-133.
    14. Savalia S.G. and Gundalia J.D. (2010). Characterization and evaluation of soil-site suitability for groundnut in the soils of Uben Irrigation Command Area of Saurashtra region in Gujarat, Legume Res., 33 (2): 79 – 86.
    15. Subbaiah, B.V. and Asija, G.L. (1956). A rapid procedure for the estimation of available nitrogen in soils. Current Sci., 25: 259-260.
    16. Vågen TG, Winowiecki LA, Tondoh JE, Desta LT, Gumbricht T. (2016). Mapping of soil properties and land degradation risk in Africa using MODIS reflectance, Geoderma, 263: 216-225.
    17. Walkely, A and Black, I.A., (1934), An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method, Soil Science, 37:29-38. 
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