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An artificial neural network model for estimating Mentha crop biomass yield using Landsat 8 OLI

  • Mohammad Saleem Khan
  • Manoj SemwalEmail author
  • Ashok Sharma
  • Rajesh Kumar Verma
Article
  • 11 Downloads

Abstract

Yield forecasting is essential for management of the food and agriculture economic growth of a country. Artificial Neural Network (ANN) based models have been used widely to make precise and realistic forecasts, especially for the nonlinear and complicated problems like crop yield prediction, biomass change detection and crop evapo-transpiration examination. In the present study, various parameters viz. spectral bands of Landsat 8 OLI (Operational Land Imager) satellite data and derived spectral indices along with field inventory data were evaluated for Mentha crop biomass estimation using ANN technique of Multilayer Perceptron. The estimated biomass showed a good relationship (R2 = 0.762 and root mean square error (RMSE) = 2.74 t/ha) with field-measured biomass.

Keywords

Aromatic crops Mentha Neural network Crop modelling Spectral indices Yield estimation 

Notes

Acknowledgements

The present work was carried out as a part of Council of Scientific and Industrial Research Network Project (BSC 0203). The authors wish to acknowledge Director CSIR-Central Institute of Medicinal and Aromatic Plants for providing support to carry out the research work. Authors are also thankful to the anonymous reviewers for their careful reading of the manuscript and insightful suggestions.

References

  1. Bairagi, G. D., & Hassan, Z. U. (2002). Wheat crop production estimation using satellite data. Journal of the Indian Society of Remote Sensing, 30(4), 213.CrossRefGoogle Scholar
  2. Bannari, A., Asalhi, H. & Teillet, P. M. (2002). Transformed difference vegetation index (TDVI) for vegetation cover mapping. In Geoscience and remote sensing symposium, 2002. IGARSS’02. 2002 IEEE International, 5, (pp. 3053–3055). IEEE.Google Scholar
  3. Bannari, A., Morin, D., Bonn, F., & Huete, A. R. (1995). A review of vegetation indices. Remote sensing reviews, 13(1–2), 95–120.CrossRefGoogle Scholar
  4. Bolton, D. K., & Friedl, M. A. (2013). Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology, 173, 74–84.CrossRefGoogle Scholar
  5. Bredemeier, C., Variani, C., Almeida, D., & Rosa, A. T. (2013). Estimation of productive potential in wheat using active optical sensor for variable rate nitrogen fertilization. Rural Science, 43(7), 1147–1154.CrossRefGoogle Scholar
  6. Chivasa, W., Mutanga, O., & Biradar, C. (2017). Application of remote sensing in estimating maize grain yield in heterogeneous African agricultural landscapes: a review. International Journal of Remote Sensing, 38(23), 6816–6845.CrossRefGoogle Scholar
  7. Eddy, P. R., Smith, A. M., Hill, B. D., Peddle, D. R., Coburn, C. A., & Blackshaw, R. E. (2008). Hybrid segmentation: artificial neural network classification of high resolution hyperspectral imagery for site-specific herbicide management in agriculture. Photogrammetric Engineering and Remote Sensing, 74, 1249–1257.CrossRefGoogle Scholar
  8. Eitel, J. U. H., Long, D. S., Gessler, P. E., & Hunt, E. R. (2008). Combined spectral index to improve ground-based estimates of nitrogen status in dryland wheat. Agronomy Journal, 100(6), 1694–1702.CrossRefGoogle Scholar
  9. Huete, A. R. (1988). A soil adjusted vegetation index (SAVI). Remote Sensing Environment, 25, 295–309.CrossRefGoogle Scholar
  10. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213.CrossRefGoogle Scholar
  11. Jackson, R. D., & Huete, A. R. (1991). Interpreting vegetation indices. Preventive veterinary medicine, 11(3–4), 185–200.CrossRefGoogle Scholar
  12. Jain, M., Mondal, P., DeFries, R. S., Small, C., & Galford, G. L. (2013). Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors. Remote Sensing Environment, 134, 210–223.CrossRefGoogle Scholar
  13. Jiang, D., Yang, X., Clinton, N., & Wang, N. (2004). An artificial neural network model for estimating crop yields using remotely sensed information. International Journal of Remote Sensing, 25, 1723–1732.CrossRefGoogle Scholar
  14. Jordan, C. F. (1969). Derivation of leaf area index from quality of light on the forest floor. Ecology, 50, 663–666.CrossRefGoogle Scholar
  15. Kauth, R. J. & Thomas, G. S. (1976). The tasselled cap: A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. In LARS Symposia, (p. 159).Google Scholar
  16. Khan, M. S., Semwal, M., Verma, R. K., & Sharma, A. (2017). Menthol mint crop yield estimation using Landsat 8 Satellite data in Barabanki District, Uttar Pradesh. In Proceedings of National Seminar on Healthy Soil for Healthy Life, 5 December 2017, Lucknow Chapter ISSS, pp. 30–36.Google Scholar
  17. Khanuja, S. P. S., Kalra, A., & Singh, A. K. (2005). Medicinal and aromatic plants: Gain from entrepreneurship. Hindu Survey of Indian Agriculture, 191–194.Google Scholar
  18. Kumar, S., Bansal, R. P., Yadav, R. P., Singh, A. K., & Khanuja, S. P. S. (2008b). Aroma economics towards rural development: A case study of geranium in Uttarkhand hills. Journal of Rural Technology, 3(6).Google Scholar
  19. Kumar, S., Srivastava, R. K., Singh, A. K., Kalra, A., Tomar, V. K. S., & Bansal, R. P. (2001). Higher yields and profits from new crop rotations permitting integration of mediculture with agriculture in the Indo-Gangetic plains. Current Science, 80, 563–566.Google Scholar
  20. Kumar, S., Suresh, R., Singh, V., & Singh, A. K. (2011). Economic analysis of menthol mint cultivation in Uttar Pradesh: A case study of Barabanki district. Agricultural Economics Research Review, 24, 345–350.Google Scholar
  21. Kumar, S., Yadav, R. P., & Singh, A. K. (2008a). Potential and business opportunities in essential oil sector. Journal of Medicinal and Aromatic Plant Sciences, 30, 336–339.Google Scholar
  22. Lamba, V., & Dhaka, V. S. (2014). Wheat yield prediction using artificial neural network and crop prediction techniques. International Journal for Research in Applied Science and Engineering Technology, 2(4), 330–341.Google Scholar
  23. Padalia, R. C., Verma, R. S., Chauhan, A., Sundaresan, V., & Chanotiya, C. S. (2013). Essential oil composition of sixteen elite cultivars of Mentha from western Himalayan region, India. Maejo International Journal of Science and Technology, 7, 83–93.Google Scholar
  24. Pandey, L., & Reddy, A. A. (2012). Farm productivity and rural poverty in Uttar Pradesh: A regional perspective. Agricultural Economics Research Review, 25(1), 25.Google Scholar
  25. Paswan, R. P., & Begum, S. A. (2013). Regression and neural networks models for prediction of crop production 1. International Journal of Scientific & Engineering Research, 4(9), 98–108.Google Scholar
  26. Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119–126.CrossRefGoogle Scholar
  27. Ram, M., & Kumar, S. (1997). Yield improvement in the regenerated and transplanted mint Mentha arvensis by recycling the organic wastes and manures. Bioresource Technology, 59, 141–149.CrossRefGoogle Scholar
  28. Ram, M., & Kumar, S. (1998). Yield and resource use optimization in late transplanted mint (Mentha arvensis) under subtropical conditions. Journal of Agronomy and Crop Science, 180, 109–112.CrossRefGoogle Scholar
  29. Rao, P. K., Rao, V. V., & Venkataratnam, L. (2002). Remote sensing: A technology for assessment of sugarcane crop acreage and yield. Sugar Tech, 4(3–4), 97–101.CrossRefGoogle Scholar
  30. Richardson, A. J., & Wiegand, C. (1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering & Remote Sensing, 43, 1541–1552.Google Scholar
  31. Singh, A. K., & Khanuja, S. P. S. (2007). CIMAP initiatives for menthol mint. Spice India, 20, 14–17.Google Scholar
  32. Singh, V. P., Singh, M., & Singh, D. V. (1998). Growth, yield and quality of peppermint (Mentha x piperita L.) as influenced by planting time. Journal of Herbs, Spices & Medicinal Plants, 5, 33–39.CrossRefGoogle Scholar
  33. Singh, M., Singh, A., Singh, S., & Ram, M. (2011). Evaluation of alternate menthol mint (Mentha arvensis L.) based intensive cropping systems for Indo-Gangetic plains of north India. Archives of Agronomy and Soil Science, 58, 411–421.CrossRefGoogle Scholar
  34. Srivastava, R. (2013). The mint that grows profits for farmers! The Hindustan Times, 29 April 2013. Retrieved March, 2018 from https://www.hindustantimes.com/lucknow/the-mint-that-grows-profits-for-farmers/story-aeaOnQL9NAHjwJb18gJbLM.html.
  35. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150.CrossRefGoogle Scholar
  36. Vimal, S. (2014). Profitability of mentha oil futures for farmers. International Journal of Management, MIT College of Management, 2, 44–48.Google Scholar
  37. Welikhe, P., Quansah, J. E., Fall, S., & Elhenney, W Mc. (2017). Estimation of soil moisture percentage using LANDSAT-based moisture stress index. Journal of Remote Sensing and GIS, 6, 200.CrossRefGoogle Scholar
  38. Zhang, W. J., Bai, C. J., & Liu, G. D. (2007). Neural network modelling of ecosystems: A case study on cabbage growth system. Ecological Modelling, 201, 317–325.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Mohammad Saleem Khan
    • 1
  • Manoj Semwal
    • 1
    • 4
    Email author
  • Ashok Sharma
    • 2
    • 4
  • Rajesh Kumar Verma
    • 3
    • 4
  1. 1.Information and Communication Technology DepartmentCSIR-Central Institute of Medicinal and Aromatic PlantsLucknowIndia
  2. 2.Plant Biotechnology DepartmentCSIR-Central Institute of Medicinal and Aromatic PlantsLucknowIndia
  3. 3.Agronomy and Soil Science DepartmentCSIR-Central Institute of Medicinal and Aromatic PlantsLucknowIndia
  4. 4.Academy of Scientific and Innovative Research (AcSIR)GhaziabadIndia

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