A prototype model for continuous agriculture field monitoring and assessment

  • Authors

    • Ratnmala Bhimanpallewar
    • Manda Rama Narasingarao
    2018-03-18
    https://doi.org/10.14419/ijet.v7i2.7.10288
  • Suitability Level, Machine Learning, Iot, Agriculture Parameter, Sensor .

  • Indian farmers are totally dependent on agriculture and livestock for satisfying their basic food and economical needs. Maximum farmers are habitual to take crops continuously with traditional ways without checking the current suitability. Government of India has developed centers to train and provide the information to farmers but everyonedon't approach to it. To get the increased yield, usually farmers add fertilizers without understanding requirement which may leads to soil degradation.Proposed solution is an automated system which can monitor major parameters required to estimate suitability for cropping. This system can be made available locally to every farmer. Outcome of this monitoring system can be used to identify particular crop suitability, so that suitable crop can be adopted.

  • References

    1. [1] S. Wolfert, L. Ge, C. Verdouw, and M. J. Bogaardt, “Big Data in Smart Farming – A review,†Agric. Syst., vol. 153, pp. 69–80, 2017.

      [2] M. J. O’Grady and G. M. P. O’Hare, “Modelling the smart farm,†Inf. Process. Agric., vol. 4, no. 3, pp. 179–187, 2017.

      [3] “FAO_Chapter 6 - Causes of land degradation.†FAO, United Nation.

      [4] Agriculture University, “Krishi Darshani 2008,†Pune, 2008.

      [5] J. Liang, X. Liu, and K. Liao, “Soil Moisture Retrieval using UWB Echoes via Fuzzy Logic and Machine Learning,†IEEE Internet Things J., vol. PP, no. 99, p. 1, 2017.

      [6] R. Elsheikh, A. R. B. Mohamed Shariff, F. Amiri, N. B. Ahmad, S. K. Balasundram, and M. A. M. Soom, “Agriculture Land Suitability Evaluator (ALSE): A decision and planning support tool for tropical and subtropical crops,†Comput. Electron. Agric., vol. 93, pp. 98–110, Apr. 2013.

      [7] A. Benzekri, K. Meghriche, and L. Refoufi, “PC-Based Automation of a Multi-Mode Control for an Irrigation System,†in 2007 International Symposium on Industrial Embedded Systems, 2007, pp. 310–315.

      [8] M. R. M. Kassim, I. Mat, and A. N. Harun, “Wireless Sensor Network in precision agriculture application,†in 2014 International Conference on Computer, Information and Telecommunication Systems (CITS), 2014, pp. 1–5.

      [9] R. G. Regalado and J. C. Dela Cruz, “Soil pH and nutrient (Nitrogen, Phosphorus and Potassium) analyzer using colorimetry,†in 2016 IEEE Region 10 Conference (TENCON), 2016, pp. 2387–2391.

      [10] R. Bhimanpallewar, “A Machine Learning Approach to Assess Crop Specific Suitability for Small / Marginal Scale Croplands,†vol. 12, no. 23, pp. 13966–13973, 2017.

      [11] FAO (Food and Agriculture Organization of the UnitedNations), “2. Water and soil requirements,†2016. [Online]. Available: http://www.fao.org/docrep/u3160e/u3160e04.htm#TopOfPage.

      [12] Food and Agriculture Organization of the United Nations (FAO), “Approaches to Land Classification.†p. 123, 1974.

      [13] A. A. Soofi and A. Awan, “Classification Techniques in Machine Learning : Applications and Issues,†pp. 459–465, 2017.

  • Downloads

  • How to Cite

    Bhimanpallewar, R., & Rama Narasingarao, M. (2018). A prototype model for continuous agriculture field monitoring and assessment. International Journal of Engineering & Technology, 7(2.7), 179-182. https://doi.org/10.14419/ijet.v7i2.7.10288