Implementation of Effective Crop Selection by Using the Random Forest Algorithm

  • Authors

    • R Karthikeyan
    • M Gowthami
    • A Abhishhek
    • P Karthikeyan
    2018-09-01
    https://doi.org/10.14419/ijet.v7i3.34.19209
  • Crop production, Machine learning, Random Forests, Weather Conditions

  • Accurate predictions of crop yield are critical for developing agriculture. We have provided a machine-learning method, Random Forests which has a ability to predict crop yield corresponds to the current climate and biophysical change. We have collected a large crop yield data from various sources. These data are used for both for the model training and testing.RF was found huge capable of predicting crop yields and over performed MLR standards in every performance statistics that were compared. From various results that shows that RF is an efficient machine-learning algorithm for crop predictions at current condition and has a huge accuracy in data analysis.

     

     

  • References

    1. [1] Y. Xiaoxia, Z.Chengming,‘A soil moisture prediction algorithm base on improved BP’, 5th International Conference on Agro-geoinformatics (Agro-geoinformatics),2016.

      [2] R.V.Chandrashekar,’Development or end of Agriculture? Implications on Agriculture’ , Indian Journal of Innovations and Developments, 2012,1(8), 653-658.

      [3] C.Notarnicola,’ A bayesian change detection approach for retrieval of soil moisture variations under different roughness conditions’, IEEE Geoscience and Remote Sensing Letters, 2014, 11(2), 414-418

      [4] Rakesh Kumar1, M.P. Singh2, Prabhat Kumar3 and J.P. Singh4,’Crop Selection Method to Maximize Crop Yield Rate using Machine Learning Technique’, 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, T.N., India. 6 - 8 May 2015,pp.138-145.

      [5] Evaluation of random forest method for agricultural cropclassification, Asli Ozdarici Ok, Ozlem Akar & Oguz Gungor European Journal of Remote Sensing , 439-450.

      [6] S. M. Z.Younis, J.Iqbal,’Estimation of soil moisture using multispectral and FTIR techniques’, The Egyptian Journal of Remote Sensing and Space Science, 2015,18(2), 151-161.

      [7] J. Dong, S.C. Steele-Dunne, T. E. Ochsner, N.van de Giesen,’Determining soil moisture by assimilating soil temperature measurements using the Ensemble Kalman Filter’, Advances in Water Resources, 2015, 86, 340-353.

      [8] Balenzano, F. Mattia,G. Satalino, M. W. Davidson,’ Dense temporal series of C-and L-band SAR data for soil moisture retrieval over agricultural crops’,IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011,4(2)

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  • How to Cite

    Karthikeyan, R., Gowthami, M., Abhishhek, A., & Karthikeyan, P. (2018). Implementation of Effective Crop Selection by Using the Random Forest Algorithm. International Journal of Engineering & Technology, 7(3.34), 287-290. https://doi.org/10.14419/ijet.v7i3.34.19209