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Wheat crop yield prediction using new activation functions in neural network

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Abstract

This research mainly based on multilayer perceptron (MLP) neural networks technique of data mining to forecast the wheat crop yield at the district level. There are many statistical and simulation models available, but the proposed algorithm with new activation function provides promising results in a shorter time with more accuracy. Sigmoid and hyperbolic tangent activation functions are widely used in the neural network. The activation functions play an important role in the neural network learning algorithm. The main objective of the proposed work is to develop an amended MLP neural network with new activation function and revised random weights and bias values for crop yield estimation by using the different weather parameter datasets. MLP model has been tested by existing activation functions and newly created activation functions with different cases including weights and bias values. In this research study, we evaluate the result of different activation functions and recommend some new simple activation functions, named DharaSig, DharaSigm and SHBSig, to improve the performance of neural networks and accurate results. Also, three new activation functions created with little variations in the DharaSig function named DharaSig1, DharaSig2 and DharaSig3. In this research study, variable numbers of hidden layers are tested with the variable number of neurons per hidden layer for the agriculture dataset. Variable values of momentum, seed and learning rate are also used in this study. Experiments show that newly created activation functions provide better results compared to ‘sigmoid’ default neural network activation function for agriculture datasets.

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Abbreviations

DM:

Data mining

SMW:

Standard Meteorological Week

WEKA:

Waikato Environment for Knowledge Analysis

MLP:

Multilayer perceptron

MAE:

Mean absolute error

MSE:

Mean squared error

RMSE:

Root mean squared error

MAPE:

Mean absolute percentage error

RAE:

Relative absolute error

RRSE:

Root relative squared error

BSS:

Basic sun shine hours

MAXT:

Maximum temperature

MINT:

Minimum temperature

RH1:

Morning relative humidity (%)

RH2:

Afternoon relative humidity (%)

VP1:

Morning vapour pressure

VP2:

Afternoon vapour pressure

AF:

Activation function

DA:

Direction accuracy

References

  1. Aditya Shastry K, Sanjay HA, Deshmukh A (2016) A parameter based customized artificial neural network model for crop yield prediction. J Artif Intell 9(1–3):23–32. https://doi.org/10.3923/jai.2016.23.32

    Article  Google Scholar 

  2. Bhojani SH, Bhatt N (2018) Wheat crop yield forecasting using WEKA time series data mining technique. Int J Sci Res Comput Sci Appl Manag Stud 7(5):2–3

    Google Scholar 

  3. Muradkhanli L (2018) Neural networks for prediction of oil production. ELSEVIER IFAC Papers OnLine 51(30):415–417

    Article  Google Scholar 

  4. Stastny J, Konecny V, Trenz O (2011) Agricultural data prediction by means of neural network. Agric Econ Czech 57(7):356–361

    Article  Google Scholar 

  5. Paswan RP, Begum SA, Neog B (2018) Comparison of abilities of different activation functions of artificial neural network to predict crop area and crop production. Int J Pure Appl Biosci 6(6):212–220. https://doi.org/10.18782/2320-7051.7145

    Article  Google Scholar 

  6. da Gomes GSS, Ludermir TB, Lima LMMR (2011) Comparison of new activation functions in neural network for forecasting financial time series. Neural Comput Appl 20:417–439. https://doi.org/10.1007/s00521-010-0407-3

    Article  Google Scholar 

  7. Dudek G (2019) Generating random weights and biases in feedforward neural networks with random hidden nodes. Information sciences. arXiv:1908.05864 [cs.LG], pp 33–56

  8. Dudek G (2017) A method of generating random weights and biases in feedforward neural networks with random hidden nodes. arXiv:1710.04874 [cs.NE]

  9. Nwankpa C, Ijomah W, Gachagan A, Marshall S (2018) Activation functions: comparison of trends in practice and research for deep learning. arXiv:1811.03378 [cs.LG]

  10. Usha TM, Appavu Alias Balamurugan S (2016) Seasonal based electricity demand forecasting using time series analysis. Circuits Syst 7:3320–3328

    Article  Google Scholar 

  11. Niedbała G, Kozłowski R (2019) Application of artificial neural networks for multi-criteria yield prediction of winter wheat. J Agric Sci Technol 21:51–61

    Google Scholar 

  12. Ganatra A, Kosta YP, Panchal G, Gajjar C (2011) Initial classification through back propagation in a neural network following optimization through GA to evaluate the fitness of an algorithm. Int J Comput Sci Inf Technol 3(1):98–116

    Google Scholar 

  13. Bhojani SH, Bhatt N (2018) Application of data mining technique for wheat crop yield forecasting for districts of Gujarat state. Int J Sci Res Public 8(7):302–306

    Google Scholar 

  14. Garg B, Aggarwal S, Sokhal J (2018) Crop yield forecasting using fuzzy logic and regression model. Elsevier Comput Electr Eng 67:383–403

    Article  Google Scholar 

  15. Jayaram MA, Marad N (2012) Fuzzy inference systems for crop yield prediction. J. Intell. Syst. 21:363–372. https://doi.org/10.1515/jisys-2012-0016

    Article  Google Scholar 

  16. Pabuccu H (2017) Time series forecasting with neural network and fuzzy logic. IASSR E-BWN Publication

  17. Menaka K, Yuvaraj N (2016) A survey on crop yield prediction models. Indian J Innov Dev 5(12)

Download references

Acknowledgements

I am thankful to the Directorate of Agriculture, Gandhinagar, and Agro-meteorology Department, Anand Agricultural University, Anand, Gujarat, for providing the datasets for my research work.

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Authors and Affiliations

  1. RK University, Rajkot, Gujarat, India

    Shital H. Bhojani

  2. AAU, Anand, Gujarat, India

    Shital H. Bhojani

  3. MCA Department, RK University, Rajkot, Gujarat, India

    Nirav Bhatt

Authors
  1. Shital H. Bhojani
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  2. Nirav Bhatt
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Correspondence to Shital H. Bhojani.

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Shital H. Bhojani is a Research Scholar, RK University, Rajkot, Gujarat, India and Assistant Professor, AAU, Anand, Gujarat, India; Nirav Bhatt is a Associate Professor, MCA Department, RK University, Rajkot, Gujarat, India.

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Bhojani, S.H., Bhatt, N. Wheat crop yield prediction using new activation functions in neural network. Neural Comput & Applic 32, 13941–13951 (2020). https://doi.org/10.1007/s00521-020-04797-8

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