Wheat crop yield prediction using new activation functions in neural network
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ORIGINAL ARTICLE
Wheat crop yield prediction using new activation functions in neural network Shital H. Bhojani1,2
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Nirav Bhatt3
Received: 12 July 2019 / Accepted: 17 February 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Multilayer perceptron Forecast Activation function Classification Yield Neural network Agriculture Abbreviations DM Data mining SMW Standard Meteorological Week
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.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00521-020-04797-8) contains supplementary material, which is available to authorized users. & Shital H. Bhojani [email protected] Nirav Bhatt [email protected] 1
RK University, Rajkot, Gujarat, India
2
AAU, Anand, Gujarat, India
3
MCA Department, RK University, Rajkot, Gujarat, India
WEKA MLP MAE MSE RMSE MAPE RAE RRSE BSS MAXT MINT RH1 RH2 VP1 VP2 AF DA
Waikato Environment for Knowledge Analysis Multilayer perceptron Mean absolute error Mean squared error Root mean squared error Mean absolute percentage error Relative absolute error Root relative squared error B
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