Abstract
This paper discuss and compares the existing neural network techniques that can be successfully applied in forecasting wheat production. The study carried out helped in determining the most efficient neural network technique suitable for wheat production forecast. The dataset was analyzed by considering the effects of various factors such as temperature, rain etc. on wheat yield. The methodologies were applied in a mandate to foretell wheat production, using artificial neural networks over 95 years of agricultural dataset.
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Abbreviations
- ANN:
-
Artificial neural network
- NN:
-
Neural network
- BP:
-
Backpropagation
- MSE:
-
Mean square error
- MLP:
-
Multilayer perceptron
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Garg, B., Kirar, N., Menon, S. et al. A performance comparison of different back propagation neural networks methods for forecasting wheat production. CSIT 4, 305–311 (2016). https://doi.org/10.1007/s40012-016-0096-x
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DOI: https://doi.org/10.1007/s40012-016-0096-x