Neural Method for Site-Specific Yield Prediction

  • Pramod Kumar Meena
  • Mahesh Kumar Hardaha
  • Deepak Khare
  • Arun Mondal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 259)


In the recent years, a variety of mathematical models relating to crop yield have been proposed. A study on Neural Method for Site –Specific Yield Prediction was undertaken for Jabalpur district using Artificial Neural Networks (ANN). The input dataset for crop yield modeling includes weekly rainfall, maximum and minimum temperature and relative humidity (morning, evening) from 1969 to 2010. ANN models were developed in Neural Network Module of MATLAB (7.6 versions, 2008). Model performance has been evaluated in terms of MSE, RMSE and MAE. The basic ANN architecture was optimized in terms of training algorithm, number of neurons in the hidden layer, input variables for training of the model. Twelve algorithms for training the neural network have been evaluated. Performance of the model was evaluated with number of neurons varied from 1 to 25 in the hidden layer. A good correlation was observed between predicted and observed yield (r = 0.898 and 0.648).


Artificial neural network Crop yield Site specific 


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Copyright information

© Springer India 2014

Authors and Affiliations

  • Pramod Kumar Meena
    • 1
  • Mahesh Kumar Hardaha
    • 2
  • Deepak Khare
    • 1
  • Arun Mondal
    • 1
  1. 1.Department of Water Resources and ManagementIITRoorkeeIndia
  2. 2.Department of Soil and Water EngineeringJNKVVJabalpurIndia

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