Weather based crop yield prediction using artificial neural networks : A comparative study with other approaches

Authors

  • AkhileshKumar Gupta Institute of Agriculture, Visva-Bharati
  • KaderAli Sarkar Department of Agricultural Statistics, Institute of Agriculture, Visva-Bharati, Santiniketan, West Bengal, India
  • DigvijaySingh Dhakre Department of Agricultural Statistics, Institute of Agriculture, Visva-Bharati, Santiniketan, West Bengal, India
  • Debasis Bhattacharya Department of Agricultural Statistics, Institute of Agriculture, Visva-Bharati, Santiniketan, West Bengal, India

DOI:

https://doi.org/10.54302/mausam.v74i3.174

Keywords:

Weather indices, crop yield prediction, regression, artificial neural network, prediction error percentage

Abstract

This paper attempts to compare the weather indices based regression approach and Multilayer Perceptron (MLP) Artificial Neural Network (ANN) approach for rice yield prediction at district level of West Bengal. The weather indices for weather variables, viz., minimum temperature, maximum temperature, rainfall, and relative humidity are used as input variables along with time variable t and yield of rice as output variable. The study reveals that the ANN approach works better than the standard regression approach in crop yield prediction. The prediction error percentages are found to be consistently less than 5% in MLP ANN approach except for one district.

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Published

02-01-2024

How to Cite

[1]
A. Gupta, K. Sarkar, D. Dhakre, and D. Bhattacharya, “Weather based crop yield prediction using artificial neural networks : A comparative study with other approaches”, MAUSAM, vol. 74, no. 3, pp. 825–832, Jan. 2024.

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Section

Research Papers