Extreme learning machines for weather-based modelling of silk cocoon production

Authors

  • PRAMIT PANDIT Department of Agricultural Statistics, Applied Mathematics and Computer Science, University of Agricultural Sciences, Bengaluru-560065, Karnataka, India
  • BISHVAJIT BAKSHI Department of Agricultural Statistics, Applied Mathematics and Computer Science, University of Agricultural Sciences, Bengaluru, Karnataka, India
  • SHILPA M. Department of Agricultural Statistics, Applied Mathematics and Computer Science, University of Agricultural Sciences, Bengaluru-560065, Karnataka, India

DOI:

https://doi.org/10.54386/jam.v24i1.946

Keywords:

BP-ANN, cocoon production, ELM, principal component regression, weather-based modelling, silk

Abstract

In spite of the immense popularity and sheer power of the neural network models, their application in sericulture is still very much limited. With this backdrop, this study evaluates the suitability of neural network models in comparison with the linear regression models in predicting silk cocoon production of the selected six districts (Kolar, Chikballapur, Ramanagara, Chamarajanagar, Mandya and Mysuru) of Karnataka by utilising weather variables for ten consecutive years (2009-2018). As the weather variables are found to be correlated, principal components are obtained and fed into the linear (principal component regression) and non-linear models (back propagation-artificial neural network and extreme learning machine) as inputs. Outcomes emanated from this experiment have revealed the clear advantages of employing extreme learning machines (ELMs) for weather-based modelling of silk cocoon production. Application of ELM would be particularly useful, when the relation between production and its attributing characters is complex and non-linear.

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Published

11-02-2022

How to Cite

PANDIT, P., BAKSHI, B. ., & M., S. . (2022). Extreme learning machines for weather-based modelling of silk cocoon production. Journal of Agrometeorology, 24(1), 50–54. https://doi.org/10.54386/jam.v24i1.946

Issue

Vol. 24 No. 1 (2022): March

Section

Research Paper

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Copyright (c) 2022 PRAMIT PANDIT, BISHVAJIT BAKSHI , SHILPA M.

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