Mulberry Leaf Yield Prediction Using Machine Learning Techniques

DOI

10.2991/ahis.k.210913.048How to use a DOI?

Keywords

Machine Learning models, Mulberry, Mulberry leaf yield, Multiple Linear Regression, Random forest Regression, Ridge Regression

Abstract

Soil nutrients are essential for the growth of healthy crops. India produces a humungous quantity of Mulberry leaves which in turn produces the raw silk. Since the climatic conditions in India is favourable, Mulberry is grown throughout the year. Majority of the farmers hardly pay attention to the nature of soil and abiotic factors due to which leaves become malnutritious and thus when they are consumed by the silkworm, desired quality end-product, raw silk, will not be produced. It is beneficial for the farmers to know the amount of yield that their land can produce so that they can plan in advance. In this paper, different Machine Learning techniques are used in predicting the yield of the Mulberry crops based on the soil parameters. Three advanced machine-learning models are selected and compared, namely, Multiple linear regression, Ridge regression and Random Forest Regression (RF). The experimental results show that Random Forest Regression outperforms other algorithms.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - K C Srikantaiah
AU  - A Deeksha
PY  - 2021
DA  - 2021/09/13
TI  - Mulberry Leaf Yield Prediction Using Machine Learning Techniques
BT  - Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021)
PB  - Atlantis Press
SP  - 393
EP  - 398
SN  - 2589-4900
UR  - https://doi.org/10.2991/ahis.k.210913.048
DO  - 10.2991/ahis.k.210913.048
ID  - Srikantaiah2021
ER  -