Crop Yield Prediction

Authors

  • Pallavi Shankarrao Mahore  Sipna College of Engineering and Technology, Amravati, Maharashtra, India
  • Dr. Aashish A. Bardekar  Sipna College of Engineering and Technology, Amravati, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT2173168

Keywords:

Agriculture, Crop Yield, Random forest, Support Vector Machine, Weather, K Nearest neighbor.

Abstract

Cotton, popularly known as White Gold has been an important commercial crop of National significance due to the immense influence of its rural economy. Transfer of technology to identify the quality of fibre is gaining importance for crop yield is compared with Random forest, Support Vector Machine, Weather, K Nearest neighbor. , which shows better performance results for each selected weather parameters. Crop yield rate depends upon various parameters such as the geography of area, soil type, soil nutrients, soil alkaline, weather condition, etc. The combination of these parameters can be used for selection of suitable crops for a farm or land to gain maximum yield. In this manuscript, soil and weather parameters such as soil type, soil fertility, maximum temperature, minimum temperature, rainfall are used to identify suitable crops for specified farm or land.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Pallavi Shankarrao Mahore, Dr. Aashish A. Bardekar, " Crop Yield Prediction, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.561-569, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT2173168