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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM35198

Crop Yield Prediction using Machine Learning Techniques

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM35198

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM35198

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

J

J.Manikandan

TTKr

Tummala Tharun Kumar reddy

DS

Dogiparthy Saideep

MH

Mittapally Harshith

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Abstract

Agriculture is the field which plays an important role in improving our countries economy. Agriculture is the one which gave birth to civilization. India is an agrarian country and its economy largely based upon crop productivity, hence we can say that agriculture can be backbone of all business in our country. Selecting of every crop is very important in the agriculture planning. The selection of crops will depend upon the different parameters such as market price, production rate and the different government policies. Many changes are required in the agriculture field to improve changes in our Indian economy. We can improve agriculture by using machine learning techniques which are applied easily on farming sector. Along with all advances in the machines and technologies used in farming, useful and accurate information about different matters also plays a significant role in it. The concept of this paper is to implement the crop selection method so that this method helps in solving many agriculture and farmers problems. This improves our Indian economy by maximizing the yield rate of crop production.

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Cite This Article

J.Manikandan, Tummala Tharun Kumar reddy, Dogiparthy Saideep, Mittapally Harshith, "Crop Yield Prediction using Machine Learning Techniques," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM35198

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12/31/45442

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Double-Blind Peer Review

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