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Prediction of crop yield using machine learning.

Authors :
Shaik, Mohammed Ali
Manoharan, Geetha
Prashanth, B.
Akhil, Nune
Akash, Anumandla
Reddy, Thudi Raja Shekhar
Source :
AIP Conference Proceedings. 5/24/2022, Vol. 2418 Issue 1, p1-8. 8p.
Publication Year :
2022

Abstract

Farming is the major work which is considered as a culture instead like job and farming is the back bone of our economy as farming is the means which carried forth human advancement. India is nation which shows more interest towards farming and also grows all types of crops and its economy generally dependent on harvest profitability. Subsequently we can say that agriculture is major support for all business in our nation. Choosing of each harvest is significant in the choosing as each and every state in India grow various crop and the climate also varies from state to state. The choice of crop will depend on the various factors like, value of the crop, price given by the government, weather conditions and the price given by the private market buyers. Numerous progressions are needed in the field of agriculture to improve the benefit to Indian economy. We can improve agriculture by implementing AI mechanisms which can be same are defficiently on various cultivating areas. With all the advancements in the areas of machines and their improvements we can use them in cultivating the valuable and detailed data concerning various issues in addition to assuming the critical part in it. This paper helps use to getting an idea towards executing all the harvest based strategy with the ambitious techniques that helps in enchanting the maintenance of numerous agriculture and agriculture field issues. This helps the farmers to choose a best crop which helps them getting profit and also helps to increase our nation's economy. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0094243X
Volume :
2418
Issue :
1
Database :
Academic Search Index
Journal :
AIP Conference Proceedings
Publication Type :
Conference
Accession number :
157067959
Full Text :
https://doi.org/10.1063/5.0081726