Published October 30, 2020 | Version v1
Journal article Open

Analysis on Present Mathematical Model for Predicting the Crop Production

  • 1. Research Scholar, Department of Mathematics, Sandip University, Nashik, Maharashtra, India.
  • 2. FPM, EDII, Ahmedabad, Gujrat, India
  • 3. Assistant Professor, Department of Mathematics, Sandip University, Nashik, Maharashtra, India
  • 1. Publisher

Description

India is a worldwide agriculture business powerhouse. Future of agriculture-based products depends on the crop production. A mathematical model might be characterized as a lot of equations that speak to the conduct of a framework. By using mathematical model in agriculture field, we can predict the production of crop in particular area. There are various factors affecting crops such as Rainfall, GHG Emissions, Temperature, Urbanization, climate, humidity etc. A mathematical model is a simplified representation of a real-world system. It forms the system using mathematical principles in the form of a condition or a set of conditions. Suppose we need to increase the crop production, at that time the mathematical model plays a major role and our work can be easier, more significant by using the mathematical model. Through the mathematical model we predict the crop production in upcoming years. .AI, ML, IOT play a major role to predict the future of agriculture, but without mathematical models it is not possible to predict crop production accurately. To solve the real-world agriculture problem, mathematical models play a major role for accurate results. Correlation Analysis, Multiple Regression analysis and fuzzy logic simulation standards have been utilized for building a grain production benefit depending model from crop production. Prediction of crop is beneficiary to the farmer to analyze the crop management. By using the present agriculture data set which is available on the government website, we can build a mathematical model.

Files

L79461091220.pdf

Files (357.4 kB)

Name Size Download all
md5:551fe2e8c15f5ece6b3b9e660b25a541
357.4 kB Preview Download

Additional details

Related works

Is cited by
Journal article: 2278-3075 (ISSN)

Subjects

ISSN
2278-3075
Retrieval Number
100.1/ijitee.L79461091220