Authors :
Sonal Chaudhari; Shaikh Haroon Shahadatali; Pandey Govind Parashuram; Vadalia Dhruvin Dharmesh
Volume/Issue :
Volume 7 - 2022, Issue 4 - April
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3vJhENv
DOI :
https://doi.org/10.5281/zenodo.6497488
Abstract :
As India is an agrarian country, its economy
depends mostly on the growth of agricultural yields and
agro-industrial products. Data mining is an emerging
research area in crop yield analysis. Yield prediction is a
very important issue in agriculture. Every farmer is
interested in how much yield he can expect. Discuss the
various related attributes such as location, pH from
which soil alkalinity is determined. In addition, the
percentage of nutrients such as nitrogen (N),
phosphorus (P) and potassium (K). Nutritional value of
the soil in this region, you can determine the amount of
precipitation in the region, the composition of the soil.
All these data attributes will be analyzed, they will train
the data with various suitable machine learning
algorithms to build a model. The system comes with a
model to predict crop yield precisely and accurately, and
gives the end user the appropriate recommendations on
the required fertilizer ratio based on the soil and
atmospheric parameters of the land, which they improve
to increase the yield and the income of the raise farmers.
Keywords :
Machine Learning, Crop prediction, Decision tree, Random Forest, Fertilizer recommendation, Heroku, Crop recommendation
As India is an agrarian country, its economy
depends mostly on the growth of agricultural yields and
agro-industrial products. Data mining is an emerging
research area in crop yield analysis. Yield prediction is a
very important issue in agriculture. Every farmer is
interested in how much yield he can expect. Discuss the
various related attributes such as location, pH from
which soil alkalinity is determined. In addition, the
percentage of nutrients such as nitrogen (N),
phosphorus (P) and potassium (K). Nutritional value of
the soil in this region, you can determine the amount of
precipitation in the region, the composition of the soil.
All these data attributes will be analyzed, they will train
the data with various suitable machine learning
algorithms to build a model. The system comes with a
model to predict crop yield precisely and accurately, and
gives the end user the appropriate recommendations on
the required fertilizer ratio based on the soil and
atmospheric parameters of the land, which they improve
to increase the yield and the income of the raise farmers.
Keywords :
Machine Learning, Crop prediction, Decision tree, Random Forest, Fertilizer recommendation, Heroku, Crop recommendation
