Agrilyst: The Crop Advisor


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

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

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