Open AccessApplication of Machine learning in Crop Yield Prediction of Finger Millet using Multiple Linear Regression
Author(s) -
Tarun Mourya. S S,
S. Nagini,
Anne Slagha
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit206616
Subject(s) - python (programming language) , production (economics) , agriculture , regression analysis , linear regression , agricultural engineering , yield (engineering) , regression , crop production , crop yield , computer science , machine learning , crop , mathematics , statistics , agronomy , engineering , economics , geography , materials science , archaeology , biology , metallurgy , macroeconomics , operating system
Agriculture is the mainstay of the Indian economy and it is important to enhance the production with the help of technology. Crop production is a complex phenomenon that is influenced by various parameters like climatic conditions, fertilizers, production, rainfall, etc. In the present study Machine learning is used to predict the crop yield of finger millet using multiple linear regression analysis. Multiple linear regression is considered as a model for prediction and the accuracy of the model for the given data is significantly high when compared to other models. Object oriented Python is used and packages like NumPy and Pandas are utilized for performing operations and data analysis respectively. The main advantage of the research is the prediction of the approximate crop yield well ahead of its harvest, which would help the farmers in taking appropriate measures in crop cultivation, marketing and storage. Such predictions will also help the associated industries for planning the logistics of their business.