Online ISSN: 2515-8260

Author : A. Suresh, N.Manjunathan1, P.Rajesh2, E. Thangadurai3,

Crop Yield Prediction Using Linear Support Vector Machine

N.Manjunathan1, P.Rajesh2, E. Thangadurai3, A. Suresh

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 6, Pages 2189-2195

The main objective of this proposal is to build a Machine Learning model that can accurately predict the rice crop yield prediction. Over 97% of the population in India depends on rice for food and is the second-highest in overall agriculture productions. But during recent years the farmers had suffered a huge loss in productions due to unexpected weather change, no knowledge about soil, underground water, area supported crops. As crop production depends on a lot of these factors, it is important to follow these factors for successful crop yield. So we are proposing a model that can accurately predict the crop yield. The algorithms used were the Support Vector Machine (SVM). SVM is used to classify the crop based on the factors of the area, season. And we are also implementing a Web Application that enables the users to interact with the ML model and make their prediction with their given inputs. The proposed system uses the Weka tool for creating the machine learning algorithms and Html, CSS, JavaScript for developing the web application