A Comparative Study of Machine Learning and Deep Learning Algorithms to Predict Crop Production
subject: Agriculture; Artificial Intelligence; Crop Production; Deep Learning; Label encoder; Machine Learning; R2 score.
description: Agriculture is a field that plays an essential part in strengthening a country's economy, especially in agrarian countries like India, where agriculture and crop productivity play a large role in the economy. The research focuses on comparing machine learning and Deep learning algorithms in predicting total crop yield production. The parameters considered for the study are State name, District name, Year, Season, Crop, Area and Production. The dataset is resourced from the data.gov.in website. Random forest from Machine Learning and Sequential model from Deep learning are compared, and the performance metric considered for the study is R2 score. The objective is to assess how well the independent variable predicts the variance in the dependent variable. Random Forest algorithm achieved an R2 score of 0.89, whereas Deep Learning Sequential algorithm gave an R2 score of 0.29. 2023 American Institute of Physics Inc.. All rights reserved.
creator: Bindu Sri G.; Thomas K.T.
source: AIP Conference Proceedings, Vol-2754, No. 1
publisher: American Institute of Physics Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 0094243X
format: Online
language: English
type: Conference paper
coverage: Bindu Sri G., Department of Data Science, Christ University, Pune, Maharashtra, India; Thomas K.T., Department of Data Science, Christ University, Pune, Maharashtra, India