A Comparative Study of Machine Learning and Deep Learning Algorithms to Predict Crop Production

Title

A Comparative Study of Machine Learning and Deep Learning Algorithms to Predict Crop Production

Creator

Bindu Sri G.; Thomas K.T.

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.

Source

AIP Conference Proceedings, Vol-2754, No. 1

Date

2023-01-01

Publisher

American Institute of Physics Inc.

Subject

Agriculture; Artificial Intelligence; Crop Production; Deep Learning; Label encoder; Machine Learning; R2 score.

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

Rights

Restricted Access

Relation

ISSN: 0094243X

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1063/5.0162300

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176753209&doi=10.1063%2f5.0162300&partnerID=40&md5=db4ea44cad24ebc2aa76db45cd97d81c