Crop Yield Analysis Using Machine Learning Algorithms

Fatin Farhan Haque, Ahmed Abdelgawad, Venkata Prasanth Yanambaka, Kumar Yelamarthi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Agriculture is not only a huge aspect of the growing economy, but it's essential for us to survive. Predicting crop yield is not an easy task, as it depends on many parameters such as water, ultra-violet (UV), pesticides, fertilizer, and the area of the land covered for that region. In this paper, two different Machine Learning (ML) algorithms are proposed to analyze the crops' yield. These two algorithms, Support Vector Regression (SVR) and Linear Regression (LR), are quite suitable for validating the variable parameters in the predicting the continuous variable estimation with 140 data points that were acquired. The parameters mentioned above are key factors affecting the yield of crops. The error rate was measured with the help of Mean Square Error (MSE) and Coefficient of Determination (R2), where MSE gave out approximately 0.005 and R2 gave around 0.85. The same dataset has been used for quick comparison between the algorithms' performances.

Original languageEnglish
Title of host publicationIEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728155036
DOIs
StatePublished - Jun 2020
Event6th IEEE World Forum on Internet of Things, WF-IoT 2020 - New Orleans, United States
Duration: Jun 2 2020Jun 16 2020

Publication series

NameIEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings

Conference

Conference6th IEEE World Forum on Internet of Things, WF-IoT 2020
Country/TerritoryUnited States
CityNew Orleans
Period06/2/2006/16/20

Keywords

  • Error
  • Linear model
  • Machine Learning
  • Regression
  • SVR

Fingerprint

Dive into the research topics of 'Crop Yield Analysis Using Machine Learning Algorithms'. Together they form a unique fingerprint.

Cite this