Wheat yield prediction based on Sentinel-2, regression, and machine learning models in Hamedan, Iran

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Ashourloo D.; Manafifard M.; Behifar M.; Kohandel M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Scientia Iranica Year:2022 | Volume:29 | Issue:6 (Transactions D: Computer Science and Engineering and Electrical Engineering) Page(s): 3230-3243

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Wheat yield prediction based on Sentinel-2, regression, and machine learning models in Hamedan, Iran

Author(s)

Ashourloo D. | Manafifard M. | Behifar M. | Kohandel M. | Issue Writer Certificate

Keywords

Wheat

Yield

Sentinel-2

Gaussian process regression

Random Forest

training data size

Machine learning

Abstract

An accurate forecast of Wheat Yield prior to harvest is of great importance to ensure the sustainability of food production in Iran. The primary objective of this study is to determine the best remote sensing features and regression model for Wheat Yield prediction in Hamedan, Iran. In addition, the effects of various time windows on different regression models are verified. For this purpose, several vegetation indices (VIs) and reflectance values obtained from Sentinel-2, as input to regression models, are used in different time windows. As a result, Gaussian process regression (GPR) and Random Forest (RF) represented the top two best methods, and the best results were achieved for the GPR model with the SAVI, NDVI, EVI2, WDRVI, SR, GNDVI and GCVI indices corresponding to the image captured at the end of May. The best model Yielded a root mean square error (RMSE) of 0.228 t/ha and coefficient of determination R^2 = 0.73. Moreover, different regression methods regarding the number of training data are compared. The neural network and linear regression were the most and stepwise regression was the model affected the least by the number of training samples. Experimental results provide a technical reference for estimating large scale Wheat Yield.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans