Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models.

ABSTRACT: In an era marked by growing global population and climate variability, ensuring food security has become a paramount concern. Rice, being a staple crop for billions of people, requires accurate and timely yield prediction to ensure global food security. This study was undertaken across two rice crop seasons in the Udham Singh Nagar district of Uttarakhand state to predict rice yield at 45, 60 and 90 days after transplanting (DAT) through machine learning (ML) models, utilizing a combination of optical and Synthetic Aperture Radar (SAR) data in conjunction with crop biophysical parameters. Results revealed that the ML models were able to provide relatively accurate early yield estimates. For summer rice, eXtreme gradient boosting (XGB) was the best-performing model at all three stages (45,

SUBMITTER: Sah S 

PROVIDER: S-EPMC11408675 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature