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

• Published in: Scientific reports Vol. 14; no. 1; pp. 21674 - 17
• Main Authors: Sah, Sonam, Haldar, Dipanwita, Singh, RN, Das, B., Nain, Ajeet Singh
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.09.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/158/2456; 631/1647/245; 631/57; Agricultural production; Biomass; Cereal crops; Climate prediction; Climate variability; Crop yield; Crops; Crops, Agricultural - growth & development; Cubist; Decision making; Food security; Humanities and Social Sciences; LAI; Learning algorithms; Machine Learning; Moisture content; multidisciplinary; Neural networks; Neural Networks, Computer; Oryza - growth & development; Radar; Regression analysis; Remote sensing; Remote Sensing Technology - methods; Resource management; Rice; Science; Science (multidisciplinary); Seasons; Summer; XGB; Cubist; XGB; Biomass; LAI; Moisture content; Remote sensing
• DOI: 10.1038/s41598-024-72624-4
• ISSN: 2045-2322

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, 60, and 90 DAT), while for kharif rice, the best-performing models at 45, 60, and 90 DAT were XGB, Neural network (NNET), and Cubist, respectively. The combined ranking of ML models showed that prediction accuracy improved as the prediction date approaches harvest, and the best prediction of yield was observed at 90 DAT for both summer and kharif rice. Overall rankings indicate that for summer rice, the top three models were XGB, NNET, and Support vector regression, while for kharif rice, these were Cubist, NNET, and Random Forest, respectively. The findings of this study offer valuable insights into the potential of the combined use of remote sensing and biophysical parameters using ML models, which enhances food security planning and resource management by enabling more informed decision-making by stakeholders such as farmers, policy planners as well as researchers.