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Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models.
Sah, Sonam; Haldar, Dipanwita; Singh, R N; Das, B; Nain, Ajeet Singh.
Afiliación
  • Sah S; G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.
  • Haldar D; ICAR-National Institute of Abiotic Stress Management, Pune, Maharashtra, India.
  • Singh RN; Indian Institute of Remote Sensing, Dehradun, Uttarakhand, India.
  • Das B; ICAR-National Institute of Abiotic Stress Management, Pune, Maharashtra, India.
  • Nain AS; ICAR-Central Coastal Agricultural Research Institute, Goa, Old Goa, India.
Sci Rep ; 14(1): 21674, 2024 09 17.
Article en En | MEDLINE | ID: mdl-39289440
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, 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oryza / Tecnología de Sensores Remotos / Aprendizaje Automático Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oryza / Tecnología de Sensores Remotos / Aprendizaje Automático Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Reino Unido