Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.)
Rice (Oryza sativa L.) is a staple crop for sustaining global food security and is particularly important in tropical and subtropical regions. In this context, precision agriculture enables more efficient crop management to increase productivity and sustainability. This study proposes an integrated...
| Autores principales: | , , , , , , , |
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| Formato: | info:eu-repo/semantics/article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2025
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12955/2811 https://doi.org/10.1016/j.atech.2025.101203 |
| _version_ | 1855028498290180096 |
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| author | Fernandez Jibaja, Jorge Antonio Atalaya Marin, Nilton Álvarez Robledo, Yeltsin Abel Taboada Mitma, Víctor Hugo Cruz Luis, Juancarlos Alejandro Tineo Flores, Daniel Goñas Goñas, Malluri Gómez Fernández, Darwin |
| author_browse | Atalaya Marin, Nilton Cruz Luis, Juancarlos Alejandro Fernandez Jibaja, Jorge Antonio Goñas Goñas, Malluri Gómez Fernández, Darwin Taboada Mitma, Víctor Hugo Tineo Flores, Daniel Álvarez Robledo, Yeltsin Abel |
| author_facet | Fernandez Jibaja, Jorge Antonio Atalaya Marin, Nilton Álvarez Robledo, Yeltsin Abel Taboada Mitma, Víctor Hugo Cruz Luis, Juancarlos Alejandro Tineo Flores, Daniel Goñas Goñas, Malluri Gómez Fernández, Darwin |
| author_sort | Fernandez Jibaja, Jorge Antonio |
| collection | Repositorio INIA |
| description | Rice (Oryza sativa L.) is a staple crop for sustaining global food security and is particularly important in tropical and subtropical regions. In this context, precision agriculture enables more efficient crop management to increase productivity and sustainability. This study proposes an integrated framework for monitoring the phenological development and estimating the yield of O. sativa by combining agronomic variables, vegetation indices (VIs), and meteorological data. Six rice varieties (Victoria, Esperanza, Bellavista, Puntilla, Capoteña, and Valor) were evaluated across six phenological stages using field data, 20 VIs and meteorological parameters. Field data revealed greater tillering of the Puntilla and Valor varieties (9–28 tillers), with Esperanza having the most stable chlorophyll values (21.5–38.7, σ = 10.46) during ripening. The temporal dynamics of the VIs consistently increased from the seedling to inflorescence emergence stage, followed by a decrease during flowering and ripening, which aligns with known physiological transitions in rice; however, significant differences in the NDVI index were detected during ripening (p > 0.05). For yield estimation, feature selection was performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) to increase model efficiency and interpretability. Among the regression algorithms tested, support vector regression (SVR) demonstrated the highest predictive accuracy (R² = 0.81) for the Bellavista variety at the maximum tillering stage. Furthermore, the Valor variety presented the highest grain yield (13.70 t/ha). These results underscore the potential of integrating multisource data with machine learning techniques for high-resolution phenological monitoring and varietal performance assessment. |
| format | info:eu-repo/semantics/article |
| id | INIA2811 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INIA28112025-07-30T06:39:29Z Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) Fernandez Jibaja, Jorge Antonio Atalaya Marin, Nilton Álvarez Robledo, Yeltsin Abel Taboada Mitma, Víctor Hugo Cruz Luis, Juancarlos Alejandro Tineo Flores, Daniel Goñas Goñas, Malluri Gómez Fernández, Darwin agronomic traits crop monitoring meteorological information remote sensing rice yield estimation características agronómicas monitoreo de cultivos información meteorológica teledetección estimación del rendimiento del arroz https://purl.org/pe-repo/ocde/ford#4.01.06 agronomic characters; Característica agronómica; meteorological data; datos meteorológicos; oryza sativa; arroz; precision agricultura; agricultura de precisión; vegetation index; índice de vegetación. Rice (Oryza sativa L.) is a staple crop for sustaining global food security and is particularly important in tropical and subtropical regions. In this context, precision agriculture enables more efficient crop management to increase productivity and sustainability. This study proposes an integrated framework for monitoring the phenological development and estimating the yield of O. sativa by combining agronomic variables, vegetation indices (VIs), and meteorological data. Six rice varieties (Victoria, Esperanza, Bellavista, Puntilla, Capoteña, and Valor) were evaluated across six phenological stages using field data, 20 VIs and meteorological parameters. Field data revealed greater tillering of the Puntilla and Valor varieties (9–28 tillers), with Esperanza having the most stable chlorophyll values (21.5–38.7, σ = 10.46) during ripening. The temporal dynamics of the VIs consistently increased from the seedling to inflorescence emergence stage, followed by a decrease during flowering and ripening, which aligns with known physiological transitions in rice; however, significant differences in the NDVI index were detected during ripening (p > 0.05). For yield estimation, feature selection was performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) to increase model efficiency and interpretability. Among the regression algorithms tested, support vector regression (SVR) demonstrated the highest predictive accuracy (R² = 0.81) for the Bellavista variety at the maximum tillering stage. Furthermore, the Valor variety presented the highest grain yield (13.70 t/ha). These results underscore the potential of integrating multisource data with machine learning techniques for high-resolution phenological monitoring and varietal performance assessment. This study was funded by Investment Project with CUI No. 2472675: “Mejoramiento de los servicios de investigación y transferencia de tecnología agraria en la estación agraria experimental Baños del Inca en la localidad de Baños del Inca del distrito de Baños del Inca - provincia de Cajamarca - departamento de Cajamarca”, Dirección de Servicios Estratégicos Agrarios (DSEA), Instituto Nacional de Innovación Agraria (INIA). The authors thank Teiser Sanchez, Pedro Torres, Larry García and Javier Yovera for their help in data collection 2025-07-30T06:39:29Z 2025-07-30T06:39:29Z 2025-07-15 info:eu-repo/semantics/article Fernandez-Jibaja, J. A., Atalaya-Marin, N., Álvarez-Robledo, Y. A., Taboada-Mitma, V. H., Cruz-Luis, J., Tineo, D., Goñas, M., & Gómez-Fernández, D. (2025). Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.). Smart Agricultural Technology , 101203. https://doi.org/10.1016/j.atech.2025.101203 2772-3755 http://hdl.handle.net/20.500.12955/2811 https://doi.org/10.1016/j.atech.2025.101203 eng urn:issn:2772-3755 Smart Agricultural Technology info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ application/pdf application/pdf Elsevier NL Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | agronomic traits crop monitoring meteorological information remote sensing rice yield estimation características agronómicas monitoreo de cultivos información meteorológica teledetección estimación del rendimiento del arroz https://purl.org/pe-repo/ocde/ford#4.01.06 agronomic characters; Característica agronómica; meteorological data; datos meteorológicos; oryza sativa; arroz; precision agricultura; agricultura de precisión; vegetation index; índice de vegetación. Fernandez Jibaja, Jorge Antonio Atalaya Marin, Nilton Álvarez Robledo, Yeltsin Abel Taboada Mitma, Víctor Hugo Cruz Luis, Juancarlos Alejandro Tineo Flores, Daniel Goñas Goñas, Malluri Gómez Fernández, Darwin Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) |
| title | Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) |
| title_full | Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) |
| title_fullStr | Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) |
| title_full_unstemmed | Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) |
| title_short | Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) |
| title_sort | integration of agronomic information vegetation indices vis and meteorological data for phenological monitoring and yield estimation of rice oryza sativa l |
| topic | agronomic traits crop monitoring meteorological information remote sensing rice yield estimation características agronómicas monitoreo de cultivos información meteorológica teledetección estimación del rendimiento del arroz https://purl.org/pe-repo/ocde/ford#4.01.06 agronomic characters; Característica agronómica; meteorological data; datos meteorológicos; oryza sativa; arroz; precision agricultura; agricultura de precisión; vegetation index; índice de vegetación. |
| url | http://hdl.handle.net/20.500.12955/2811 https://doi.org/10.1016/j.atech.2025.101203 |
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