Yield prediction models for rice varieties using UAV multispectral imagery in the Amazon lowlands of Peru
Rice is cataloged as one of the most widely cultivated crops globally, providing food for a large proportion of the global population. Integrating Geographic Information Systems (GISs), such as unmanned aerial vehicles (UAVs), into agricultural practices offers numerous benefits. UAVs, equipped with...
| Main Authors: | , , , , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
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MDPI
2024
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| Online Access: | https://hdl.handle.net/20.500.12955/2561 https://doi.org/10.3390/agriengineering6030170 |
| _version_ | 1855490631646838784 |
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| author | Goigochea Pinchi, Diego Justino Pinedo, Maikol Vega Herrera, Sergio Sebastian Sanchez Ojanasta, Martín Lobato Galvez, Roiser Honorio Santillan Gonzales, Manuel Dante Ganoza Roncal, Jorge Juan Ore Aquino, Zoila Luz Agurto Piñarreta, Alex Iván |
| author_browse | Agurto Piñarreta, Alex Iván Ganoza Roncal, Jorge Juan Goigochea Pinchi, Diego Justino Pinedo, Maikol Lobato Galvez, Roiser Honorio Ore Aquino, Zoila Luz Sanchez Ojanasta, Martín Santillan Gonzales, Manuel Dante Vega Herrera, Sergio Sebastian |
| author_facet | Goigochea Pinchi, Diego Justino Pinedo, Maikol Vega Herrera, Sergio Sebastian Sanchez Ojanasta, Martín Lobato Galvez, Roiser Honorio Santillan Gonzales, Manuel Dante Ganoza Roncal, Jorge Juan Ore Aquino, Zoila Luz Agurto Piñarreta, Alex Iván |
| author_sort | Goigochea Pinchi, Diego |
| collection | Repositorio INIA |
| description | Rice is cataloged as one of the most widely cultivated crops globally, providing food for a large proportion of the global population. Integrating Geographic Information Systems (GISs), such as unmanned aerial vehicles (UAVs), into agricultural practices offers numerous benefits. UAVs, equipped with imaging sensors and geolocation technology, enable precise crop monitoring and management, enhancing yield and efficiency. However, Peru lacks sufficient experience with the application of these technologies, making them somewhat unfamiliar in the context of modern agriculture. In this study, we conducted experiments involving four distinct rice varieties (n = 24) at various stages of growth to predict yield using vegetation indices (VIs). A total of nine VIs (NDVI, GNDVI, ReCL, CIgreen, MCARI, SAVI, CVI, LCI, and EVI) were assessed across four dates: 88, 103, 116, and 130 days after sowing (DAS). Pearson correlation analysis, principal component analysis (PCA), and multiple linear regression were used to build prediction models. The results showed a general prediction model (including all the varieties) with the best performance at 130 days after sowing (DAS) using NDVI, EVI, and SAVI, with a coefficient of determination (adjusted-R2 = 0.43). The prediction models by variety showed the best performance for Esperanza at 88 DAS (adjusted-R2 = 0.94) using EVI as the vegetation index. The other varieties showed their best performance using different indices at different times: Capirona (LCI and CIgreen, 130 DAS, adjusted-R2 = 0.62); Conquista Certificada (MCARI, 116 DAS, R2 = 0.52); and Conquista Registrada (CVI and LCI, 116 DAS, adjusted-R2 = 0.79). These results provide critical information for optimizing rice crop management and support the use of unmanned aerial vehicles (UAVs) to inform timely decision making and mitigate yield losses in Peruvian agriculture. |
| format | Artículo |
| id | INIA2561 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INIA25612024-08-28T05:38:28Z Yield prediction models for rice varieties using UAV multispectral imagery in the Amazon lowlands of Peru Goigochea Pinchi, Diego Justino Pinedo, Maikol Vega Herrera, Sergio Sebastian Sanchez Ojanasta, Martín Lobato Galvez, Roiser Honorio Santillan Gonzales, Manuel Dante Ganoza Roncal, Jorge Juan Ore Aquino, Zoila Luz Agurto Piñarreta, Alex Iván Multiple regressions Remote Sensing Precision agriculture RPAS Drones San Martin Oryza sativa https://purl.org/pe-repo/ocde/ford#4.01.01 Regression analysis Análisis de la regresión Remote sensing Teledetección Precision agriculture Agricultura de precisión Unmanned aerial vehicles Vehículo aéreo no tripulado Oryza sativa Rice is cataloged as one of the most widely cultivated crops globally, providing food for a large proportion of the global population. Integrating Geographic Information Systems (GISs), such as unmanned aerial vehicles (UAVs), into agricultural practices offers numerous benefits. UAVs, equipped with imaging sensors and geolocation technology, enable precise crop monitoring and management, enhancing yield and efficiency. However, Peru lacks sufficient experience with the application of these technologies, making them somewhat unfamiliar in the context of modern agriculture. In this study, we conducted experiments involving four distinct rice varieties (n = 24) at various stages of growth to predict yield using vegetation indices (VIs). A total of nine VIs (NDVI, GNDVI, ReCL, CIgreen, MCARI, SAVI, CVI, LCI, and EVI) were assessed across four dates: 88, 103, 116, and 130 days after sowing (DAS). Pearson correlation analysis, principal component analysis (PCA), and multiple linear regression were used to build prediction models. The results showed a general prediction model (including all the varieties) with the best performance at 130 days after sowing (DAS) using NDVI, EVI, and SAVI, with a coefficient of determination (adjusted-R2 = 0.43). The prediction models by variety showed the best performance for Esperanza at 88 DAS (adjusted-R2 = 0.94) using EVI as the vegetation index. The other varieties showed their best performance using different indices at different times: Capirona (LCI and CIgreen, 130 DAS, adjusted-R2 = 0.62); Conquista Certificada (MCARI, 116 DAS, R2 = 0.52); and Conquista Registrada (CVI and LCI, 116 DAS, adjusted-R2 = 0.79). These results provide critical information for optimizing rice crop management and support the use of unmanned aerial vehicles (UAVs) to inform timely decision making and mitigate yield losses in Peruvian agriculture. This research was funded by the project “Creación del servicio de agricultura de precisión en los Departamentos de Lambayeque, Huancavelica, Ucayali y San Martín” of the Instituto Nacional de Innovación Agraria (INIA), which is part of the Ministerio de Desarrollo Agrario y Riego (MIDAGRI) of the Peruvian Government, with grant number CUI 2449640. 2024-08-28T05:38:27Z 2024-08-28T05:38:27Z 2024-08-20 info:eu-repo/semantics/article Goigochea-Pinchi, D.; Justino-Pinedo, M.; Vega-Herrera, S.S.; Sanchez-Ojanasta, M.; Lobato-Galvez, R.H.; Santillan-Gonzales, M.D.; Ganoza-Roncal, J.J.; Ore-Aquino, Z.L. & Agurto-Piñarreta, A.I. (2024). Yield prediction models for rice varieties using UAV multispectral imagery in the Amazon lowlands of Peru. AgriEngineering, 6(3), 2955-2969. doi:10.3390/agriengineering6030170 2624-7402 https://hdl.handle.net/20.500.12955/2561 https://doi.org/10.3390/agriengineering6030170 eng urn:issn:2624-7402 AgriEngineering info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ application/pdf application/pdf MDPI CH Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | Multiple regressions Remote Sensing Precision agriculture RPAS Drones San Martin Oryza sativa https://purl.org/pe-repo/ocde/ford#4.01.01 Regression analysis Análisis de la regresión Remote sensing Teledetección Precision agriculture Agricultura de precisión Unmanned aerial vehicles Vehículo aéreo no tripulado Oryza sativa Goigochea Pinchi, Diego Justino Pinedo, Maikol Vega Herrera, Sergio Sebastian Sanchez Ojanasta, Martín Lobato Galvez, Roiser Honorio Santillan Gonzales, Manuel Dante Ganoza Roncal, Jorge Juan Ore Aquino, Zoila Luz Agurto Piñarreta, Alex Iván Yield prediction models for rice varieties using UAV multispectral imagery in the Amazon lowlands of Peru |
| title | Yield prediction models for rice varieties using UAV multispectral imagery in the Amazon lowlands of Peru |
| title_full | Yield prediction models for rice varieties using UAV multispectral imagery in the Amazon lowlands of Peru |
| title_fullStr | Yield prediction models for rice varieties using UAV multispectral imagery in the Amazon lowlands of Peru |
| title_full_unstemmed | Yield prediction models for rice varieties using UAV multispectral imagery in the Amazon lowlands of Peru |
| title_short | Yield prediction models for rice varieties using UAV multispectral imagery in the Amazon lowlands of Peru |
| title_sort | yield prediction models for rice varieties using uav multispectral imagery in the amazon lowlands of peru |
| topic | Multiple regressions Remote Sensing Precision agriculture RPAS Drones San Martin Oryza sativa https://purl.org/pe-repo/ocde/ford#4.01.01 Regression analysis Análisis de la regresión Remote sensing Teledetección Precision agriculture Agricultura de precisión Unmanned aerial vehicles Vehículo aéreo no tripulado Oryza sativa |
| url | https://hdl.handle.net/20.500.12955/2561 https://doi.org/10.3390/agriengineering6030170 |
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