Rice phenotyping using unmanned aerial vehicles: Analyzing morphological characteristics and yield
Rice is a globally important crop and a staple in the diet of a large part of the world's population. This underscores the need for hybridization and improvement of rice genotypes to meet food demand in an environmentally sustainable manner. Geographic Information Systems (GIS) have proven to be val...
| Autores principales: | , , , , , , , , , , , , |
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| Formato: | info:eu-repo/semantics/article |
| Lenguaje: | Inglés |
| Publicado: |
PE
2025
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12955/2896 http://doi.org/10.17268/agroind.sci.2025.03.05 |
| _version_ | 1855028755166134272 |
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| author | Goigochea Pinchi, Diego Vega Herrera, Sergio Sebastian Torres Chavez, Edson Esmith Archentti Reategui, Fernando Barrera Torres, Ciceron Dominguez Yap, Percy Luis Ysuiza Perez, Alfredo Perez Tello, Monica Rios Rios, Raúl Santillan Gonzáles, Manuel Dante Ganoza Roncal, Jorge Juan Ruiz Reyes, Jose Guillermo Agurto Piñarreta, Alex Ivan |
| author_browse | Agurto Piñarreta, Alex Ivan Archentti Reategui, Fernando Barrera Torres, Ciceron Dominguez Yap, Percy Luis Ganoza Roncal, Jorge Juan Goigochea Pinchi, Diego Perez Tello, Monica Rios Rios, Raúl Ruiz Reyes, Jose Guillermo Santillan Gonzáles, Manuel Dante Torres Chavez, Edson Esmith Vega Herrera, Sergio Sebastian Ysuiza Perez, Alfredo |
| author_facet | Goigochea Pinchi, Diego Vega Herrera, Sergio Sebastian Torres Chavez, Edson Esmith Archentti Reategui, Fernando Barrera Torres, Ciceron Dominguez Yap, Percy Luis Ysuiza Perez, Alfredo Perez Tello, Monica Rios Rios, Raúl Santillan Gonzáles, Manuel Dante Ganoza Roncal, Jorge Juan Ruiz Reyes, Jose Guillermo Agurto Piñarreta, Alex Ivan |
| author_sort | Goigochea Pinchi, Diego |
| collection | Repositorio INIA |
| description | Rice is a globally important crop and a staple in the diet of a large part of the world's population. This underscores the need for hybridization and improvement of rice genotypes to meet food demand in an environmentally sustainable manner. Geographic Information Systems (GIS) have proven to be valuable tools for the morphometric phenotyping of different genotypes. In this study, seven different rice genotypes were evaluated with the objective of selecting those with high yield. Multispectral imagery was used to develop prediction models based on supervised learning algorithms, including Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Elastic Net (EN), and Neural Networks (NN). The variables studied were plant height, number of panicles, number of tillers, and yield. The results showed the following performances: R² = 0.44 for plant height using Random Forest, R² = 0.92 for number of panicles with Neural Networks, R² = 0.44 for number of tillers with SVM, and R² = 0.31 for yield with SVM. This technology significantly supports traditional selection methodologies for hybridization and improvement by providing a spatial approach that enhances and facilitates selection criteria. |
| format | info:eu-repo/semantics/article |
| id | INIA2896 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | PE |
| publisherStr | PE |
| record_format | dspace |
| spelling | INIA28962025-10-29T14:08:34Z Rice phenotyping using unmanned aerial vehicles: Analyzing morphological characteristics and yield Fenotipado del arroz mediante vehículos aéreos no tripulados: Análisis de características morfológicas y rendimiento Goigochea Pinchi, Diego Vega Herrera, Sergio Sebastian Torres Chavez, Edson Esmith Archentti Reategui, Fernando Barrera Torres, Ciceron Dominguez Yap, Percy Luis Ysuiza Perez, Alfredo Perez Tello, Monica Rios Rios, Raúl Santillan Gonzáles, Manuel Dante Ganoza Roncal, Jorge Juan Ruiz Reyes, Jose Guillermo Agurto Piñarreta, Alex Ivan Oryza sativa Teledetección Imágenes multispectrales Aprendizaje automático Mejora genética Remote sensing Multispectral imaging Machine learning Genetic improvement. https://purl.org/pe-repo/ocde/ford#4.01.01 Arroz; Rice; Vehículos aéreos no tripulados; Unmanned aerial vehicles; Fenotipado; Phenotyping; Rendimiento de cultivos; Crop yield; Agricultura de precisión; Precision agriculture Rice is a globally important crop and a staple in the diet of a large part of the world's population. This underscores the need for hybridization and improvement of rice genotypes to meet food demand in an environmentally sustainable manner. Geographic Information Systems (GIS) have proven to be valuable tools for the morphometric phenotyping of different genotypes. In this study, seven different rice genotypes were evaluated with the objective of selecting those with high yield. Multispectral imagery was used to develop prediction models based on supervised learning algorithms, including Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Elastic Net (EN), and Neural Networks (NN). The variables studied were plant height, number of panicles, number of tillers, and yield. The results showed the following performances: R² = 0.44 for plant height using Random Forest, R² = 0.92 for number of panicles with Neural Networks, R² = 0.44 for number of tillers with SVM, and R² = 0.31 for yield with SVM. This technology significantly supports traditional selection methodologies for hybridization and improvement by providing a spatial approach that enhances and facilitates selection criteria. 2025-10-13T20:17:11Z 2025-10-13T20:17:11Z 2025-09-26 info:eu-repo/semantics/article 2226-2989 http://hdl.handle.net/20.500.12955/2896 http://doi.org/10.17268/agroind.sci.2025.03.05 eng urn:issn:2226-2989 Universidad Nacional de Trujillo - Escuela de Ingeniería Agroindustrial info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/nc/4.0/ application/pdf application/pdf PE Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | Oryza sativa Teledetección Imágenes multispectrales Aprendizaje automático Mejora genética Remote sensing Multispectral imaging Machine learning Genetic improvement. https://purl.org/pe-repo/ocde/ford#4.01.01 Arroz; Rice; Vehículos aéreos no tripulados; Unmanned aerial vehicles; Fenotipado; Phenotyping; Rendimiento de cultivos; Crop yield; Agricultura de precisión; Precision agriculture Goigochea Pinchi, Diego Vega Herrera, Sergio Sebastian Torres Chavez, Edson Esmith Archentti Reategui, Fernando Barrera Torres, Ciceron Dominguez Yap, Percy Luis Ysuiza Perez, Alfredo Perez Tello, Monica Rios Rios, Raúl Santillan Gonzáles, Manuel Dante Ganoza Roncal, Jorge Juan Ruiz Reyes, Jose Guillermo Agurto Piñarreta, Alex Ivan Rice phenotyping using unmanned aerial vehicles: Analyzing morphological characteristics and yield |
| title | Rice phenotyping using unmanned aerial vehicles: Analyzing morphological characteristics and yield |
| title_full | Rice phenotyping using unmanned aerial vehicles: Analyzing morphological characteristics and yield |
| title_fullStr | Rice phenotyping using unmanned aerial vehicles: Analyzing morphological characteristics and yield |
| title_full_unstemmed | Rice phenotyping using unmanned aerial vehicles: Analyzing morphological characteristics and yield |
| title_short | Rice phenotyping using unmanned aerial vehicles: Analyzing morphological characteristics and yield |
| title_sort | rice phenotyping using unmanned aerial vehicles analyzing morphological characteristics and yield |
| topic | Oryza sativa Teledetección Imágenes multispectrales Aprendizaje automático Mejora genética Remote sensing Multispectral imaging Machine learning Genetic improvement. https://purl.org/pe-repo/ocde/ford#4.01.01 Arroz; Rice; Vehículos aéreos no tripulados; Unmanned aerial vehicles; Fenotipado; Phenotyping; Rendimiento de cultivos; Crop yield; Agricultura de precisión; Precision agriculture |
| url | http://hdl.handle.net/20.500.12955/2896 http://doi.org/10.17268/agroind.sci.2025.03.05 |
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