Rice phenotyping using unmanned aerial vehicles: Analyzing morphological characteristics and yield

• Autores: 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
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.inia.gob.pe:20.500.12955/2896
• Enlace del recurso: http://hdl.handle.net/20.500.12955/2896; http://doi.org/10.17268/agroind.sci.2025.03.05
• Nivel de acceso: acceso abierto
• Materia: 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.