Rice phenotyping using unmanned aerial vehicles: Analyzing morphological characteristics and yield
Year: 2025
Type: article
Abstract: 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
Source: Agroindustrial science
Authors: Diego Goigochea-Pinchi, Edson Esmith Torres-Chavez, Sergio Sebastian Vega-Herrera, Fernando Archentti-Reategui, Ciceron Barrera-Torres, Percy Luis Dominguez-Yap, Alfredo Ysuiza-Perez, Monica Perez-Tello, Raúl Rios-Rios, Manuel Dante Santillan-Gonzáles, Jorge Juan Ganoza-Roncal, Jose Guillermo Ruiz-Reyes, Alex Ivan Agurto-Piñarreta
Institution: Instituto Nacional de Innovación Agraria
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4415112900
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