Goigochea Pinchi, DiegoVega Herrera, Sergio SebastianTorres Chavez, Edson EsmithArchentti Reategui, FernandoBarrera Torres, CiceronDominguez Yap, Percy LuisYsuiza Perez, AlfredoPerez Tello, MonicaRios Rios, RaúlSantillan Gonzáles, Manuel DanteGanoza Roncal, Jorge JuanRuiz Reyes, Jose GuillermoAgurto Piñarreta, Alex Ivan2025-10-132025-10-132025-09-262226-2989http://hdl.handle.net/20.500.12955/2896Rice 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.application/pdfenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/nc/4.0/Oryza sativaTeledetecciónImágenes multispectralesAprendizaje automáticoMejora genéticaRemote sensingMultispectral imagingMachine learningGenetic improvement.info:eu-repo/semantics/articlehttps://purl.org/pe-repo/ocde/ford#4.01.01http://doi.org/10.17268/agroind.sci.2025.03.05Arroz; Rice; Vehículos aéreos no tripulados; Unmanned aerial vehicles; Fenotipado; Phenotyping; Rendimiento de cultivos; Crop yield; Agricultura de precisión; Precision agriculture