Urquizo Barrera, Julio CesarJulio CesarUrquizo BarreraCcopi Trucios, DennisDennisCcopi TruciosOrtega Quispe, KevinKevinOrtega QuispeCastañeda Tinco, ItaloItaloCastañeda TincoPatricio Rosales, SolanchSolanchPatricio RosalesPassuni Huayta, JorgeJorgePassuni HuaytaFigueroa Venegas, DeyaniraDeyaniraFigueroa VenegasEnriquez Pinedo, LuciaLuciaEnriquez PinedoOre Aquino, ZoilaZoilaOre AquinoPizarro Carcausto, SamuelSamuelPizarro Carcausto2025-03-062025-03-062024-10-06Urquizo-Barrera, J.; Ccopi-Trucios, D.; Ortega-Quispe, K.; Castañeda-Tinco, I.; Patricio-Rosales, S.; Passuni-Huayta, J.; Figueroa-Venegas, D.; Enriquez-Pinedo, L.; Ore-Aquino, Z.; & Pizarro-Carcausto, S. (2024). Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging. Remote sensing,16, 3720. doi:10.3390/rs161937202072-4292https://hdl.handle.net/20.500.12955/2599Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R2 of 0.52 on the test set, followed by Support Vector Machines with an R2 of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose.application/pdfenhttp://purl.org/coar/access_right/c_abf2Germination rateMachine learningRemote sensingPhotogrammetryVegetation indiceshttp://purl.org/coar/resource_type/c_650110.3390/rs16193720https://purl.org/pe-repo/ocde/ford#4.01.06GerminabilityPoder germinativoMachine learningAprendizaje automaticoRemote sensingTeledeteccionPhotogrammetryFotogrametríaVegetation indexÍndice de vegetación