Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging
Accurate 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 1...
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2024
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.12955/2599 https://doi.org/10.3390/rs16193720 |
| _version_ | 1855490665651109888 |
|---|---|
| author | Urquizo Barrera, Julio Cesar Ccopi Trucios, Dennis Ortega Quispe, Kevin Castañeda Tinco, Italo Patricio Rosales, Solanch Passuni Huayta, Jorge Figueroa Venegas, Deyanira Enriquez Pinedo, Lucia Ore Aquino, Zoila Pizarro Carcausto, Samuel |
| author_browse | Castañeda Tinco, Italo Ccopi Trucios, Dennis Enriquez Pinedo, Lucia Figueroa Venegas, Deyanira Ore Aquino, Zoila Ortega Quispe, Kevin Passuni Huayta, Jorge Patricio Rosales, Solanch Pizarro Carcausto, Samuel Urquizo Barrera, Julio Cesar |
| author_facet | Urquizo Barrera, Julio Cesar Ccopi Trucios, Dennis Ortega Quispe, Kevin Castañeda Tinco, Italo Patricio Rosales, Solanch Passuni Huayta, Jorge Figueroa Venegas, Deyanira Enriquez Pinedo, Lucia Ore Aquino, Zoila Pizarro Carcausto, Samuel |
| author_sort | Urquizo Barrera, Julio Cesar |
| collection | Repositorio INIA |
| description | Accurate 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. |
| format | Artículo |
| id | INIA2599 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INIA25992025-03-04T21:45:13Z Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging Urquizo Barrera, Julio Cesar Ccopi Trucios, Dennis Ortega Quispe, Kevin Castañeda Tinco, Italo Patricio Rosales, Solanch Passuni Huayta, Jorge Figueroa Venegas, Deyanira Enriquez Pinedo, Lucia Ore Aquino, Zoila Pizarro Carcausto, Samuel Germination rate Machine learning Remote sensing Photogrammetry Vegetation indices https://purl.org/pe-repo/ocde/ford#4.01.06 Germinability Poder germinativo Machine learning Aprendizaje automatico Remote sensing Teledeteccion Photogrammetry Fotogrametría Vegetation index Índice de vegetación Accurate 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. This research was funded by the project “Creación del servicio de agricultura de precision en los Departamentos de Lambayeque, Huancavelica, Ucayali y San Martín 4 Departamentos” of the Ministry of Agrarian Development and Irrigation (MIDAGRI) of the Peruvian Government with grant number CUI 2449640. 2024-10-24T17:07:01Z 2024-10-24T17:07:01Z 2024-10-06 info:eu-repo/semantics/article Urquizo-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/rs16193720 2072-4292 https://hdl.handle.net/20.500.12955/2599 https://doi.org/10.3390/rs16193720 eng urn:issn:2072-4292 Remote sensing info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ application/pdf application/pdf MDPI CH Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | Germination rate Machine learning Remote sensing Photogrammetry Vegetation indices https://purl.org/pe-repo/ocde/ford#4.01.06 Germinability Poder germinativo Machine learning Aprendizaje automatico Remote sensing Teledeteccion Photogrammetry Fotogrametría Vegetation index Índice de vegetación Urquizo Barrera, Julio Cesar Ccopi Trucios, Dennis Ortega Quispe, Kevin Castañeda Tinco, Italo Patricio Rosales, Solanch Passuni Huayta, Jorge Figueroa Venegas, Deyanira Enriquez Pinedo, Lucia Ore Aquino, Zoila Pizarro Carcausto, Samuel Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging |
| title | Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging |
| title_full | Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging |
| title_fullStr | Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging |
| title_full_unstemmed | Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging |
| title_short | Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging |
| title_sort | estimation of forage biomass in oat avena sativa using agronomic variables through uav multispectral imaging |
| topic | Germination rate Machine learning Remote sensing Photogrammetry Vegetation indices https://purl.org/pe-repo/ocde/ford#4.01.06 Germinability Poder germinativo Machine learning Aprendizaje automatico Remote sensing Teledeteccion Photogrammetry Fotogrametría Vegetation index Índice de vegetación |
| url | https://hdl.handle.net/20.500.12955/2599 https://doi.org/10.3390/rs16193720 |
| work_keys_str_mv | AT urquizobarrerajuliocesar estimationofforagebiomassinoatavenasativausingagronomicvariablesthroughuavmultispectralimaging AT ccopitruciosdennis estimationofforagebiomassinoatavenasativausingagronomicvariablesthroughuavmultispectralimaging AT ortegaquispekevin estimationofforagebiomassinoatavenasativausingagronomicvariablesthroughuavmultispectralimaging AT castanedatincoitalo estimationofforagebiomassinoatavenasativausingagronomicvariablesthroughuavmultispectralimaging AT patriciorosalessolanch estimationofforagebiomassinoatavenasativausingagronomicvariablesthroughuavmultispectralimaging AT passunihuaytajorge estimationofforagebiomassinoatavenasativausingagronomicvariablesthroughuavmultispectralimaging AT figueroavenegasdeyanira estimationofforagebiomassinoatavenasativausingagronomicvariablesthroughuavmultispectralimaging AT enriquezpinedolucia estimationofforagebiomassinoatavenasativausingagronomicvariablesthroughuavmultispectralimaging AT oreaquinozoila estimationofforagebiomassinoatavenasativausingagronomicvariablesthroughuavmultispectralimaging AT pizarrocarcaustosamuel estimationofforagebiomassinoatavenasativausingagronomicvariablesthroughuavmultispectralimaging |