Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru
In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of in...
| Autores principales: | , , , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.12955/2168 https://doi.org/10.3390/drones7050325 |
| _version_ | 1855490570788536320 |
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| author | Saravia Navarro, David Valqui Valqui, Lamberto Salazar Coronal, Wilian Quille Mamani, Javier Alvaro Barboza Castillo, Elgar Porras Jorge, Zenaida Rossana Injante Silva, Pedro Hugo Arbizu Berrocal, Carlos Irvin |
| author_browse | Arbizu Berrocal, Carlos Irvin Barboza Castillo, Elgar Injante Silva, Pedro Hugo Porras Jorge, Zenaida Rossana Quille Mamani, Javier Alvaro Salazar Coronal, Wilian Saravia Navarro, David Valqui Valqui, Lamberto |
| author_facet | Saravia Navarro, David Valqui Valqui, Lamberto Salazar Coronal, Wilian Quille Mamani, Javier Alvaro Barboza Castillo, Elgar Porras Jorge, Zenaida Rossana Injante Silva, Pedro Hugo Arbizu Berrocal, Carlos Irvin |
| author_sort | Saravia Navarro, David |
| collection | Repositorio INIA |
| description | In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R2 for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R2 for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem. |
| format | Artículo |
| id | INIA2168 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INIA21682023-08-22T17:42:06Z Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru Saravia Navarro, David Valqui Valqui, Lamberto Salazar Coronal, Wilian Quille Mamani, Javier Alvaro Barboza Castillo, Elgar Porras Jorge, Zenaida Rossana Injante Silva, Pedro Hugo Arbizu Berrocal, Carlos Irvin Multiple regression Multispectral imaging NDVI Precision agriculture Remote sensing https://purl.org/pe-repo/ocde/ford#4.01.06 Multiple regression analysis Multispectral imagery Normalized difference vegetation index Análisis por regresión múltiple Imágenes multiespectrales Indice normalizado diferencial de la vegetación Precision agricultura Agricultura de precisión Teledetección In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R2 for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R2 for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem. 2023-06-05T16:48:47Z 2023-06-05T16:48:47Z 2023-05-19 info:eu-repo/semantics/article Saravia, D.; Valqui-Valqui, L.; Salazar, W.; Quille-Mamani, J.; Barboza, E.; Porras-Jorge, R.; ... & Arbizu, C. I. (2023). Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru. Drones, 7(5), 325. doi: 10.3390/drones7050325 2504-446X https://hdl.handle.net/20.500.12955/2168 https://doi.org/10.3390/drones7050325 eng urn:issn:2504-446X Drones 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 | Multiple regression Multispectral imaging NDVI Precision agriculture Remote sensing https://purl.org/pe-repo/ocde/ford#4.01.06 Multiple regression analysis Multispectral imagery Normalized difference vegetation index Análisis por regresión múltiple Imágenes multiespectrales Indice normalizado diferencial de la vegetación Precision agricultura Agricultura de precisión Teledetección Saravia Navarro, David Valqui Valqui, Lamberto Salazar Coronal, Wilian Quille Mamani, Javier Alvaro Barboza Castillo, Elgar Porras Jorge, Zenaida Rossana Injante Silva, Pedro Hugo Arbizu Berrocal, Carlos Irvin Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru |
| title | Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru |
| title_full | Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru |
| title_fullStr | Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru |
| title_full_unstemmed | Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru |
| title_short | Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru |
| title_sort | yield prediction of four bean phaseolus vulgaris cultivars using vegetation indices based on multispectral images from uav in an arid zone of peru |
| topic | Multiple regression Multispectral imaging NDVI Precision agriculture Remote sensing https://purl.org/pe-repo/ocde/ford#4.01.06 Multiple regression analysis Multispectral imagery Normalized difference vegetation index Análisis por regresión múltiple Imágenes multiespectrales Indice normalizado diferencial de la vegetación Precision agricultura Agricultura de precisión Teledetección |
| url | https://hdl.handle.net/20.500.12955/2168 https://doi.org/10.3390/drones7050325 |
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