Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru
Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability in the farmer's econ...
| Main Authors: | , , , , , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | Inglés |
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MDPI
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/20.500.12955/1852 https://doi.org/10.20944/preprints202205.0231.v1 |
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| author | Saravia Navarro, David Salazar Coronel, Wilian Valqui Valqui, Lamberto Quille Mamani, Javier Alvaro Porras Jorge, Rossana Corredor Arizapana, Flor Anita Barboza Castillo, Elgar Vásquez Pérez, Héctor Vladimir Arbizu Berrocal, Carlos Irvin |
| author_browse | Arbizu Berrocal, Carlos Irvin Barboza Castillo, Elgar Corredor Arizapana, Flor Anita Porras Jorge, Rossana Quille Mamani, Javier Alvaro Salazar Coronel, Wilian Saravia Navarro, David Valqui Valqui, Lamberto Vásquez Pérez, Héctor Vladimir |
| author_facet | Saravia Navarro, David Salazar Coronel, Wilian Valqui Valqui, Lamberto Quille Mamani, Javier Alvaro Porras Jorge, Rossana Corredor Arizapana, Flor Anita Barboza Castillo, Elgar Vásquez Pérez, Héctor Vladimir Arbizu Berrocal, Carlos Irvin |
| author_sort | Saravia Navarro, David |
| collection | Repositorio INIA |
| description | Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability in the farmer's economy. In this study we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using remotely sensed spectral vegetation indices (VI). A total of 10 VI (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. In the present study, highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA indicated a clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimate the performance, showing greater precision at 51 DAS. The use of RPAS to monitor crops allows optimizing resources and helps in making timely decisions in agriculture in Peru. |
| format | info:eu-repo/semantics/article |
| id | INIA1852 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INIA18522023-08-18T17:32:52Z Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru Saravia Navarro, David Salazar Coronel, Wilian Valqui Valqui, Lamberto Quille Mamani, Javier Alvaro Porras Jorge, Rossana Corredor Arizapana, Flor Anita Barboza Castillo, Elgar Vásquez Pérez, Héctor Vladimir Arbizu Berrocal, Carlos Irvin Vegetation índices Precision farming Hybrid Phenotyping Remote sensing https://purl.org/pe-repo/ocde/ford#4.04.00 Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability in the farmer's economy. In this study we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using remotely sensed spectral vegetation indices (VI). A total of 10 VI (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. In the present study, highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA indicated a clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimate the performance, showing greater precision at 51 DAS. The use of RPAS to monitor crops allows optimizing resources and helps in making timely decisions in agriculture in Peru. Abstract. 1. Introduction. 2. Materials and Methods. 3. Results. 4. Discussion. 5. Conclusions. References. 2022-09-05T16:59:47Z 2022-09-05T16:59:47Z 2022-05-17 info:eu-repo/semantics/article Saravia, D.; Salazar, W.; Valqui, L.; Quille, J.; Porras, R.; Corredor, F.; Barboza, E.; Vásquez, H. & Arbizu, C. (2022). Yield Predictions of Four Hybrids of Maize (Zea mays) using Multispectral Images Obtained from RPAS in the Coast of Peru. Preprints, 2022050231. doi: 10.20944/preprints202205.0231.v1 https://hdl.handle.net/20.500.12955/1852 Preprints https://doi.org/10.20944/preprints202205.0231.v1 eng https://doi.org/10.20944/preprints202205.0231.v1 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ application/pdf application/pdf Perú MDPI Suiza Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | Vegetation índices Precision farming Hybrid Phenotyping Remote sensing https://purl.org/pe-repo/ocde/ford#4.04.00 Saravia Navarro, David Salazar Coronel, Wilian Valqui Valqui, Lamberto Quille Mamani, Javier Alvaro Porras Jorge, Rossana Corredor Arizapana, Flor Anita Barboza Castillo, Elgar Vásquez Pérez, Héctor Vladimir Arbizu Berrocal, Carlos Irvin Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru |
| title | Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru |
| title_full | Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru |
| title_fullStr | Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru |
| title_full_unstemmed | Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru |
| title_short | Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru |
| title_sort | yield predictions of four hybrids of maize zea mays using multispectral images obtained from rpas in the coast of peru |
| topic | Vegetation índices Precision farming Hybrid Phenotyping Remote sensing https://purl.org/pe-repo/ocde/ford#4.04.00 |
| url | https://hdl.handle.net/20.500.12955/1852 https://doi.org/10.20944/preprints202205.0231.v1 |
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