Yield Predictions of Four Hybrids of Maize (<em>Zea mays</em>) Using Multispectral Images Obtained from UAV 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 for the farmer’s eco...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/11/2630 |
| _version_ | 1797469458861654016 |
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| author | David Saravia Wilian Salazar Lamberto Valqui-Valqui Javier Quille-Mamani Rossana Porras-Jorge Flor-Anita Corredor Elgar Barboza Héctor V. Vásquez Andrés V. Casas Diaz Carlos I. Arbizu |
| author_facet | David Saravia Wilian Salazar Lamberto Valqui-Valqui Javier Quille-Mamani Rossana Porras-Jorge Flor-Anita Corredor Elgar Barboza Héctor V. Vásquez Andrés V. Casas Diaz Carlos I. Arbizu |
| author_sort | David Saravia |
| collection | DOAJ |
| 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 for 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 vegetation indices (VIs). A total of 10 VIs (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. 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 showed 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 estimating the performance, showing greater precision at 51 DAS. The use of unmanned aerial vehicles (UAVs) to monitor crops allows us to optimize resources and helps in making timely decisions in agriculture in Peru. |
| first_indexed | 2024-03-09T19:21:42Z |
| format | Article |
| id | doaj.art-20c00c9403794862be893a45f9590790 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T19:21:42Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-20c00c9403794862be893a45f95907902023-11-24T03:19:33ZengMDPI AGAgronomy2073-43952022-10-011211263010.3390/agronomy12112630Yield Predictions of Four Hybrids of Maize (<em>Zea mays</em>) Using Multispectral Images Obtained from UAV in the Coast of PeruDavid Saravia0Wilian Salazar1Lamberto Valqui-Valqui2Javier Quille-Mamani3Rossana Porras-Jorge4Flor-Anita Corredor5Elgar Barboza6Héctor V. Vásquez7Andrés V. Casas Diaz8Carlos I. Arbizu9Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina, 1981, Lima 15024, PeruDirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina, 1981, Lima 15024, PeruDirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina, 1981, Lima 15024, PeruDirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina, 1981, Lima 15024, PeruDirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina, 1981, Lima 15024, PeruDirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina, 1981, Lima 15024, PeruDirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina, 1981, Lima 15024, PeruDirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina, 1981, Lima 15024, PeruFacultad de Agronomía, Universidad Nacional Agraria La Molina, Av. La Molina s/n, Lima 15024, PeruDirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina, 1981, Lima 15024, PeruEarly 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 for 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 vegetation indices (VIs). A total of 10 VIs (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. 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 showed 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 estimating the performance, showing greater precision at 51 DAS. The use of unmanned aerial vehicles (UAVs) to monitor crops allows us to optimize resources and helps in making timely decisions in agriculture in Peru.https://www.mdpi.com/2073-4395/12/11/2630vegetation indicesprecision farminghybridphenotypingremote sensing |
| spellingShingle | David Saravia Wilian Salazar Lamberto Valqui-Valqui Javier Quille-Mamani Rossana Porras-Jorge Flor-Anita Corredor Elgar Barboza Héctor V. Vásquez Andrés V. Casas Diaz Carlos I. Arbizu Yield Predictions of Four Hybrids of Maize (<em>Zea mays</em>) Using Multispectral Images Obtained from UAV in the Coast of Peru Agronomy vegetation indices precision farming hybrid phenotyping remote sensing |
| title | Yield Predictions of Four Hybrids of Maize (<em>Zea mays</em>) Using Multispectral Images Obtained from UAV in the Coast of Peru |
| title_full | Yield Predictions of Four Hybrids of Maize (<em>Zea mays</em>) Using Multispectral Images Obtained from UAV in the Coast of Peru |
| title_fullStr | Yield Predictions of Four Hybrids of Maize (<em>Zea mays</em>) Using Multispectral Images Obtained from UAV in the Coast of Peru |
| title_full_unstemmed | Yield Predictions of Four Hybrids of Maize (<em>Zea mays</em>) Using Multispectral Images Obtained from UAV in the Coast of Peru |
| title_short | Yield Predictions of Four Hybrids of Maize (<em>Zea mays</em>) Using Multispectral Images Obtained from UAV in the Coast of Peru |
| title_sort | yield predictions of four hybrids of maize em zea mays em using multispectral images obtained from uav in the coast of peru |
| topic | vegetation indices precision farming hybrid phenotyping remote sensing |
| url | https://www.mdpi.com/2073-4395/12/11/2630 |
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