Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Yield Predictions of Four Hybrids of Maize (Zea mays) using Multispectral Images Obtained from RPAS in the Coast of Peru

David Saravia
,
Wilian Salazar
,
Lamberto Valqui-Valqui
* ORCID logo ,
Javier Quille-Mamani
,
Rossana Porras-Jorge
,
Flor-Anita Corredor
,
Elgar Barboza
ORCID logo ,
Héctor V. Vásquez
ORCID logo ,
Carlos I. Arbizu
* ORCID logo
Version 1 : Received: 16 May 2022 / Approved: 17 May 2022 / Online: 17 May 2022 (12:47:44 CEST)

How to cite: Saravia, D.; Salazar, W.; Valqui-Valqui, L.; Quille-Mamani, J.; Porras-Jorge, R.; Corredor, F.; Barboza, E.; Vásquez, H.V.; Arbizu, C.I. Yield Predictions of Four Hybrids of Maize (Zea mays) using Multispectral Images Obtained from RPAS in the Coast of Peru. Preprints 2022, 2022050231. https://doi.org/10.20944/preprints202205.0231.v1 Saravia, D.; Salazar, W.; Valqui-Valqui, L.; Quille-Mamani, J.; Porras-Jorge, R.; Corredor, F.; Barboza, E.; Vásquez, H.V.; Arbizu, C.I. Yield Predictions of Four Hybrids of Maize (Zea mays) using Multispectral Images Obtained from RPAS in the Coast of Peru. Preprints 2022, 2022050231. https://doi.org/10.20944/preprints202205.0231.v1

Abstract

Abstract: 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.

Keywords

vegetation indices; precision farming; hybrid; phenotyping; remote sensing

Subject

Biology and Life Sciences, Agricultural Science and Agronomy

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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