Examinando por Autor "Porras Jorge, Rossana"

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Assessment of vegetation índices derived from UAV images for predicting biometric variables in bean during ripening stage
(Universidad de Tarapacá, 2022-03-01) Quille Mamani, Javier Alvaro; Porras Jorge, Rossana; Saravia Navarro, David; Herrera, Jordán; Chávez Galarza, Julio César; Arbizu Berrocal, Carlos Irvin; Valqui Valqui, Lamberto
Here, we report the prediction of vegetative stages variables of canary bean crop employing RGB and multispectral images obtained from UAV during the ripening stage, correlating the vegetation indices with biometric variables measured manually in the field. Results indicated a highly significant correlation of plant height with eight vegetation indices derived from UAV images from the canary bean, which were evaluated by multiple regression models, obtaining a maximum correlation of R2 = 0.79. On the other hand, the estimated indices of multispectral images did not show significant correlations.
Prediction of biometric variables through multispectral images obtained from UAV in beans (Phaseolus vulgaris L.) during ripening stage
(MDPI, 2021-06-04) Quille Mamani, Javier Alvaro; Porras Jorge, Rossana; Saravia Navarro, David; Herrera, Jordán; Chávez Galarza, Julio César; Arbizu Berrocal, Carlos Irvin
Here, we report the prediction of vegetative stages variables of canary bean crop by means of RGB and multispectral images obtained from UAV during the ripening stage, correlating the vegetation indices with biometric variables measured manually in the field. Results indicated a highly significant correlation of plant height with eight RGB image vegetation indices for the canary bean crop, which were used for predictive models, obtaining a maximum correlation of R2 = 0.79. On the other hand, the estimated indices of multispectral images did not show significant correlations.
Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru
(MDPI, 2022-05-17) 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
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.