Examinando por Autor "Porras Jorge, Zenaida Rossana"

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Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru
(MDPI, 2023-05-19) 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
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.
Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from UAV in the Coast of Peru
(MDPI, 2022-10-26) Saravia Navarro, David; Salazar Coronel, Wilian; Valqui Valqui, Lamberto; Quille Mamani, Javier Alvaro; Porras Jorge, Zenaida Rossana; Corredor Arizapana, Flor Anita; Barboza Castillo, Elgar; Vásquez Pérez, Héctor Vladimir; Casas Diaz, Andrés V.; 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 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.