Examinando por Autor "Quille Mamani, Javier Alvaro"

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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.
Estimación de la evapotranspiración a partir de imágenes de alta resolución de VANT para sistemas de riego en arrozales de la costa norte de Perú
(Universidad Nacional de Trujillo. Facultad de Ciencia Agropecuarias, 2024-02-05) Ramos Fernández, Lia; Quispe Tito, David; Altamirano Gutiérrez, Lisette; Cruz Grimaldo, Camila Leandra; Quille Mamani, Javier Alvaro; Carbonell Rivera, Juan Pedro; Torralba, Jesús; Ángel Ruiz, Luis
Ante la creciente escasez del agua para la agricultura, el incremento de la demanda de alimentos y los futuros escenarios de sequía que nos plantea el cambio climático es indispensable diseñar nuevas tecnologías que contribuyan a un menor consumo de agua. En esta investigación se han empleado imágenes de alta resolución para estimar la evapotranspiración en arrozales aplicando el modelo de balance de energía METRICTM (Mapping Evapotranspiration at High Resolution using Internalized Calibration). Para ello, se monitorizaron 5900 m2 de cultivo bajo riego por inundación continua (IC) y 2600 m2 bajo la técnica de riego de alternancia humedecimiento y secado (AWD, por sus siglas en inglés), además de algunas parcelas con filtración lateral. Se realizaron 10 vuelos entre las etapas de macollamiento y floración, cinco vuelos con un VANT Matrice 210 con una cámara multiespectral Parrot Sequoia, y cinco vuelos con un Matrice 300 RTK equipado con una cámara térmica H20T. Se colectó información de campo de los índices de vegetación (NDVI e IAF), y lecturas de un radiómetro, para ajustar información de las imágenes multiespectrales y térmicas, respectivamente; y obtener los componentes del balance de energía en superficie. Se obtuvo valores medios para evapotranspiración del cultivo (ETc) de 6,34 ±1,49 y 5,84 ± 0,41 mm d-1 para riego IC y riego AWD, respectivamente, obteniéndose un ahorro de agua del 42% con una reducción del rendimiento en 14%, proporcionando una guía para la gestión adecuada del riego, sin embargo, se sugiere utilizar el modelo para optimizar el rendimiento obteniendo umbrales críticos para la aplicación óptima de AWD frente a la escasez del recurso hídrico.
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.
Procesamiento de imágenes de vehículos aéreos no tripulados
(Instituto Nacional de Innovación Agraria, 2021-06-21) Quille Mamani, Javier Alvaro
En el taller se mostró las diferentes funciones del software Pix4D y su uso en el procesamiento RGB (Red, Green and Blue) y multiespectral de imágenes de vehículos aéreos no tripulados, en la cual se analizaron las imágenes obtenidas, el ingreso y edición de puntos de fotocontrol y la exportación de productos cartográficos. El expositor destacó que el software mencionado es uno de los más utilizados y prácticos para la fotogrametría. El taller fue desarrollado por el especialista Javier Álvaro Quille Mamani quien expuso el tema “Procesamiento de imágenes de vehículos aéreos no tripulados”.
Procesamiento de imágenes de vehículos aéreos no tripulados
(Instituto Nacional de Innovación Agraria, 2020-09-21) Quille Mamani, Javier Alvaro
El objetivo de esta presentación es hacer una comparación de adquisición de datos con sensores remotos en AP y ahondar en el uso de vehículos aéreos no tripulados (VANTs) como lo son en este caso los drones, sus utilidades y protocolos de vuelo.
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 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.
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.