Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Luciano Shozo Shiratsuchi"'
Autor:
Franciele Morlin Carneiro, Armando Lopes de Brito Filho, Francielle Morelli Ferreira, Getulio de Freitas Seben Junior, Ziany Neiva Brandão, Rouverson Pereira da Silva, Luciano Shozo Shiratsuchi
Publikováno v:
Smart Agricultural Technology, Vol 5, Iss , Pp 100292- (2023)
Remote sensing (RS) in agriculture has been widely used for mapping soil, plant, and atmosphere attributes, as well as helping in the sustainable production of the crop by providing the possibility of application at variable rates and estimating the
Externí odkaz:
https://doaj.org/article/1cecdbd3c88b4c0ebd3f285bd2e60637
Autor:
Gabriella Silva de Gregori, Elisângela de Souza Loureiro, Luis Gustavo Amorim Pessoa, Gileno Brito de Azevedo, Glauce Taís de Oliveira Sousa Azevedo, Dthenifer Cordeiro Santana, Izabela Cristina de Oliveira, João Lucas Gouveia de Oliveira, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Carlos Antonio da Silva Junior, Paulo Eduardo Teodoro, Luciano Shozo Shiratsuchi
Publikováno v:
Remote Sensing, Vol 15, Iss 24, p 5657 (2023)
Assessing different levels of red gum lerp psyllid (Glycaspis brimblecombei) can influence the hyperspectral reflectance of leaves in different ways due to changes in chlorophyll. In order to classify these levels, the use of machine learning (ML) al
Externí odkaz:
https://doaj.org/article/329b1aaf1dac4cf0a304cfe558ead7c1
Autor:
Marcelo Rodrigues Barbosa Júnior, Bruno Rafael de Almeida Moreira, Romário Porto de Oliveira, Luciano Shozo Shiratsuchi, Rouverson Pereira da Silva
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
Predicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of ful
Externí odkaz:
https://doaj.org/article/89cfad5542154b11a990b0b12dd1cc6e
Autor:
Fernando Saragosa Rossi, Carlos Antonio da Silva Junior, José Francisco de Oliveira-Júnior, Paulo Eduardo Teodoro, Luciano Shozo Shiratsuchi, Mendelson Lima, Larissa Pereira Ribeiro Teodoro, Auana Vicente Tiago, Guilherme Fernando Capristo-Silva
Publikováno v:
International Journal of Digital Earth, Vol 14, Iss 8, Pp 1040-1066 (2021)
The aims of this study were: i) to compare no-till areas in two municipalities located in different regions of Brazil, along with the influence on CO2Flux and GPP, and ii) to verify the difference between environmental factors followed by the trends
Externí odkaz:
https://doaj.org/article/bbe0d5e2b58443cf8809c32861fc66e2
Autor:
Dthenifer Cordeiro Santana, Marcelo Carvalho Minhoto Teixeira Filho, Marcelo Rinaldi da Silva, Paulo Henrique Menezes das Chagas, João Lucas Gouveia de Oliveira, Fábio Henrique Rojo Baio, Cid Naudi Silva Campos, Larissa Pereira Ribeiro Teodoro, Carlos Antonio da Silva Junior, Paulo Eduardo Teodoro, Luciano Shozo Shiratsuchi
Publikováno v:
Remote Sensing, Vol 15, Iss 5, p 1457 (2023)
Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in soybean plants can help breeding programs develop fertilizer-efficient genotypes. Employing machine learning (ML) techniques to classify these genotypes accor
Externí odkaz:
https://doaj.org/article/bb146357efa94378a625c29e54cd7a5b
Autor:
Fábio Henrique Rojo Baio, Dthenifer Cordeiro Santana, Larissa Pereira Ribeiro Teodoro, Izabela Cristina de Oliveira, Ricardo Gava, João Lucas Gouveia de Oliveira, Carlos Antonio da Silva Junior, Paulo Eduardo Teodoro, Luciano Shozo Shiratsuchi
Publikováno v:
Remote Sensing, Vol 15, Iss 1, p 79 (2022)
Predicting maize yield using spectral information, temperature, and different irrigation management through machine learning algorithms provide information in a fast, accurate, and non-destructive way. The use of multispectral sensor data coupled wit
Externí odkaz:
https://doaj.org/article/43b4f3407b0e4c3d81ff07f4437c9066
Autor:
Carlos Antonio da Silva Junior, Marcos Rafael Nanni, José Francisco de Oliveira-Júnior, Everson Cezar, Paulo Eduardo Teodoro, Rafael Coll Delgado, Luciano Shozo Shiratsuchi, Muhammad Shakir, Marcelo Luiz Chicati
Publikováno v:
International Journal of Digital Earth, Vol 12, Iss 3, Pp 270-292 (2019)
This research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS (Moderate-Resolution Imaging Spectroradiometer) images. Two major techniques were used: GEOgraphic-Object-Bas
Externí odkaz:
https://doaj.org/article/415898c9021241ca88cdd23ee87ae6ea
Autor:
Marcelo Rodrigues Barbosa Júnior, Danilo Tedesco, Vinicius dos Santos Carreira, Antonio Alves Pinto, Bruno Rafael de Almeida Moreira, Luciano Shozo Shiratsuchi, Cristiano Zerbato, Rouverson Pereira da Silva
Publikováno v:
Drones, Vol 6, Iss 5, p 112 (2022)
Remote sensing can provide useful imagery data to monitor sugarcane in the field, whether for precision management or high-throughput phenotyping (HTP). However, research and technological development into aerial remote sensing for distinguishing cul
Externí odkaz:
https://doaj.org/article/81a0e72e573540988d67450827ec0740
Autor:
Marcelo Rodrigues Barbosa Júnior, Bruno Rafael de Almeida Moreira, Armando Lopes de Brito Filho, Danilo Tedesco, Luciano Shozo Shiratsuchi, Rouverson Pereira da Silva
Publikováno v:
Agronomy, Vol 12, Iss 3, p 661 (2022)
Pilotless aircraft systems will reshape our critical thinking about agriculture. Furthermore, because they can drive a transformative precision and digital farming, we authoritatively review the contemporary academic literature on UAVs from every ang
Externí odkaz:
https://doaj.org/article/dfca4f9a64304688913fcd5405f805a8
Autor:
Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Carlos Antonio da Silva Junior, Regimar Garcia dos Santos, Ana Paula Marques Ramos, Mayara Maezano Faita Pinheiro, Lucas Prado Osco, Wesley Nunes Gonçalves, Alexsandro Monteiro Carneiro, José Marcato Junior, Hemerson Pistori, Luciano Shozo Shiratsuchi
Publikováno v:
Remote Sensing, Vol 13, Iss 22, p 4632 (2021)
In soybean, there is a lack of research aiming to compare the performance of machine learning (ML) and deep learning (DL) methods to predict more than one agronomic variable, such as days to maturity (DM), plant height (PH), and grain yield (GY). As
Externí odkaz:
https://doaj.org/article/4a9e1fabf11b42c19ae48cb439e10211