Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Nilton Nobuhiro Imai"'
Remote Prediction of Soybean Yield Using UAV-Based Hyperspectral Imaging and Machine Learning Models
Autor:
Adilson Berveglieri, Nilton Nobuhiro Imai, Fernanda Sayuri Yoshino Watanabe, Antonio Maria Garcia Tommaselli, Glória Maria Padovani Ederli, Fábio Fernandes de Araújo, Gelci Carlos Lupatini, Eija Honkavaara
Publikováno v:
AgriEngineering, Vol 6, Iss 3, Pp 3242-3260 (2024)
Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield esti
Externí odkaz:
https://doaj.org/article/70737f057af6443fb3cc5494211ae0bd
Autor:
Érika Akemi Saito Moriya, Nilton Nobuhiro Imai, Antonio Maria Garcia Tommaselli, Eija Honkavaara, David Luciano Rosalen
Publikováno v:
Agronomy, Vol 13, Iss 6, p 1542 (2023)
Phytosanitary control of crops requires the rapid mapping of diseases to enable management attention. This study aimed to evaluate the potential of vegetation indices for the detection of sugarcane mosaic disease. Spectral indices were applied to hyp
Externí odkaz:
https://doaj.org/article/cac43dd90ce64cb2b5308aeb8de24a0b
Autor:
Rorai Pereira Martins-Neto, Antonio Maria Garcia Tommaselli, Nilton Nobuhiro Imai, Eija Honkavaara, Milto Miltiadou, Erika Akemi Saito Moriya, Hassan Camil David
Publikováno v:
Forests, Vol 14, Iss 5, p 945 (2023)
This study experiments with different combinations of UAV hyperspectral data and LiDAR metrics for classifying eight tree species found in a Brazilian Atlantic Forest remnant, the most degraded Brazilian biome with high fragmentation but with huge st
Externí odkaz:
https://doaj.org/article/9d897e1b70184b159e41524fbaedd043
Autor:
Adilson Berveglieri, Nilton Nobuhiro Imai, Antonio Maria Garcia Tommaselli, Beatriz Fabretti Martinez
Publikováno v:
Anuário do Instituto de Geociências, Vol 42, Iss 4, Pp 206-218 (2020)
A reconstrução 3D do dossel de florestas para estudos retrospectivos em longas datas é possível a partir de fotografias aéreas históricas, usando modernos algoritmos fotogramétricos. Isto permite a análise de mudanças estruturais em florest
Externí odkaz:
https://doaj.org/article/db4bfc51cdd3484298a8e0ec8c952ce4
Publikováno v:
Remote Sensing, Vol 14, Iss 12, p 2805 (2022)
Invasive alien species reduce biodiversity. In southern Brazil, the genus Pinus is considered invasive, and its dispersal by humans has resulted in this species reaching ecosystems that are more sensitive and less suitable for cultivation, as is the
Externí odkaz:
https://doaj.org/article/c9e39e4656764752918c0342a0048543
Publikováno v:
Revista Brasileira de Cartografia, Vol 52 (2020)
Externí odkaz:
https://doaj.org/article/790595cffe134e86adaf1ec934fd6f61
Publikováno v:
Revista Brasileira de Cartografia, Vol 52 (2020)
Externí odkaz:
https://doaj.org/article/d2f76e2c6a78473c9bca0e7c15a88ebd
Autor:
Rorai Pereira Martins-Neto, Antonio Maria Garcia Tommaselli, Nilton Nobuhiro Imai, Hassan Camil David, Milto Miltiadou, Eija Honkavaara
Publikováno v:
Remote Sensing, Vol 13, Iss 13, p 2444 (2021)
Data collection and estimation of variables that describe the structure of tropical forests, diversity, and richness of tree species are challenging tasks. Light detection and ranging (LiDAR) is a powerful technique due to its ability to penetrate sm
Externí odkaz:
https://doaj.org/article/d724093c152d43ef8a52eea5fc4f6d80
Autor:
Carlos Rodrigo Tanajura Caldeira, Maurício Galo, Nilton Nobuhiro Imai, Maria de Lourdes Bueno Trindade Galo, Júlio Kiyoshi Hasegawa, Amilton Amorim, Milton Hirokasu Shimabukuro, Marcelo Solfa Pinto
Publikováno v:
Revista Brasileira de Cartografia, Vol 70, Iss 4 (2018)
Realizar detecções de mudanças de características da superfície da Terra é importante para a compreensão tanto da dinâmica dos fenômenos quanto para a previsão dos impactos, bem como para o apoio na tomada de decisões. Durante as últimas
Externí odkaz:
https://doaj.org/article/9b889fa057874b61893406e0579186db
Autor:
Gabriela Takahashi Miyoshi, Mauro dos Santos Arruda, Lucas Prado Osco, José Marcato Junior, Diogo Nunes Gonçalves, Nilton Nobuhiro Imai, Antonio Maria Garcia Tommaselli, Eija Honkavaara, Wesley Nunes Gonçalves
Publikováno v:
Remote Sensing, Vol 12, Iss 8, p 1294 (2020)
Deep neural networks are currently the focus of many remote sensing approaches related to forest management. Although they return satisfactory results in most tasks, some challenges related to hyperspectral data remain, like the curse of data dimensi
Externí odkaz:
https://doaj.org/article/6706a4f68521453b853cc4666d6aecc0