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	Provedor de dados Agronomy (1) CIGR Journal (1) Acta Amazonica (1) Autor ADAMI,Marcos (1) ALMEIDA,Cláudio Aparecido de (1) COUTINHO,Alexandre Camargo (1) DESSAY,Nadine (1) DINIZ,Cesar Guerreiro (1) DURIEUX,Laurent (1) ESQUERDO,Júlio César Dalla Mora (1) GOMES,Alessandra Rodrigues (1) Ganganagowdar, Narendra Veranagouda (1) Kalantari, Davood (1) Rédl, Jozef (1) Sedighi, Shahryar (1) Shiukhy, Saeid (1) Siddaramappa, Hareesha Katiganere (1) VENTURIERI,Adriano (1) Mais... Palavra-chave Image Processing (3) Artificial neural networks (1) Classification (1) Computer Science and Engineering (1) Computer Vision (1) Feature extraction (1) Machine Vision (1) Poplar Tree (1) Remote Sensing (1) Soft Computing White Wholes (WW) grade cashew kernel images (1) TerraClass (1) Tropical Deforestation (1) Wavelength (1) Mais... Tipo do documento Info:eu-repo/semantics/article (3) Ano 2021 (1) 2016 (2) País Brazil (2) China (1) Idioma Inglês (3)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 3 Primeira ... 1 ... Última High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data Acta Amazonica ALMEIDA,Cláudio Aparecido de; COUTINHO,Alexandre Camargo; ESQUERDO,Júlio César Dalla Mora; ADAMI,Marcos; VENTURIERI,Adriano; DINIZ,Cesar Guerreiro; DESSAY,Nadine; DURIEUX,Laurent; GOMES,Alessandra Rodrigues. ABSTRACT Understanding spatial patterns of land use and land cover is essential for studies addressing biodiversity, climate change and environmental modeling as well as for the design and monitoring of land use policies. The aim of this study was to create a detailed map of land use land cover of the deforested areas of the Brazilian Legal Amazon up to 2008. Deforestation data from and uses were mapped with Landsat-5/TM images analysed with techniques, such as linear spectral mixture model, threshold slicing and visual interpretation, aided by temporal information extracted from NDVI MODIS time series. The result is a high spatial resolution of land use and land cover map of the entire Brazilian Legal Amazon for the year 2008 and corresponding calculation... Tipo: Info:eu-repo/semantics/article Palavras-chave: Remote Sensing; Tropical Deforestation; TerraClass; Image Processing. Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0044-59672016000300291 Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks Agronomy Ganganagowdar, Narendra Veranagouda; Siddaramappa, Hareesha Katiganere.  A novel intelligent automated model to recognize and classify a cashew kernels using Artificial Neural Network (ANN). The model primarily intends to work on two phases. The phase one, built with a proposed method to extract features, which includes 16 morphological features and also 24 color features from the input cashew kernel images. In phase two, a Multilayer Perceptron ANN is being used to recognize and classify the given white wholes grades using back propagation learning algorithm. The proposed method achieves a classification accuracy of 88.93%. This study also reveals that the combination of morphological and color features outperforms rather using any one set of features separately to grade cashew kernels.  Tipo: Info:eu-repo/semantics/article Palavras-chave: Computer Science and Engineering; Computer Vision; Image Processing; Soft Computing White Wholes (WW) grade cashew kernel images; Feature extraction; Artificial neural networks; Classification. Ano: 2016 URL: http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861 Determination of the appropriate illumination wavelength for accurate and early detection of poplar tree leaf spot disease by using image processing technique CIGR Journal Sedighi, Shahryar; Kalantari, Davood; Shiukhy, Saeid; Rédl, Jozef. Leaf spot disease is one of the most common fungal diseases that cause immense and sometimes irreparable damage to poplar trees. Therefore, in order to prevent the development of this fungal disease and to reduce its losses, identification and elimination of its pathogen (Septoria fungi) is very important. In this regard, conventional methods for detection of fungal contamination are time-consuming, costly and difficult. Therefore in this study, in order to distinguish healthy leaves from infected ones, as well as determining the rate of infection progress, a modified image processing algorithm by using the Laplacian threshold was used. Based on the results obtained in this study, the effect of illumination wavelength on the percentage of disease... Tipo: Info:eu-repo/semantics/article Palavras-chave: Image Processing; Machine Vision; Poplar Tree; Wavelength. Ano: 2021 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/6601 Registros recuperados: 3 Primeira ... 1 ... Última

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