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	Provedor de dados Repositório Alice (3) Autor BARBEDO, J. G. A. (2) CESARO JÚNIOR, T. de (1) DEL PONTE, E. M. (1) DI DOMÊNICO, J. R. (1) GONÇALVES, J. P. (1) KOENIGKAN, L. V. (1) LAU, D. (1) PINTO, F. A. C. (1) QUEIROZ, D. M. (1) RIEDER, R. (1) SANTOS, P. M. (1) VILLAR, F. M. M. (1) Mais... Palavra-chave Convolutional neural network (3) Aprendizado profundo (2) Aphids (1) Aprendizado de máquina (1) Artificial intelligence (1) Cattle (1) Deep learning (1) Doença de Planta (1) Fitopatometria (1) Gado (1) Image segmentation (1) Inteligência artificial (1) Machine learning (1) Mask r-cnn (1) Neural networks (1) Object detection (1) Pest detection (1) Phytopathometry (1) Plant diseases and disorders (1) Rede neural convolucional (1) Mais... Tipo do documento Artigo de periódico (3) Ano 2021 (2) 2020 (1) País Brazil (3) Idioma Inglês (3)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 3 Primeira ... 1 ... Última Cattle detection using oblique UAV images. Repositório Alice BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, P. M.. The evolution in imaging technologies and artificial intelligence algorithms, coupled with improvements in UAV technology, has enabled the use of unmanned aircraft in a wide range of applications. The feasibility of this kind of approach for cattle monitoring has been demonstrated by several studies, but practical use is still challenging due to the particular characteristics of this application, such as the need to track mobile targets and the extensive areas that need to be covered in most cases. The objective of this study was to investigate the feasibility of using a tilted angle to increase the area covered by each image. Deep Convolutional Neural Networks (Xception architecture) were used to generate the models for animal detection. Three experiments... Tipo: Artigo de periódico Palavras-chave: Redes neurais; Redes neurais convolucionais; Aprendizado profundo; Veículos aéreos não tripulados; Convolutional neural network; Deep learning; Gado; Unmanned aerial vehicles; Cattle. Ano: 2020 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1127885 Deep learning architectures for semantic segmentation and automatic estimation of severity of foliar symptoms caused by diseases or pests. Repositório Alice GONÇALVES, J. P.; PINTO, F. A. C.; QUEIROZ, D. M.; VILLAR, F. M. M.; BARBEDO, J. G. A.; DEL PONTE, E. M.. Colour-thresholding digital imaging methods are generally accurate for measuring the percentage of foliar area affected by disease or pests (severity), but they perform poorly when scene illumination and background are not uniform. In this study, six convolutional neural network (CNN) architectures were trained for semantic segmentation in images of individual leaves exhibiting necrotic lesions and/or yellowing, caused by the insect pest coffee leaf miner (CLM), and two fungal diseases: soybean rust (SBR) and wheat tan spot (WTS). All images were manually annotated for three classes: leaf background (B), healthy leaf (H) and injured leaf (I). Precision, recall, and Intersection over Union (IoU) metrics in the test image set were the highest for B, followed... Tipo: Artigo de periódico Palavras-chave: Aprendizado profundo; Fitopatometria; Inteligência artificial; Aprendizado de máquina; Rede neural convolucional; Segmentação de imagem; Phytopathometry; Machine learning; Convolutional neural network; Image segmentation; Doença de Planta; Artificial intelligence; Plant diseases and disorders; Neural networks. Ano: 2021 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1134326 InsectCV: a system for insect detection in the lab from trap images. Repositório Alice CESARO JÚNIOR, T. de; RIEDER, R.; DI DOMÊNICO, J. R.; LAU, D.. Advances in artificial intelligence, computer vision, and high-performance computing have enabled the creation of efficient solutions to monitor pests and identify plant diseases. In this context, we present InsectCV, a system for automatic insect detection in the lab from scanned trap images. This study considered the use of Moericke-type traps to capture insects in outdoor environments. Each sample can contain hundreds of insects of interest, such as aphids, parasitoids, thrips, and flies. The presence of debris, superimposed objects, and insects in varied poses is also common. To develop this solution, we used a set of 209 grayscale images containing 17,908 labeled insects. We applied the Mask R-CNN method to generate the model and created three web... Tipo: Artigo de periódico Palavras-chave: Convolutional neural network; Mask r-cnn; Object detection; Pest detection; Aphids; Warning systems. Ano: 2021 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1137367 Registros recuperados: 3 Primeira ... 1 ... Última

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