|
|
|
Registros recuperados: 10 | |
|
| |
|
|
BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, T. T.; SANTOS, P. M.. |
Abstract: Unmanned aerial vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more loosely monitored. With the advent of deep learning, and convolutional neural networks (CNNs) in particular, extracting relevant information from aerial images has become more effective. Despite the technological advancements in drone, imaging and machine learning technologies, the application of UAVs for cattle monitoring is far from being thoroughly studied, with many research gaps still remaining. In this context, the objectives of this study were threefold: (1) to... |
Tipo: Artigo de periódico |
Palavras-chave: Veículo aéreo não tripulado; Redes neurais; Drone; Aprendizado profundo; Convolutional neural networks; Deep learning; Canchim breed; Nelore breed; Gado de Corte; Gado Canchim; Gado Nelore; Cattle; Unmanned aerial vehicles. |
Ano: 2019 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1116449 |
| |
|
| |
|
|
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 |
| |
|
| |
|
|
BOCK, C. H.; BARBEDO, J. G. A.; DEL PONTE, E. M.; BOHNENKAMP, D.; MAHLEIN, A. K.. |
Abstract. The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and... |
Tipo: Artigo de periódico |
Palavras-chave: Inteligência artificial; Aprendizado de máquina; Dispositivo móvel; Tecnologias digitais; Aprendizado profundo; Precisão; Acurácia; Severidade da doença; Machine learning; Assessment; Sensor; Mobile device; Digital technologies; Deep learning; Phenotyping; Doença de Planta; Precision agriculture; Plant diseases and disorders; Artificial intelligence; Disease severity; Accuracy; Precision. |
Ano: 2020 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1122199 |
| |
|
| |
|
|
SOUSA, M. A. de; SOUZA, K. X. S. de; CAMARGO NETO, J.; TERNES, S.; YANO, I. H.. |
RESUMO - A agropecuária é uma das mais importantes fontes de riqueza no Brasil. Dentro desse contexto, se destaca o cultivo das laranjas, principalmente na região de São Paulo e do Triângulo Mineiro. Infelizmente, o processo de estimativa da quantidade de frutos é custoso, assim, essa pesquisa tem como objetivo analisar por meio de visão computacional e de aprendizado profundo se essas técnicas geram resultados satisfatórios para identificar os frutos por fotografias. Caso apresente um bom desempenho, esta tecnologia poderá ser utilizada para prever a quantidade de laranjas em árvores. |
Tipo: Anais e Proceedings de eventos |
Palavras-chave: Visão computacional; Redes neurais; Aprendizado profundo; Rede SSD; Cultura da laranja; Deep learning; SSD network; Computer vision; Neural networks. |
Ano: 2021 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1135159 |
| |
|
| |
|
| |
Registros recuperados: 10 | |
|
|
|