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| 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 em periódico indexado (ALICE) |
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 |
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| 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 em periódico indexado (ALICE) |
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 |
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