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| Zhao,Yun; Guindo,Mahamed L.; Xu,Xing; Shi,Xiang; Sun,Miao; He,Yong. |
| ABSTRACT We propose a segmentation algorithm for raisin extraction. The proposed approach consists of the following aspects. Deep learning is used to predict the number of raisins in each connected region, and the shape features such as the roundness, area, X-axis value for the centroid, Y-axis value for the centroid, axis length and perimeter of each region will be used to establish the prediction model. Morphological analysis, based on edge parameters including the polar axis, polar angle and angular velocity, is applied to search for the suitable break points that are useful for identifying the dividing lines between two adjacent raisins. To make our segmentation more accurate, some machine-learning algorithms such as the random forest (RF), support... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Raisin extraction; Segmentation algorithm; Deep learning; Image analysis; Food quality grading. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000500639 |
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| Barde, J.; Bonhommeau, Sylvain; Chassot, E.; Motah, B.. |
| ollecting data on aquatic biodiversity is very challenging because of the difficulty to access underwater ecosystems. Over the years, field surveys have become easier and cheaper with the development of low cost electronics. Commercial and recreational vessels, including sailboats, can now substantially complement expensive scientific surveys and arrays of observation buoys deployed across the world oceans (Pesant et al., 2015, Karsenti et al., 2011). Meanwhile, a large variety of marine animals such as birds, mammals, and fish have become data collection platforms for both biological and environmental parameters through the advent of archival tags. It becomes obvious that data collection in coastal and high seas will become more popular and that citizen... |
| Tipo: Text |
Palavras-chave: Citizen science; Ocean and coastal observing systems; Surfing; Action cameras; Coral reef mapping; Photogrammetry; Deep learning; R. |
| Ano: 2018 |
URL: http://archimer.ifremer.fr/doc/00450/56164/57712.pdf |
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| Thai-nghe, Nguyen; Thanh-hai, Nguyen; Chi Ngon, Nguyen. |
| Global climate change and water pollution effects have caused many problems to the farmers in fish/shrimp raising, for example, the shrimps/fishes had early died before harvest. How to monitor and manage quality of the water to help the farmers tackling this problem is very necessary. Water quality monitoring is important when developing IoT systems, especially for aquaculture and fisheries. By monitoring the real-time sensor data indicators (such as indicators of salinity, temperature, pH, and dissolved oxygen - DO) and forecasting them to get early warning, we can manage the quality of the water, thus collecting both quality and quantity in shrimp/fish raising. In this work, we introduce an architecture with a forecasting model for the IoT systems to... |
| Tipo: Text |
Palavras-chave: Forecasting model; Deep learning; Long-Short Term Memory (LSTM); Water quality indicators. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00646/75836/76830.pdf |
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| SOUSA, R. de C. P. de; SMIDERLE, O. J.; COSTA, P. da. |
| A tecnologia de visão artificial engloba uma série de métodos que podem ser empregados de forma individual ou integrados entre si para uso em várias áreas da ciência, principalmente em laboratórios, agilizando e inovando o processo de análises. No laboratório de sementes da Empresa Brasileira de Pesquisa Agropecuária, em Roraima, existe um instrumento com sistema semi-automatizado, que utiliza-se dessa tecnologia para análise de sementes e plântulas. Possibilita a obtenção de imagens de alta resolução e, coleta ao mesmo tempo, ampla gama de informações por semente. No entanto, o referido instrumento, veio configurado/calibrado de fábrica apenas para algumas espécies de grãos/sementes, como milho, soja, tabaco e trigo. Mas, aceita nova configuração para... |
| Tipo: Parte de livro |
Palavras-chave: Deep learning; SAS-PRO; Imagens; Nova metodologia. |
| Ano: 2022 |
URL: http://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/1139497 |
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