Computer vision applied to food and agricultural products
ABSTRACT Computer vision (CV) has been applied for years to automate many human activities. It is one of the key technologies for the modernization of the agri-food industry towards the fourth industrial revolution (Industry 4.0). In the agricultural sector, CV systems are applied to automate or obtain information from many agricultural tasks such as planting, cultivation, farm management, disease control, weed control or robotic harvesting. It is also widely used in postharvest to automate and obtain objective information in processes such as quality control and evaluation, damage detection, classification of fruits or vegetables in commercial categories or composition analysis. One of the main advantages is the ability of this technology to obtain information in regions of the spectrum that are invisible to the human eye. An example is the case of hyperspectral imaging systems. These systems generate a large amount of data that needs to be processed efficiently, creating robust and repeatable statistical models that allow the technology to be implemented at an industrial level. To achieve this, it is necessary to couple CV systems with advanced artificial intelligence tools such as machine learning or deep learning. The objective of this work is to review the latest advances in CV systems applied to food and agricultural products and processes.
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Universidade Federal do Ceará
2020
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oai:scielo:S1806-669020200005004162021-08-17Computer vision applied to food and agricultural productsFracarolli,Juliana AparecidaPavarin,Fernanda Fernandes AdimariCastro,WilsonBlasco,Jose Digital images Machine vision Agriculture 4.0 Machine learning Artificial intelligence ABSTRACT Computer vision (CV) has been applied for years to automate many human activities. It is one of the key technologies for the modernization of the agri-food industry towards the fourth industrial revolution (Industry 4.0). In the agricultural sector, CV systems are applied to automate or obtain information from many agricultural tasks such as planting, cultivation, farm management, disease control, weed control or robotic harvesting. It is also widely used in postharvest to automate and obtain objective information in processes such as quality control and evaluation, damage detection, classification of fruits or vegetables in commercial categories or composition analysis. One of the main advantages is the ability of this technology to obtain information in regions of the spectrum that are invisible to the human eye. An example is the case of hyperspectral imaging systems. These systems generate a large amount of data that needs to be processed efficiently, creating robust and repeatable statistical models that allow the technology to be implemented at an industrial level. To achieve this, it is necessary to couple CV systems with advanced artificial intelligence tools such as machine learning or deep learning. The objective of this work is to review the latest advances in CV systems applied to food and agricultural products and processes.info:eu-repo/semantics/openAccessUniversidade Federal do CearáRevista Ciência Agronômica v.51 n.spe 20202020-01-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500416en10.5935/1806-6690.20200087 |
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Fracarolli,Juliana Aparecida Pavarin,Fernanda Fernandes Adimari Castro,Wilson Blasco,Jose |
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Fracarolli,Juliana Aparecida Pavarin,Fernanda Fernandes Adimari Castro,Wilson Blasco,Jose Computer vision applied to food and agricultural products |
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Fracarolli,Juliana Aparecida Pavarin,Fernanda Fernandes Adimari Castro,Wilson Blasco,Jose |
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Fracarolli,Juliana Aparecida |
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Computer vision applied to food and agricultural products |
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Computer vision applied to food and agricultural products |
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Computer vision applied to food and agricultural products |
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Computer vision applied to food and agricultural products |
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Computer vision applied to food and agricultural products |
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computer vision applied to food and agricultural products |
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ABSTRACT Computer vision (CV) has been applied for years to automate many human activities. It is one of the key technologies for the modernization of the agri-food industry towards the fourth industrial revolution (Industry 4.0). In the agricultural sector, CV systems are applied to automate or obtain information from many agricultural tasks such as planting, cultivation, farm management, disease control, weed control or robotic harvesting. It is also widely used in postharvest to automate and obtain objective information in processes such as quality control and evaluation, damage detection, classification of fruits or vegetables in commercial categories or composition analysis. One of the main advantages is the ability of this technology to obtain information in regions of the spectrum that are invisible to the human eye. An example is the case of hyperspectral imaging systems. These systems generate a large amount of data that needs to be processed efficiently, creating robust and repeatable statistical models that allow the technology to be implemented at an industrial level. To achieve this, it is necessary to couple CV systems with advanced artificial intelligence tools such as machine learning or deep learning. The objective of this work is to review the latest advances in CV systems applied to food and agricultural products and processes. |
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Universidade Federal do Ceará |
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2020 |
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http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500416 |
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AT fracarollijulianaaparecida computervisionappliedtofoodandagriculturalproducts AT pavarinfernandafernandesadimari computervisionappliedtofoodandagriculturalproducts AT castrowilson computervisionappliedtofoodandagriculturalproducts AT blascojose computervisionappliedtofoodandagriculturalproducts |
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