Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1125315
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dc.contributor.authorBARBEDO, J. G. A.
dc.contributor.authorCASTRO, G. B.
dc.date.accessioned2020-10-07T09:14:58Z-
dc.date.available2020-10-07T09:14:58Z-
dc.date.created2020-10-06
dc.date.issued2020
dc.identifier.citationAI, v. 1, n. 2, p. 198-208, June 2020.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1125315-
dc.descriptionAbstract: Deep learning architectures like Convolutional Neural Networks (CNNs) are quickly becoming the standard for detecting and counting objects in digital images. However, most of the experiments found in the literature train and test the neural networks using data from a single image source, making it difficult to infer how the trained models would perform under a more diverse context. The objective of this study was to assess the robustness of models trained using data from a varying number of sources. Nine different devices were used to acquire images of yellow sticky traps containing psyllids and a wide variety of other objects, with each model being trained and tested using different data combinations. The results from the experiments were used to draw several conclusions about how the training process should be conducted and how the robustness of the trained models is influenced by data quantity and variety.
dc.language.isoeng
dc.rightsopenAccesseng
dc.subjectAprendizado profundo
dc.subjectRobustez de modelo
dc.subjectVariedade de dados
dc.subjectRedes neurais
dc.subjectRedes Neurais Convolucionais
dc.subjectCitrus huanglongbing
dc.subjectHLB
dc.subjectImagens digitais
dc.subjectDeep learning
dc.subjectModel robustness
dc.subjectData variety
dc.subjectConvolutional Neural Networks
dc.titleA study on CNN-based detection of psyllids in sticky traps using multiple image data sources.
dc.typeArtigo de periódico
dc.subject.nalthesaurusCitrus
dc.subject.nalthesaurusNeural networks
dc.subject.nalthesaurusDigital images
riaa.ainfo.id1125315
riaa.ainfo.lastupdate2020-10-08 -03:00:00
dc.identifier.doihttps://doi.org/10.3390/ai1020013
dc.contributor.institutionJAYME GARCIA ARNAL BARBEDO, CNPTIA; GUILHERME BARROS CASTRO, CromAI, São Paulo.
Appears in Collections:Artigo em periódico indexado (CNPTIA)

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