Data/hora: 08/06/2024 02:07:45

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 06/10/2020
Data da última atualização: | 08/10/2020
Autoria: | BARBEDO, J. G. A.; CASTRO, G. B.
Afiliação: | JAYME GARCIA ARNAL BARBEDO, CNPTIA; GUILHERME BARROS CASTRO, CromAI, São Paulo.
Título: | A study on CNN-based detection of psyllids in sticky traps using multiple image data sources.
Ano de publicação: | 2020
Fonte/Imprenta: | AI, v. 1, n. 2, p. 198-208, June 2020.
DOI: | https://doi.org/10.3390/ai1020013
Idioma: | Inglês
Conteúdo: | Abstract: 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.
Palavras-Chave: | Aprendizado profundo; Citrus huanglongbing; Convolutional Neural Networks; Data variety; Deep learning; HLB; Imagens digitais; Model robustness; Redes neurais; Redes Neurais Convolucionais; Robustez de modelo; Variedade de dados.
Thesaurus Nal: | Citrus; Digital images; Neural networks.
Categoria do assunto: | --
URL: | http://ainfo.cnptia.embrapa.br/digital/bitstream/item/216450/1/AP-CNN-psyllids-2020.pdf
Registro original: | Embrapa Agricultura Digital (CNPTIA)
Exemplares: | CNPTIA - (20506-1) - UPC - AP - DD -  -  -  - DIS