Detalhes bibliográficos
| Ano de defesa: |
2018 |
| Autor(a) principal: |
Vilas Boas, Lenilson Lemos
 |
| Orientador(a): |
Nöth, Winfried |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Pontifícia Universidade Católica de São Paulo
|
| Programa de Pós-Graduação: |
Programa de Estudos Pós-Graduados em Tecnologias da Inteligência e Design Digital
|
| Departamento: |
Faculdade de Ciências Exatas e Tecnologia
|
| País: |
Brasil
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
https://tede2.pucsp.br/handle/handle/21763
|
Resumo: |
Systems that use Computer Vision Application Program Interfaces (APIs) can learn and identify patterns and thus perform associations to retrieve additional data. They are able to obtain results much faster than any human agent is. The study uses three computational vision APIs and evaluates their application in the identification of four plant leave diseases. Based on a corpus of fifty images, the API training was conducted in two stages, the first with thirty images and the second training with twenty more images. After the two trainings, the results of the diseases were collected for each API studied, which made it possible to evaluate the identification capacity and its evolution of learning after each training. The results corroborated the hypothesis. They gave evidence of the feasibility of identification of plant leaf diseases by means of computer vision APIs |
