Data/hora: 14/06/2024 12:18:48

Biblioteca(s): | Embrapa Arroz e Feijão; Embrapa Soja.
Data corrente: | 22/07/2022
Data da última atualização: | 22/08/2022
Autoria: | PEREIRA, R. de C.; HIROSE, E.; CARVALHO, O. L. F. de; COSTA, R. M. da; BORGES, D. L.
Afiliação: | RUBENS DE CASTRO PEREIRA, CNPAF; EDSON HIROSE, CNPSO; OSMAR LUIZ FERREIRA DE CARVALHO, Universidade de Brasília; RONALDO MARTINS DA COSTA; DÍBIO LEANDRO BORGES, UNIVERSIDADE DE BRASÍLIA.
Título: | Detection and classification of whiteflies and development stages on soybean leaves images using an improved deep learning strategy.
Ano de publicação: | 2022
Fonte/Imprenta: | Computers and Electronics in Agriculture, v. 199, 107132, p. 1-12, Aug. 2022.
ISSN: | 0168-1699
DOI: | doi.org/10.1016/j.compag.2022.107132
Idioma: | Inglês
Conteúdo: | This paper presents a novel strategy to detect and classify adult whiteflies and five important related stages on images of detached soybean leaves. The whitefly Bemisia tabaci is a major pest in soybean crops, and by detecting, counting and differentiating its related life stages in field collected leaves control management decisions can be made. The proposed solution is based on a deep learning object detection algorithm (YOLOv4), modified into an specific new learning strategy with innovations on data augmentation, image mosaicking, and fusion of hypothesized object categories. A real and annotated dataset of images is provided from a controlled experiment infected with whitefly eggs having 121 images and 973 annotated objects. The experimental results showed a promising performance of the proposed system, reaching an f1-score of 0.87, in comparison with a single YOLOv4 algorithm that reached f1-score of 0.80. The overall strategy could be extended to work in other similar tasks for image based pest management.
Palavras-Chave: | Classificação; Detecção; Imagem digital; Visão computacional; Yolov4.
Thesagro: | Bemisia Tabaci; Identificação; Mosca Branca; Programa de Computador; Soja.
Thesaurus Nal: | Classification; Computer software; Computer vision; Detection; Digital images; Models.
Categoria do assunto: | O Insetos e Entomologia
Registro original: | Embrapa Arroz e Feijão (CNPAF)

Exemplares:

CNPAF - (36465-1) - UPC - AP - DD - 2022 -  - 2022 - DIS
CNPSO - (40498-1) - UPC - AP - DD -  -  -  - DIS