Data/hora: 14/06/2024 12:20:00

Biblioteca(s): | Embrapa Uva e Vinho.
Data corrente: | 16/12/2020
Data da última atualização: | 20/12/2022
Autoria: | MELO, R. F. de; LIMA, G. L. de; CORRÊA, G. R.; ZATT, B.; AGUIAR, M. S. de; NACHTIGALL, G. R.; ARAÚJO, R. M.
Afiliação: | RAMÁSIO FERREIRA DE MELO; GUSTAVO LAMEIRÃO DE LIMA; GUILHERME RIBEIRO CORRÊA; BRUNO ZATT; MARILTON SANCHOTENE DE AGUIAR; GILMAR RIBEIRO NACHTIGALL, CNPUV; RICARDO MATSUMURA ARAÚJO.
Título: | Diagnosis of apple fruit diseases in the wild with Mask R-CNN.
Ano de publicação: | 2020
Fonte/Imprenta: | In: CERRI, R.; PRATI, R. C. (Eds). Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science, vol 12319. Springer, 2020. p. 256?270.
DOI: | https://doi.org/10.1007/978-3-030-61377-8_18
Idioma: | Português
Conteúdo: | A major challenge in image classification tasks using Machine Learning, and in particular when using deep neural networks, is domain shifting in deployment. This happens when images during usage are capture in different conditions from those used during training. In this paper, we show that despite previous works on the diagnosis of apple tree diseases using standard Convolutional Neural Networks displaying high accuracy, they do so only when no domain shift is present. When the trained model is asked to classify photos of apples taken in the wild, a 22% reduction in F1 score is observed. We propose to treat the task as a segmentation problem and test two different approaches, showing that using Mask R-CNN allows not only to improve performance in the original domain by 3%, but also significantly reduce losses in the new domain (only 6% reduction in F1 score). We establish segmentation as an important alternative towards improving diagnosis of apple tree diseases from photos.
Palavras-Chave: | Apple fruits; Deep learning; Instance segmentation.
Categoria do assunto: | F Plantas e Produtos de Origem Vegetal
Registro original: | Embrapa Uva e Vinho (CNPUV)
Exemplares: | CNPUV - (18567-1) - UPC - PL - DD -  -  -  - DIS