Application of Image Processing and Advanced Intelligent Computing for Determining Stage of Asian Rust in Soybean Plants

Autor(es)

Neves, Ricardo A. ; Cruvinel, Paulo E. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 16TH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2022); v. N/A, p. 7-pg., 2022-01-01.

Resumo

This paper presents a new method that uses the advanced techniques of digital image processing and computational intelligence for monitoring and identifying the stages of Asian rust (Phakopsora pachyrhizi) in soybean plants (Glycine max (L.) Merril). Its establishment included organization and structuring of digital images of soybean plant leaves, image preprocessing steps, and segmentation based on the phenomenology of the disease development process with a semantics approach involving agricultural evaluation. The method also required recognition of the patterns appearing on leaves due to the presence of the disease as well as machine learning using a support vector machine for the classification and interpretation of the stages and their evolutions. For the stage of pattern recognition, the techniques of feature extraction scale-invariant feature transform, Hu invariant moments, and histogram of oriented gradients were used and principal component analysis was conducted for the dimensionality reduction of the integrated vector of the features. The results of the application of the method showed potential for the monitoring and identification of the stages of the disease for determining the subsidies in the decision-making of production systems by agricultural producers. (AU)

• Processo FAPESP: 17/19350-2 - Ferramenta digital avançada para o gerenciamento de riscos agrícolas
• Beneficiário: Paulo Estevão Cruvinel
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE