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Algorithm for appearance simulation of plant diseases based on symptom classification

Plant disease visualization simulation belongs to an important research area at the intersection of computer application technology and plant pathology. However, due to the variety of plant diseases and their complex causes, how to achieve realistic, flexible, and universal plant disease simulation...

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Detalles Bibliográficos
Autores principales: Yang, Meng, Ding, Shu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9340076/
https://www.ncbi.nlm.nih.gov/pubmed/35923887
http://dx.doi.org/10.3389/fpls.2022.935157
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author Yang, Meng
Ding, Shu
author_facet Yang, Meng
Ding, Shu
author_sort Yang, Meng
collection PubMed
description Plant disease visualization simulation belongs to an important research area at the intersection of computer application technology and plant pathology. However, due to the variety of plant diseases and their complex causes, how to achieve realistic, flexible, and universal plant disease simulation is still a problem to be explored in depth. Based on the principles of plant disease prediction, a time-varying generic model of diseases affected by common environmental factors was established, and interactive environmental parameters such as temperature, humidity, and time were set to express the plant disease spread and color change processes through a unified calculation. Using the apparent symptoms as the basis for plant disease classification, simulation algorithms for different symptom types were propose. The composition of disease spots was deconstructed from a computer simulation perspective, and the simulation of plant diseases with symptoms such as discoloration, powdery mildew, ring pattern, rust spot, and scatter was realized based on the combined application of visualization techniques such as image processing, noise optimization and texture synthesis. To verify the effectiveness of the algorithm, a simulation similarity test method based on deep learning was proposed to test the similarity with the recognition accuracy of symptom types, and the overall accuracy reaches 87%. The experimental results showed that the algorithm in this paper can realistically and effectively simulate five common plant disease forms. It provided a useful reference for the popularization of plant disease knowledge and visualization teaching, and also had certain research value and application value in the fields of film and television advertising, games, and entertainment.
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spelling pubmed-93400762022-08-02 Algorithm for appearance simulation of plant diseases based on symptom classification Yang, Meng Ding, Shu Front Plant Sci Plant Science Plant disease visualization simulation belongs to an important research area at the intersection of computer application technology and plant pathology. However, due to the variety of plant diseases and their complex causes, how to achieve realistic, flexible, and universal plant disease simulation is still a problem to be explored in depth. Based on the principles of plant disease prediction, a time-varying generic model of diseases affected by common environmental factors was established, and interactive environmental parameters such as temperature, humidity, and time were set to express the plant disease spread and color change processes through a unified calculation. Using the apparent symptoms as the basis for plant disease classification, simulation algorithms for different symptom types were propose. The composition of disease spots was deconstructed from a computer simulation perspective, and the simulation of plant diseases with symptoms such as discoloration, powdery mildew, ring pattern, rust spot, and scatter was realized based on the combined application of visualization techniques such as image processing, noise optimization and texture synthesis. To verify the effectiveness of the algorithm, a simulation similarity test method based on deep learning was proposed to test the similarity with the recognition accuracy of symptom types, and the overall accuracy reaches 87%. The experimental results showed that the algorithm in this paper can realistically and effectively simulate five common plant disease forms. It provided a useful reference for the popularization of plant disease knowledge and visualization teaching, and also had certain research value and application value in the fields of film and television advertising, games, and entertainment. Frontiers Media S.A. 2022-07-18 /pmc/articles/PMC9340076/ /pubmed/35923887 http://dx.doi.org/10.3389/fpls.2022.935157 Text en Copyright © 2022 Yang and Ding. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Yang, Meng
Ding, Shu
Algorithm for appearance simulation of plant diseases based on symptom classification
title Algorithm for appearance simulation of plant diseases based on symptom classification
title_full Algorithm for appearance simulation of plant diseases based on symptom classification
title_fullStr Algorithm for appearance simulation of plant diseases based on symptom classification
title_full_unstemmed Algorithm for appearance simulation of plant diseases based on symptom classification
title_short Algorithm for appearance simulation of plant diseases based on symptom classification
title_sort algorithm for appearance simulation of plant diseases based on symptom classification
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9340076/
https://www.ncbi.nlm.nih.gov/pubmed/35923887
http://dx.doi.org/10.3389/fpls.2022.935157
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