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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9340076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT yangmeng algorithmforappearancesimulationofplantdiseasesbasedonsymptomclassification AT dingshu algorithmforappearancesimulationofplantdiseasesbasedonsymptomclassification |