Improved image recognition via Synthetic Plants using 3D Modelling with Stochastic Variations
This research extends previous plant modelling using L-systems by means of a novel arrangement comprising synthetic plants and a refined global wheat dataset in combination with a synthetic inference application. The study demonstrates an application with direct recognition of real plant stereotypes...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | BIO Web of Conferences |
| Subjects: | |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2023/25/bioconf_icosia2023_06004.pdf |
| _version_ | 1797353042046091264 |
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| author | Napier Chris C. Cook David M. Armstrong Leisa Diepeveen Dean |
| author_facet | Napier Chris C. Cook David M. Armstrong Leisa Diepeveen Dean |
| author_sort | Napier Chris C. |
| collection | DOAJ |
| description | This research extends previous plant modelling using L-systems by means of a novel arrangement comprising synthetic plants and a refined global wheat dataset in combination with a synthetic inference application. The study demonstrates an application with direct recognition of real plant stereotypes, and augmentation via a plant-wide stochastic growth variation structure. The study showed that the automatic annotation and counting of wheat heads using the Global Wheat dataset images provides a time and cost saving over traditional manual approaches and neural networks. This study introduces a novel synthetic inference application using a plant-wide stochastic variation system, resulting in improved structural dataset hierarchy. The research demonstrates a significantly improved L-system that can more effectively and more accurately define and distinguish wheat crop characteristics. |
| first_indexed | 2024-03-08T13:25:25Z |
| format | Article |
| id | doaj.art-4d3e245728f04cd4be03ee266248b666 |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-03-08T13:25:25Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-4d3e245728f04cd4be03ee266248b6662024-01-17T14:58:04ZengEDP SciencesBIO Web of Conferences2117-44582023-01-01800600410.1051/bioconf/20238006004bioconf_icosia2023_06004Improved image recognition via Synthetic Plants using 3D Modelling with Stochastic VariationsNapier Chris C.0Cook David M.1Armstrong Leisa2Diepeveen Dean3School of Science, Edith Cowan UniversitySchool of Science, Edith Cowan UniversitySchool of Science, Edith Cowan UniversitySchool of Science, Edith Cowan UniversityThis research extends previous plant modelling using L-systems by means of a novel arrangement comprising synthetic plants and a refined global wheat dataset in combination with a synthetic inference application. The study demonstrates an application with direct recognition of real plant stereotypes, and augmentation via a plant-wide stochastic growth variation structure. The study showed that the automatic annotation and counting of wheat heads using the Global Wheat dataset images provides a time and cost saving over traditional manual approaches and neural networks. This study introduces a novel synthetic inference application using a plant-wide stochastic variation system, resulting in improved structural dataset hierarchy. The research demonstrates a significantly improved L-system that can more effectively and more accurately define and distinguish wheat crop characteristics.https://www.bio-conferences.org/articles/bioconf/pdf/2023/25/bioconf_icosia2023_06004.pdfsynthetic plantsstochastic modellingl-systemsglobal wheatinference |
| spellingShingle | Napier Chris C. Cook David M. Armstrong Leisa Diepeveen Dean Improved image recognition via Synthetic Plants using 3D Modelling with Stochastic Variations BIO Web of Conferences synthetic plants stochastic modelling l-systems global wheat inference |
| title | Improved image recognition via Synthetic Plants using 3D Modelling with Stochastic Variations |
| title_full | Improved image recognition via Synthetic Plants using 3D Modelling with Stochastic Variations |
| title_fullStr | Improved image recognition via Synthetic Plants using 3D Modelling with Stochastic Variations |
| title_full_unstemmed | Improved image recognition via Synthetic Plants using 3D Modelling with Stochastic Variations |
| title_short | Improved image recognition via Synthetic Plants using 3D Modelling with Stochastic Variations |
| title_sort | improved image recognition via synthetic plants using 3d modelling with stochastic variations |
| topic | synthetic plants stochastic modelling l-systems global wheat inference |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2023/25/bioconf_icosia2023_06004.pdf |
| work_keys_str_mv | AT napierchrisc improvedimagerecognitionviasyntheticplantsusing3dmodellingwithstochasticvariations AT cookdavidm improvedimagerecognitionviasyntheticplantsusing3dmodellingwithstochasticvariations AT armstrongleisa improvedimagerecognitionviasyntheticplantsusing3dmodellingwithstochasticvariations AT diepeveendean improvedimagerecognitionviasyntheticplantsusing3dmodellingwithstochasticvariations |