Improved image recognition via synthetic plants using 3D modelling with stochastic variations

Authors

Chris C. Napier, Edith Cowan UniversityFollow
David M. Cook, Edith Cowan UniversityFollow
Leisa Armstrong, Edith Cowan UniversityFollow
Dean Diepeveen, Edith Cowan UniversityFollow

Document Type

Conference Proceeding

Publication Title

4th International Conference on Smart and Innovative Agriculture (ICoSIA 2023)

Volume

80

Publisher

EDP Sciences

School

School of Science

RAS ID

62403

Comments

Napier, C. C., Cook, D. M., Armstrong, L., & Diepeveen, D. (2023). Improved image recognition via synthetic plants using 3D modelling with stochastic variations. In 4th International Conference on Smart and Innovative Agriculture (ICoSIA 2023), 80, article 06004. https://doi.org/10.1051/bioconf/20238006004

Abstract

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.

DOI

10.1051/bioconf/20238006004

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.