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.

RAS ID

62403

Document Type

Conference Proceeding

Date of Publication

12-14-2023

Volume

80

School

School of Science

Creative Commons License

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

Publisher

EDP Sciences

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

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Link to publisher version (DOI)

10.1051/bioconf/20238006004