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
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.
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