Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)

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A Synthetic Wheat L-System to Accurately Detect and Visualise Wheat Head Anomalies

Authors
Chris C. Napier1, David M. Cook1, *, Leisa Armstrong1, Dean Diepeveen1, 2
1School of Science, Edith Cowan University, Joondalup, 6027, Australia
2Department of Primary Industry Resource Development (DPIRD), South Perth, Australia
*Corresponding author. Email: d.cook@ecu.edu.au
Corresponding Author
David M. Cook
Available Online 22 May 2023.
DOI
10.2991/978-94-6463-122-7_36How to use a DOI?
Keywords
Synthetic Wheat; L-system; Global Wheat; Blender; COCO
Abstract

Greater knowledge of wheat crop phenology and growth and improvements in measurement are beneficial to wheat agronomy and productivity. This is constrained by a lack of public plant datasets. Collecting plant data is expensive and time consuming and methods to augment this with synthetic data could address this issue. This paper describes a cost-effective and accurate Synthetic Wheat dataset which has been created by a novel L-system, based on technological advances in cameras and deep learning. The dataset images have been automatically created, categorised, masked and labelled, and used to successfully train a synthetic neural network. This network has been shown to accurately recognise wheat in pasture images taken from the Global Wheat dataset, which provides for the ongoing interest in the phenotyping of wheat characteristics around the world. The proven Mask R-CNN and Detectron2 frameworks have been used, and the created network is based on the public COCO format. The research question is “How can L-system knowledge be used to create an accurate synthetic wheat dataset and to make cost-effective wheat crop measurements?”.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)
Series
Advances in Biological Sciences Research
Publication Date
22 May 2023
ISBN
10.2991/978-94-6463-122-7_36
ISSN
2468-5747
DOI
10.2991/978-94-6463-122-7_36How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Chris C. Napier
AU  - David M. Cook
AU  - Leisa Armstrong
AU  - Dean Diepeveen
PY  - 2023
DA  - 2023/05/22
TI  - A Synthetic Wheat L-System to Accurately Detect and Visualise Wheat Head Anomalies
BT  - Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)
PB  - Atlantis Press
SP  - 379
EP  - 391
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-122-7_36
DO  - 10.2991/978-94-6463-122-7_36
ID  - Napier2023
ER  -