- Title
- Enhanced plant species and early water stress detection using visible and near-infrared spectra
- Creator
- Brown, Dane L
- Creator
- Poole, Louise C
- Subject
- To be catalogued
- Date Issued
- 2023
- Date
- 2023
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/463384
- Identifier
- vital:76404
- Identifier
- xlink:href="https://link.springer.com/chapter/10.1007/978-981-19-9819-5_55"
- Description
- This paper reports on recent successful work aimed at preventing crop loss and failure before visible symptoms are present. Food security is critical, especially after the COVID-19 pandemic. Detecting early-stage plant stresses in agriculture is essential in minimizing crop damage and maximizing yield. Identification of both the stress type and cause is a non-trivial multitask classification problem. However, the application of spectroscopy to early plant diseases and stress detection has become viable with recent advancements in technology. Suitable frequencies of the electromagnetic spectrum and machine learning algorithms were thus first investigated. This guided data collection in two sessions by capturing standard visible images in contrast with images from multiple spectra (VIS-IR). These images consisted of six plant species that were carefully monitored from healthy to dehydrated stages. Promising results were achieved using VIS-IR compared to standard visible images on three deep learning architectures. Statistically, significant accuracy improvements were shown for VIS-IR for early dehydration detection, where ResNet-44 modelling of VIS-IR input yielded 92.5% accuracy compared to 77.5% on visible input on general plant species. Moreover, ResNet-44 achieved good species separation.
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (14 pages)
- Format
- Publisher
- SpringerLink
- Language
- English
- Relation
- In Computational Vision and Bio-Inspired Computing: Proceedings of ICCVBIC 2022
- Relation
- Brown, D. and Poole, L., 2023. Enhanced plant species and early water stress detection using visible and near-infrared spectra. In Computational Vision and Bio-Inspired Computing: Proceedings of ICCVBIC 2022 (pp. 765-779). Singapore: Springer Nature Singapore
- Relation
- In Computational Vision and Bio-Inspired Computing: Proceedings of ICCVBIC 2022 p. 765 2023 2194-5365
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the SpringerLink Terms of Use Statement ( https://link.springer.com/termsandconditions)
- Rights
- Closed Access
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- Visitors: 91
- Downloads: 3
Collections
SDG 12. Responsible Consumption and Production
Brown, Dane A (Associate Prof)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Enhanced plant species and early water stress detection using visible and near-infrared spectra.pdf | 730 KB | Adobe Acrobat PDF | View Details Download |