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The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat

The Plant Accelerator, Australian Plant Phenomics Facility, Waite Campus, School of Agriculture, Food and Wine, University of Adelaide, Building WT 40, Hartley Grove, Urrbrae, SA 5064, Australia
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Academic Editor: Mathew G. Pelletier
AgriEngineering 2021, 3(4), 924-941; https://doi.org/10.3390/agriengineering3040058 (registering DOI)
Received: 30 September 2021 / Revised: 19 November 2021 / Accepted: 22 November 2021 / Published: 25 November 2021
(This article belongs to the Special Issue Hyperspectral Imaging Technique in Agriculture)
Crown rot disease is caused by Fusarium pseudograminearum and is one of the major stubble-soil fungal diseases threatening the cereal industry globally. It causes failure of grain establishment, which brings significant yield loss. Screening crops affected by crown rot is one of the key tools to manage crown rot, because it is necessary to understand disease infection conditions, identify the severity of infection, and discover potential resistant varieties. However, screening crown rot is challenging as there are no clear visible symptoms on leaves at early growth stages. Hyperspectral imaging (HSI) technologies have been successfully used to better understand plant health and disease incidence, including light absorption rate, water and nutrient distribution, and disease classification. This suggests HSI imaging technologies may be used to detect crown rot at early growing stages, however, related studies are limited. This paper briefly describes the symptoms of crown rot disease and traditional screening methods with their limitations. It, then, reviews state-of-art imaging technologies for disease detection, from color imaging to hyperspectral imaging. In particular, this paper highlights the suitability of hyperspectral-based screening methods for crown rot disease. A hypothesis is presented that HSI can detect crown-rot-infected plants before clearly visible symptoms on leaves by sensing the changes of photosynthesis, water, and nutrients contents of plants. In addition, it describes our initial experiment to support the hypothesis and further research directions are described. View Full-Text
Keywords: crown rot disease; plant phenotyping; hyperspectral imaging; computer vison; machine learning crown rot disease; plant phenotyping; hyperspectral imaging; computer vison; machine learning
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MDPI and ACS Style

Xie, Y.; Plett, D.; Liu, H. The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat. AgriEngineering 2021, 3, 924-941. https://doi.org/10.3390/agriengineering3040058

AMA Style

Xie Y, Plett D, Liu H. The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat. AgriEngineering. 2021; 3(4):924-941. https://doi.org/10.3390/agriengineering3040058

Chicago/Turabian Style

Xie, Yiting, Darren Plett, and Huajian Liu. 2021. "The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat" AgriEngineering 3, no. 4: 924-941. https://doi.org/10.3390/agriengineering3040058

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