Detection of crown rot in wheat utilising near-infrared spectroscopy: towards remote and robotic sensing
Paper
Paper/Presentation Title | Detection of crown rot in wheat utilising near-infrared spectroscopy: towards remote and robotic sensing |
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Presentation Type | Paper |
Authors | Humpal, Jacob (Author), McCarthy, Cheryl (Author), Percy, Cassy (Author) and Thomasson, J. Alex (Author) |
Editors | Thomasson, J. Alex and Torres-Rua, Alfonso F. |
Journal or Proceedings Title | Proceedings of SPIE: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V |
Journal Citation | 11414, pp. 1-7 |
Number of Pages | 7 |
Year | 2020 |
Place of Publication | United States |
ISBN | 9781510636057 |
9781510636064 | |
Digital Object Identifier (DOI) | https://doi.org/10.1117/12.2557949 |
Web Address (URL) of Paper | https://spie.org/Publications/Proceedings/Paper/10.1117/12.2557949 |
Conference/Event | Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V (SPIE 2020) |
Event Details | Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V (SPIE 2020) Event Date 27 Apr 2020 to end of 08 May 2020 Event Location Online |
Abstract | Forty percent wheat yield reduction is reported globally due to crown rot (Fusarium pseudograminearum). An emerging approach for sensor-based disease discrimination is the use of spectral reflectance with combinations of wavebands and varying bandwidths, which has potential to reduce the impact of environmental factors on spectral sensitivity detection accuracy. Transferring such technology from a laboratory to field environment presents challenges, particularly in regard to producing adequately robust models. An experiment was conducted in which near-infrared spectral reflectance data was captured in a glasshouse environment, for cultivars of spring bread wheat with varying resistances to F. pseudograminearum. A contact sensor sensitive to nearinfrared (900–1700 nm) wavebands was used. Raw sensor data was calibrated and transformed, allowing for variable waveband size. Optimised machine learning disease identification models were compared across the nine weeks following inoculation with F. pseudograminearum. Models were compared for the ability to accurately detect crown rot across weeks. The results show crown rot detection ability with accuracies ranging from 49–74%, as well as a temporal patterning effect as the season progresses. An artificial neural network classifier (ANN) performed best with a top accuracy of 74.14%, of the six machine learning algorithms trialed. Waveform differences between plus and minus treatments indicate that the sensing approach has potential to be scaled to a camera-based system for use on remote sensing platforms. Further work is being conducted to understand the viability of such an approach, which is an important step towards both robotic and RPA-based disease discrimination. |
Keywords | NIR, Spectroscopy, Crown Rot, Disease Detection |
ANZSRC Field of Research 2020 | 400909. Photonic and electro-optical devices, sensors and systems (excl. communications) |
309999. Other agricultural, veterinary and food sciences not elsewhere classified | |
300409. Crop and pasture protection (incl. pests, diseases and weeds) | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Centre for Agricultural Engineering |
Centre for Crop Health | |
Texas A&M University, United States | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q796x/detection-of-crown-rot-in-wheat-utilising-near-infrared-spectroscopy-towards-remote-and-robotic-sensing
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