Detection of crown rot in wheat utilising near-infrared spectroscopy: towards remote and robotic sensing

Paper


Humpal, Jacob, McCarthy, Cheryl, Percy, Cassy and Thomasson, J. Alex. 2020. "Detection of crown rot in wheat utilising near-infrared spectroscopy: towards remote and robotic sensing." Thomasson, J. Alex and Torres-Rua, Alfonso F. (ed.) Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V (SPIE 2020). Online 27 Apr - 08 May 2020 United States. https://doi.org/10.1117/12.2557949
Paper/Presentation Title

Detection of crown rot in wheat utilising near-infrared spectroscopy: towards remote and robotic sensing

Presentation TypePaper
AuthorsHumpal, Jacob (Author), McCarthy, Cheryl (Author), Percy, Cassy (Author) and Thomasson, J. Alex (Author)
EditorsThomasson, J. Alex and Torres-Rua, Alfonso F.
Journal or Proceedings TitleProceedings of SPIE: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V
Journal Citation11414, pp. 1-7
Number of Pages7
Year2020
Place of PublicationUnited States
ISBN9781510636057
9781510636064
Digital Object Identifier (DOI)https://doi.org/10.1117/12.2557949
Web Address (URL) of Paperhttps://spie.org/Publications/Proceedings/Paper/10.1117/12.2557949
Conference/EventAutonomous 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.

KeywordsNIR, Spectroscopy, Crown Rot, Disease Detection
ANZSRC Field of Research 2020400909. 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

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Byline AffiliationsCentre for Agricultural Engineering
Centre for Crop Health
Texas A&M University, United States
Institution of OriginUniversity of Southern Queensland
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