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

Humpal, Jacob ORCID: https://orcid.org/0000-0002-7525-5169 and McCarthy, Cheryl ORCID: https://orcid.org/0000-0003-3297-7425 and Percy, Cassy ORCID: https://orcid.org/0000-0002-7807-6764 and Thomasson, J. Alex (2020) Detection of crown rot in wheat utilising near-infrared spectroscopy: towards remote and robotic sensing. In: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V (SPIE 2020), 27 April - 8 May 2020, Online.

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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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: File reproduced in accordance with the copyright policy of the publisher/author.
Faculty/School / Institute/Centre: Current - Institute for Advanced Engineering and Space Sciences - Centre for Agricultural Engineering (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Crop Health (24 Mar 2014 -)
Date Deposited: 01 Aug 2022 03:19
Last Modified: 31 Aug 2022 03:16
Uncontrolled Keywords: NIR, Spectroscopy, Crown Rot, Disease Detection
Fields of Research (2020): 40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400909 Photonic and electro-optical devices, sensors and systems (excl. communications)
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3099 Other agricultural, veterinary and food sciences > 309999 Other agricultural, veterinary and food sciences not elsewhere classified
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3004 Crop and pasture production > 300409 Crop and pasture protection (incl. pests, diseases and weeds)
Socio-Economic Objectives (2020): 18 ENVIRONMENTAL MANAGEMENT > 1806 Terrestrial systems and management > 180602 Control of pests, diseases and exotic species in terrestrial environments
26 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 2603 Grains and seeds > 260312 Wheat
Identification Number or DOI: https://doi.org/10.1117/12.2557949
URI: http://eprints.usq.edu.au/id/eprint/50539

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