Preliminary evaluation of real-time sensing of harvester losses by machine vision

Paper


McCarthy Chery. 2022. "Preliminary evaluation of real-time sensing of harvester losses by machine vision ." 43rd Annual Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2022). Mackay, Australia 19 - 22 Apr 2022 Australia. Australian Society of Sugar Cane Technologists. pp. 108-110
Paper/Presentation Title

Preliminary evaluation of real-time sensing of harvester losses by machine vision

Presentation TypePaper
AuthorsMcCarthy Chery
Journal or Proceedings TitleProceedings of the 43rd Annual Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2022)
Journal Citation43, pp. 108-110
Page Range108-110
Number of Pages3
Year2022
PublisherAustralian Society of Sugar Cane Technologists
Place of PublicationAustralia
ISBN9781713859215
Web Address (URL) of Conference Proceedingshttps://www.proceedings.com/65361.html
Conference/Event43rd Annual Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2022)
Event Details
43rd Annual Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2022)
Parent
Australian Society of Sugar Cane Technologists Conference
Delivery
Online
Event Date
19 to end of 22 Apr 2022
Event Location
Mackay, Australia
Event Venue
Mackay Entertainment and Convention Centre
Abstract

Sugar losses during cane cleaning in mechanical harvesters are estimated to cause millions of dollars of lost income per year. Existing commercially available loss-monitoring devices do not directly sense losses from the material expelled during cane cleaning in the harvester. Development of technologies for real-time, accurate and consistent measurement of harvester losses is required to achieve improved efficiency of harvesting with reduced losses. A proof-of-concept machine-vision sensor containing cameras with visible light and non-visible light sensitivity has been developed for the purpose of real-time sensing of harvester losses. Initial trials were conducted in October 2020 in the Gordonvale region. Primary extractor fan speed was varied for the trials, and a Sugar Research Australia field team recorded losses data using the Infield Sucrose Loss Measurement System. The trials enabled machine-vision sensor data to be compared with sugar expelled from the harvester under a range of field conditions. Machine-vision analysis has indicated a coefficient of determination of between 0.72 and 0.93 for prediction of sugar loss from image data from a combination of camera sensors. Further analysis is presently being undertaken on trial data collected in 2021 under different field conditions. Machine vision has potential to detect sugar losses for the purpose of providing real-time feedback to harvester operators. Ultimately, such a sensor has potential use to automatically detect and provide recommendations for harvester settings in real-time to minimise losses.

Keywordsautomation; billets; harvest losses; Image analysis; infield sucrose loss measurement system
Public Notes

Files associated with this item cannot be displayed due to copyright restrictions.

FunderSugar Research Australia
Byline AffiliationsUniversity of Southern Queensland
Permalink -

https://research.usq.edu.au/item/yyyv7/preliminary-evaluation-of-real-time-sensing-of-harvester-losses-by-machine-vision

  • 46
    total views
  • 0
    total downloads
  • 3
    views this month
  • 0
    downloads this month

Export as

Related outputs

Non-visual common root rot disease detection using NIR spectrum and machine learning methods
Xiong, Yiyi, McCarthy, Cheryl, Humpal, Jacob and Percy, Cassandra. "Non-visual common root rot disease detection using NIR spectrum and machine learning methods." 2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Wuhan, China 25 - 28 Jul 2023 IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/https://ieeexplore.ieee.org/xpl/conhome/10233256/proceeding
NIR spectroscopy and Deep Neural Networks for early common root rot detection in wheat from multi-season trials
Xiong, Yiyi, McCarthy, Cheryl, Humpal, Jacob and Percy, Cassandra. 2024. "NIR spectroscopy and Deep Neural Networks for early common root rot detection in wheat from multi-season trials." Agronomy Journal. 116 (5), pp. 2370-2390. https://doi.org/10.1002/agj2.21648
A review on common root rot of wheat and barley in Australia
Xiong, Yiyi, McCarthy, Cheryl, Humpal, Jacob and Percy, Cassandra. 2023. "A review on common root rot of wheat and barley in Australia." Plant Pathology. 72 (8), pp. 1347-1364. https://doi.org/10.1111/ppa.13777
NIR sensing and machine learning to rapidly assess crown rot in wheat
Humpal, Jacob, McCarthy, Cheryl, Percy, Cassandra and Thomasson, J. Alex. 2020. "NIR sensing and machine learning to rapidly assess crown rot in wheat." GRDC Grains Research Update: Driving Profit Through Research. Goondiwindi, Australia 03 - 04 Mar 2020 Australia.
Detection of crown rot in wheat utilising near-infrared spectroscopy: towards remote and robotic sensing
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
Machine vision learning for crop disease and quality parameters
Humpal, Jacob, McCarthy, Cheryl, Thomasson, J. Alex and Percy, Cassy. 2018. "Machine vision learning for crop disease and quality parameters." 21st Precision Agriculture Symposium (2018). Adelaide, Australia Australia.
Space Agriculture: Sensing Crops in Space
Humpal, Jacob, McCarthy, Cheryl, Baillie, Craig, Percy, Cassy and Brett, Peter. 2022. "Space Agriculture: Sensing Crops in Space." GRDC Update Papers.
Deep learning - method overview and review of use for fruit detection and yield estimation
Koirala, Anand, Walsh, Kerry B., Wang, Zhenglin and McCarthy, Cheryl. 2019. "Deep learning - method overview and review of use for fruit detection and yield estimation." Computers and Electronics in Agriculture. 162, pp. 219-234. https://doi.org/10.1016/j.compag.2019.04.017
Deep learning for real‑time fruit detection and orchard fruit load estimation: benchmarking of ‘MangoYOLO’
Koirala, A, Walsh, K.B, Wang, Z and McCarthy, C. 2019. "Deep learning for real‑time fruit detection and orchard fruit load estimation: benchmarking of ‘MangoYOLO’." Precision Agriculture. 20 (6), pp. 1107-1135. https://doi.org/10.1007/s11119-019-09642-0
A review of autonomous navigation systems in agricultural environments
Shalal, N., Low, T., McCarthy, C. and Hancock, N.. 2013. "A review of autonomous navigation systems in agricultural environments." SEAg 2013: Innovative Agricultural Technologies for a Sustainable Future. Barton, Australia 22 - 25 Sep 2013
Row and water front detection from UAV thermal-infrared imagery for furrow irrigation monitoring
Long, Derek, McCarthy, Cheryl and Jensen, Troy. 2016. "Row and water front detection from UAV thermal-infrared imagery for furrow irrigation monitoring." 2016 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2016). Banff, Canada 12 - 15 Jul 2016 https://doi.org/10.1109/AIM.2016.7576783
Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion – Part B: mapping and localisation
Shalal, Nagham, Low, Tobias, McCarthy, Cheryl and Hancock, Nigel. 2015. "Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion – Part B: mapping and localisation." Computers and Electronics in Agriculture. 119, pp. 267-278. https://doi.org/10.1016/j.compag.2015.09.026
Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion – Part A: tree detection
Shalal, Nagham, Low, Tobias, McCarthy, Cheryl and Hancock, Nigel. 2015. "Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion – Part A: tree detection." Computers and Electronics in Agriculture. 119, pp. 254-266. https://doi.org/10.1016/j.compag.2015.09.025
Evaluation of stereovision for extracting plant features
Mohammed Amean, Z., Low, T., McCarthy, C. and Hancock, N.. 2013. "Evaluation of stereovision for extracting plant features." SEAg 2013: Innovative Agricultural Technologies for a Sustainable Future. Barton, Australia 22 - 25 Sep 2013 Canberra, Australia.
A preliminary evaluation of vision and laser sensing for tree trunk detection and orchard mapping
Shalal, Nagham, Low, Tobias, McCarthy, Cheryl and Hancock, Nigel. 2013. "A preliminary evaluation of vision and laser sensing for tree trunk detection and orchard mapping." Katupitiya, Jayantha, Guivant, Jose and Eaton, Ray (ed.) Australasian Conference on Robotics and Automation (ACRA 2013). Sydney, Australia 02 - 04 Dec 2013 Australia.
Automatic plant branch segmentation and classification using vesselness measure
Mohammed Amean, Z., Low, T., McCarthy, C. and Hancock, N.. 2013. "Automatic plant branch segmentation and classification using vesselness measure." Katupitiya, Jayantha, Guivant, Jose and Eaton, Ray (ed.) Australasian Conference on Robotics and Automation (ACRA 2013). Sydney, Australia 02 - 04 Dec 2013 Australia.
Commercialisation of precision agriculture technologies in the macadamia industry
Rees, Steven, Dunn, Mark, Werkman, Peter and McCarthy, Cheryl. 2009. Commercialisation of precision agriculture technologies in the macadamia industry. Toowoomba, Australia. University of Southern Queensland.
Preliminary evaluation of shape and colour image sensing for automated weed identification in sugarcane
McCarthy, Cheryl, Rees, Steven and Baillie, Craig. 2012. "Preliminary evaluation of shape and colour image sensing for automated weed identification in sugarcane." Bruce, R. C. (ed.) 34th Annual Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2012). Palm Cove, Australia 01 - 04 May 2012 Australia.
Development and evaluation of a prototype precision spot spray system using image analysis to target guinea grass in sugarcane
Rees, S. J., McCarthy, C. L., Baillie, C. P., Burgos-Artizzu, X. P. and Dunn, M. T.. 2011. "Development and evaluation of a prototype precision spot spray system using image analysis to target guinea grass in sugarcane." Australian Journal of Multi-Disciplinary Engineering. 8 (2), pp. 97-106.
Automated internode length measurement of cotton plants under field conditions
McCarthy, C. L., Hancock, N. H. and Raine, S. R.. 2009. "Automated internode length measurement of cotton plants under field conditions." Transactions of the ASABE. 52 (6), pp. 2093-2103.
On-the-go machine vision sensing of cotton plant geometric parameters: first results
McCarthy, Cheryl, Hancock, Nigel and Raine, Steven. 2008. "On-the-go machine vision sensing of cotton plant geometric parameters: first results." Billingsley, John and Bradbeer, Robin (ed.) 13th Annual Conference on Mechatronics and Machine Vision in Practice. (M2VIP 2006). Toowoomba, Australia 05 - 07 Dec 2006 Germany. pp. 305-312 https://doi.org/10.1007/978-3-540-74027-8_26
Applied machine vision of plants: a review with implications for field deployment in automated farming operations
McCarthy, C. L., Hancock, N. H. and Raine, S. R.. 2010. "Applied machine vision of plants: a review with implications for field deployment in automated farming operations." Intelligent Service Robotics. 3 (4), pp. 209-217. https://doi.org/10.1007/s11370-010-0075-2
Machine vision-based weed spot spraying: a review and where next for sugarcane?
McCarthy, Cheryl, Rees, Steven and Baillie, Craig. 2010. "Machine vision-based weed spot spraying: a review and where next for sugarcane?" Bruce, R. C. (ed.) 32nd Annual Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2010). Bundaberg, Australia 11 - 14 May 2010 Australia.
Apparatus and infield evaluations of a prototype machine vision system for cotton plant internode length measurement
McCarthy, Cheryl, Hancock, Nigel and Raine, Steven R.. 2010. "Apparatus and infield evaluations of a prototype machine vision system for cotton plant internode length measurement." Journal of Cotton Science. 14 (4), pp. 221-232.
Applied machine vision in agriculture at the NCEA
McCarthy, Cheryl and Billingsley, John. 2009. "Applied machine vision in agriculture at the NCEA." Banhazi, T. M. and Saunders, C. (ed.) SEAg 2009: Agricultural Technologies In a Changing Climate. Brisbane, Australia 13 - 16 Sep 2009 Brisbane, Australia.
Cattle liveweight estimation using machine vision assessment of objective body measurements: first results
McCarthy, C., Billingsley, J., Finch, N., Murray, P. and Gaughan, J.. 2010. "Cattle liveweight estimation using machine vision assessment of objective body measurements: first results." 28th Biennial Australian Society of Animal Production Conference. Armidale, Australia 11 - 15 Jul 2010 Brisbane, Australia.
Development of a prototype precision spot spray system using image analysis and plant identification technology
Rees, Steven, McCarthy, Cheryl, Artizzu, X. P. B., Baillie, Craig and Dunn, Mark. 2009. "Development of a prototype precision spot spray system using image analysis and plant identification technology." Banhazi, T. M. and Saunders, C. (ed.) SEAg 2009: Agricultural Technologies In a Changing Climate. Brisbane, Australia 13 - 16 Sep 2009 Brisbane, Australia.
A preliminary field evaluation of an automated vision-based plant geometry measurement system
McCarthy, Cheryl, Hancock, Nigel and Raine, Steven R.. 2007. "A preliminary field evaluation of an automated vision-based plant geometry measurement system." Prusinkiewicz, Przemyslaw, Hanan, Jim S. and Lane, Brendan (ed.) 5th International Workshop on Functional Structural Plant Models (FSPM07). Napier, New Zealand 04 - 09 Nov 2007
Automated machine vision sensing of plant structural parameters
McCarthy, C. L., Hancock, Nigel and Raine, Steven R.. 2007. "Automated machine vision sensing of plant structural parameters." Biological Sensorics 2007: Critical Technologies for Future Biosystems. Minneapolis, Australia 15 - 17 Jun 2007 St. Joseph, MI, United States.
On-the-go machine vision sensing of cotton plant geometric parameters: first results
McCarthy, Cheryl, Hancock, Nigel and Raine, Steven R.. 2006. "On-the-go machine vision sensing of cotton plant geometric parameters: first results." Billingsley, John (ed.) 13th Annual Conference on Mechatronics and Machine Vision in Practice. (M2VIP 2006). Toowoomba, Australia 05 - 07 Dec 2006 Berlin, Heidelberg.
A preliminary evaluation of machine vision sensing of cotton nodes for automated irrigation control
McCarthy, Cheryl, Hancock, Nigel and Raine, Steven R.. 2006. "A preliminary evaluation of machine vision sensing of cotton nodes for automated irrigation control." Irrigation Australia 2006: Irrigation Association of Australia National Conference and Exhibition. Brisbane, Australia 09 - 11 May 2006 Brisbane.