Automated variety trial plot growth and flowering detection for maize and soybean using machine vision

McCarthy, Alison ORCID: https://orcid.org/0000-0003-4595-6447 and Raine, Steven (2022) Automated variety trial plot growth and flowering detection for maize and soybean using machine vision. Computers and Electronics in Agriculture, 194:106727. pp. 1-21. ISSN 0168-1699

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Abstract

Hundreds of crop variety trial sites are operated across Australia with up to 100 small plots (20 m2) that require manual, labour-intensive monitoring for emergence, height, canopy cover and flowering status. Machine vision systems can reduce labour in monitoring, and infield fixed cameras are most suitable to provide low-cost continuous sensing without requiring labour of travelling to sites. Height is commonly detected using stereo cameras; however, a low-cost single camera system is preferable for broad scale use at variety trial sites. Existing low-cost fixed camera systems assess multiple plots in the image's field of view from a fixed mask that needs to be manually updated as the crop grows. Perspective transformation could be applied automatically to identify plot locations in the image. Existing systems also analyse multiple images independently and filter results to remove impacts from lighting variations. An alternative approach is to only analyse images in the same lighting conditions each day to reduce the need for filtering. In addition, a series of daily images can be used to track multiple leaf positions to monitor plant growth. A machine vision system has been developed that combines these technologies to track plant height from a series of daily images selected in the same lighting conditions, in combination with perspective transformation, in multiple plots in the camera's field of view. Emergence, canopy cover and flowering status were also identified at the canopy surface in each plot using colour segmentation and/or shape analysis. The algorithms were evaluated on 5 and 10 m towers monitoring randomised trials of 16 maize and soybean plots and detected maize flowering date within one day, soybean height (RMSE = 18.38 cm; R2 = 0.880), maize height (RMSE = 47.73 cm; R2 = 0.838), soybean canopy cover (RMSE = 22.14%; R2 = 0.818) and maize canopy cover (RMSE = 14.01%; R2 = 0.750). The larger error in maize height detection was due to the flowers being tracked by the algorithm instead of vegetation. Further work is required to transfer the algorithms to other crops and varieties.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Institute for Advanced Engineering and Space Sciences - Centre for Agricultural Engineering (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Agriculture and Environmental Science (1 Jan 2022 -)
Date Deposited: 21 Mar 2022 01:45
Last Modified: 21 Mar 2022 01:45
Uncontrolled Keywords: Colour segmentation; Feature tracking; Oblique imagery; Perspective transformation; Smartphone
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460304 Computer vision
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300206 Agricultural spatial analysis and modelling
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering
28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280101 Expanding knowledge in the agricultural, food and veterinary sciences
26 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 2603 Grains and seeds > 260311 Soybeans
26 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 2603 Grains and seeds > 260306 Maize
Identification Number or DOI: https://doi.org/10.1016/j.compag.2022.106727
URI: http://eprints.usq.edu.au/id/eprint/47044

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