Automatic leaf segmentation and overlapping leaf separation using stereo vision

Mohammed Amean, Zainab ORCID: https://orcid.org/0000-0003-1581-3883 and Low, Tobias ORCID: https://orcid.org/0000-0001-8666-0517 and Hancock, Nigel (2021) Automatic leaf segmentation and overlapping leaf separation using stereo vision. Array, 12:100099. pp. 1-17.

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Abstract

Farm management and crop quality assessment is becoming increasingly automated to keep up with demand. The physical examination of the plant leaves, stems and fruit can provide valuable information about a plant’s health. Automating the visual inspection through machine vision spawns challenges such as occlusions, irregular lightning and varying environmental conditions. In this paper, a plant leaf extraction algorithm utilising depth from a stereo vision sensor is presented. The algorithm tackles multiple leaf segmentation and overlapping leaf separation through synergising features such as colour, shape and depth. Depth is particularly used to measure discontinuities along its gradient in the disparity maps. The algorithm has a segmentation rate of 78% for individual plant leaves, over a range of complex backgrounds and changing plant canopies. The proposed algorithm was evaluated using 272 cotton and hibiscus plant images with results demonstrating that depth properties were effective in separating occluded and overlapping leaves, with a high separation rate of 84%. Leaf occlusion could be detected automatically without adding any artificial tags on the leaf boundaries. Furthermore, the results show a nearly identical performance for both types of plants (cotton and hibiscus) under various lighting and environmental conditions. The developed algorithm could be potentially applied to other types of plants that have similar structures to cotton and hibiscus.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021)
Date Deposited: 25 Oct 2021 23:41
Last Modified: 08 Mar 2022 21:32
Uncontrolled Keywords: machine vision, stereo vision, depth segmentation, crop monitoring, leaves detection, overlapping leaves
Fields of Research (2008): 09 Engineering > 0906 Electrical and Electronic Engineering > 090602 Control Systems, Robotics and Automation
09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
09 Engineering > 0906 Electrical and Electronic Engineering > 090699 Electrical and Electronic Engineering not elsewhere classified
Fields of Research (2020): 40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400906 Electronic sensors
Identification Number or DOI: https://doi.org/10.1016/j.array.2021.100099
URI: http://eprints.usq.edu.au/id/eprint/43991

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