A New Method for Extracting Individual Plant Bio-Characteristics from High-Resolution Digital Images

Rabab, Saba; Breen, Edmond; Gebremedhin, Alem; Shi, Fan; Badenhorst, Pieter; Chen, Yi-Ping Phoebe; Daetwyler, Hans D.

doi:10.3390/rs13061212

Open AccessArticle

A New Method for Extracting Individual Plant Bio-Characteristics from High-Resolution Digital Images

by

Saba Rabab

^1,2,*,

Edmond Breen

²,

Alem Gebremedhin

³,

Fan Shi

²,

Pieter Badenhorst

⁴,

Yi-Ping Phoebe Chen

⁵

and

Hans D. Daetwyler

^1,2

¹

School of Applied Systems Biology, La Trobe University, Bundoora 3083, Australia

²

Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora 3083, Australia

³

Agriculture Victoria, Victoria, Grain Innovation Park, Horsham 3400, Australia

⁴

Agriculture Victoria, Hamilton Centre, Hamilton 3300, Australia

⁵

Department of Computer Science and Information Technology, La Trobe University, Bundoora 3083, Australia

^*

Author to whom correspondence should be addressed.

Remote Sens. 2021, 13(6), 1212; https://doi.org/10.3390/rs13061212

Submission received: 20 January 2021 / Revised: 18 March 2021 / Accepted: 19 March 2021 / Published: 23 March 2021

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Abstract

The extraction of automated plant phenomics from digital images has advanced in recent years. However, the accuracy of extracted phenomics, especially for individual plants in a field environment, requires improvement. In this paper, a new and efficient method of extracting individual plant areas and their mean normalized difference vegetation index from high-resolution digital images is proposed. The algorithm was applied on perennial ryegrass row field data multispectral images taken from the top view. First, the center points of individual plants from digital images were located to exclude plant positions without plants. Second, the accurate area of each plant was extracted using its center point and radius. Third, the accurate mean normalized difference vegetation index of each plant was extracted and adjusted for overlapping plants. The correlation between the extracted individual plant phenomics and fresh weight ranged between 0.63 and 0.75 across four time points. The methods proposed are applicable to other crops where individual plant phenotypes are of interest.

Keywords: plant phenomics; image processing; plant area; plant center points; normalized difference vegetation index

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MDPI and ACS Style

Rabab, S.; Breen, E.; Gebremedhin, A.; Shi, F.; Badenhorst, P.; Chen, Y.-P.P.; Daetwyler, H.D. A New Method for Extracting Individual Plant Bio-Characteristics from High-Resolution Digital Images. Remote Sens. 2021, 13, 1212. https://doi.org/10.3390/rs13061212

AMA Style

Rabab S, Breen E, Gebremedhin A, Shi F, Badenhorst P, Chen Y-PP, Daetwyler HD. A New Method for Extracting Individual Plant Bio-Characteristics from High-Resolution Digital Images. Remote Sensing. 2021; 13(6):1212. https://doi.org/10.3390/rs13061212

Chicago/Turabian Style

Rabab, Saba, Edmond Breen, Alem Gebremedhin, Fan Shi, Pieter Badenhorst, Yi-Ping Phoebe Chen, and Hans D. Daetwyler. 2021. "A New Method for Extracting Individual Plant Bio-Characteristics from High-Resolution Digital Images" Remote Sensing 13, no. 6: 1212. https://doi.org/10.3390/rs13061212

APA Style

Rabab, S., Breen, E., Gebremedhin, A., Shi, F., Badenhorst, P., Chen, Y.-P. P., & Daetwyler, H. D. (2021). A New Method for Extracting Individual Plant Bio-Characteristics from High-Resolution Digital Images. Remote Sensing, 13(6), 1212. https://doi.org/10.3390/rs13061212

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A New Method for Extracting Individual Plant Bio-Characteristics from High-Resolution Digital Images

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