biologia plantarum

International journal on Plant Life established by Bohumil Nìmec in 1959

Biologia plantarum 69:98-105, 2025 | DOI: 10.32615/bp.2025.010

Performance assessment of predictive models for morphological and biomass traits using image-derived canopy parameter at early stage of sunflower

Kwang-O JONG1, *, Ye-Kwang SIN1, Yu-Jin JANG2, Kum-Sil RI3
1 Department of Landscape Plant Breeding, Faculty of Life Science, Kim Il Sung University, Pyongyang, Democratic People's Republic of Korea
2 Pyongyang Turf Institute, Landscaping Technology Agency, Pyongyang, Democratic People's Republic of Korea
3 Institute of Industrial Crops, Academy of Agricultural Science (AAS), Pyongyang, Democratic People's Republic of Korea

Image-derived phenotyping at individual plant level can provide more accurate and more comprehensive information

than manual measuring for quantitative traits related to canopy growth in field environment. Aims of this study were

to: (i) assess smartphone image-derived canopy parameter at early stage of sunflower, and (ii) to evaluate performance

of predictive models for morphological and biomass traits related to canopy growth using smartphone image-derived

parameter. Original top-view image datasets taken with a smartphone camera were processed, and necessary information

was extracted with image analysis software developed using fuzzy c-means clustering algorithm. Canopy cover rate per

plant (CCR) was not only the relative value but also image-derived phenotyping feature. CCR were significantly and

positively correlated (r ≧ 0.90; **P < 0.01) with plant height, total leaf area per plant, plant dry mass, aboveground

plant dry and leaf dry mass, respectively. Ground measured and predicted values from linear regression model for plant

height, total leaf area per plant, plant dry mass, aboveground total dry mass, leaf dry mass per plant with CCR showed

an accurate prediction with high coefficients of determination (R ) of more than 0.8063, respectively. The present study

documented the robustness of predictive models using several metrics.

Keywords: biomass, canopy, image-derived phenotyping, leaf, regression linear model.

Received: March 28, 2024; Revised: November 11, 2025; Accepted: November 14, 2025; Published online: December 16, 2025  Show citation

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JONG, K., SIN, Y., JANG, Y., & Kum-Sil, R. (2025). Performance assessment of predictive models for morphological and biomass traits using image-derived canopy parameter at early stage of sunflower. Biologia plantarum, 69, Article 98-105. https://doi.org/10.32615/bp.2025.010
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