A Semi-Automatic and Visual Leaf Area Measurement System Integrating Hough Transform and Gaussian Level-Set Method

Linjuan Wang, Chengyi Hao, Xiaoying Zhang (), Wenfeng Guo, Zhifang Bi, Zhaoqing Lan, Lili Zhang and Yuanhuai Han
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Linjuan Wang: Department of Basic Sciences, Shanxi Agricultural University, Jinzhong 030801, China
Chengyi Hao: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Xiaoying Zhang: School of Software, Shanxi Agricultural University, Jinzhong 030801, China
Wenfeng Guo: Department of Basic Sciences, Shanxi Agricultural University, Jinzhong 030801, China
Zhifang Bi: Department of Basic Sciences, Shanxi Agricultural University, Jinzhong 030801, China
Zhaoqing Lan: Department of Basic Sciences, Shanxi Agricultural University, Jinzhong 030801, China
Lili Zhang: Department of Basic Sciences, Shanxi Agricultural University, Jinzhong 030801, China
Yuanhuai Han: College of Agriculture, Shanxi Agricultural University, Jinzhong 030801, China

Agriculture, 2025, vol. 15, issue 19, 1-24

Abstract: Accurate leaf area measurement is essential for plant growth monitoring and ecological research; however, it is often challenged by perspective distortion and color inconsistencies resulting from variations in shooting conditions and plant status. To address these issues, this study proposes a visual and semi-automatic measurement system. The system utilizes Hough transform-based perspective transformation to correct perspective distortions and incorporates manually sampled points to obtain prior color information, effectively mitigating color inconsistency. Based on this prior knowledge, the level-set function is automatically initialized. The leaf extraction is achieved through level-set curve evolution that minimizes an energy function derived from a multivariate Gaussian distribution model, and the evolution process allows visual monitoring of the leaf extraction progress. Experimental results demonstrate robust performance under diverse conditions: the standard deviation remains below 1 cm 2 , the relative error is under 1%, the coefficient of variation is less than 3%, and processing time is under 10 s for most images. Compared to the traditional labor-intensive and time-consuming manual photocopy-weighing approach, as well as OpenPheno (which lacks parameter adjustability) and ImageJ 1.54g (whose results are highly operator-dependent), the proposed system provides a more flexible, controllable, and robust semi-automatic solution. It significantly reduces operational barriers while enhancing measurement stability, demonstrating considerable practical application value.

Keywords: color leaf area; multivariate Gaussian model; level set; Hough transform; semi-automatic system; plant phenotyping (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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