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

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 cm2, 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.

Plant phenotyping relevance

葉面積という植物形態形質を画像から抽出する半自動手法を開発し、精度・再現性・処理時間を評価しているため、方法が研究の中心である。

Code and data availability

The paper's leaf images, measurement data, and MATLAB system are not publicly deposited; the Data Availability Statement says data are available only on request from the corresponding author. No authors' public code or dataset URL is provided.

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