Treffer: Automating Leaf Area Measurement in Citrus: The Development and Validation of a Python-Based Tool.

Featured Application: This paper presents a fully automated Python-based tool that enables the rapid and reproducible measurement of citrus leaf area from scanned images, making it ideal for high-throughput phenotyping in agricultural research. Its batch-processing capability and robust performance under challenging imaging conditions make it especially valuable for breeding programs, physiological studies, and precision agriculture applications where large-scale, consistent leaf area data are essential. Leaf area is a critical trait in plant physiology and agronomy, yet conventional measurement approaches such as those using ImageJ remain labor-intensive, user-dependent, and difficult to scale for high-throughput phenotyping. To address these limitations, we developed a fully automated, open-source Python tool for quantifying citrus leaf area from scanned images using multi-mask HSV segmentation, contour-hierarchy filtering, and batch calibration. The tool was validated against ImageJ across 11 citrus cultivars (n = 412 leaves), representing a broad range of leaf sizes and morphologies. Agreement between methods was near perfect, with correlation coefficients exceeding 0.997, mean bias within ±0.14 cm², and error rates below 2.5%. Bland–Altman analysis confirmed narrow limits of agreement (±0.3 cm²) while scatter plots showed robust performance across both small and large leaves. Importantly, the Python tool successfully handled challenging imaging conditions, including low-contrast leaves and edge-aligned specimens, where ImageJ required manual intervention. Processing efficiency was markedly improved, with the full dataset analyzed in 7 s compared with over 3 h using ImageJ, representing a >1600-fold speed increase. By eliminating manual thresholding and reducing user variability, this tool provides a reliable, efficient, and accessible framework for high-throughput leaf area quantification, advancing reproducibility and scalability in digital phenotyping. [ABSTRACT FROM AUTHOR]

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