QuickLeaf-LWA high-throughput software: automated digital image analysis for accurate measurement of leaf shape and size

Aim: Leaf size and shape are key character for assessing photosynthesis, growth, genetic diversity, and plant health. Manual measurement is labor-intensive, time-consuming, and error-prone, particularly in large-scale phenotyping studies. This study aimed to develop QuickLeaf-LWA, a software tool for rapid, precise, and automated estimation of leaf size (length and width) and shape (aspect ratio) from digital images. Methodology: QuickLeaf-LWA integrates high-throughput automation (HTA) with comprehensive trait management and morphological analysis workflows. The software employs a robust image-processing pipeline using adaptive threshold for accurate leaf segmentation from complex backgrounds, followed by morphological operations to reduce noise. Leaf dimensions and aspect ratio are calculated using a rectangular-bounded, radially rotated image approach. Results: Accuracy assessment showed low mean error rates (0.584–3.283%) and high accuracy (96.717–99.416%). Root mean square error (RMSE) and mean absolute error (MAE) values were low (0.150–0.564 and 0.082–0.476, respectively), with strong correlations to manual measurements (R2 ≥ 0.96). Interpretation: QuickLeaf-LWA enables rapid analysis of hundreds of leaves within minutes offers an intuitive interface with batch-processing capability, making it a practical tool for plant physiology, genetics, agriculture, horticulture, and leaf phenotyping research.