A computer vision approach for quantifying leaf shape of maize (Zea mays L.) and simulating its impact on light interception

Dina Otto^*Sebastian MunzEmir MemicJens HartungSimone Graeff-Hönninger

• Faculty of Agricultural Sciences, University of Hohenheim, Stuttgart, Germany

Accurate leaf dimension assessment is essential for quantifying morphological variations across growth stages and cultivars and for precise 3D modeling of light interception. Standard manual measurements on destructively collected leaves to define leaf shape are time-consuming and susceptible to errors, especially for species such as maize (Zea mays L.) with large, undulating leaves that are difficult to flatten. To overcome these limitations, this study presents an innovative computer vision-based approach that automates maize leaf shape analysis, offering a more efficient and scalable alternative to manual measurements along the blade. The new camera method uses a GoPro Hero8 Black camera integrated withing an LI-3100C Area Meter, capturing high-resolution videos (1920 × 1080 pixels, 60 fps). A semi-automated software facilitates object detection, contour extraction, and leaf width determination including calibration for accuracy. Validation was performed using pixel-counting and contrast analysis, compared to standard manual measurements. Leaf width functions were fitted to quantify leaf shape parameters. The correlation between manual method (I) and camera method (II) was analyzed seperately for leaf shape term alpha and a, yielding a Pearson correlation coefficient of 0.87 and 0.67, respectively. Simulations within a functional-structural plant model (FSPM) demonstrated that variations in leaf shape can significantly alter light interception by up to 7%, highlighting the need for precise parameterization in modeling. This approach provides the foundation for future studies investigating rank-dependent leaf shape effects, enabling improved canopy representation in FSPMs, and improving agricultural decision-making.