A computer vision approach for quantifying leaf shape of maize (Zea mays L.) and simulating its impact on light interception
The precise determination of leaf shape is crucial for the quantification of morphological variations between individual leaf ranks and cultivars and simulating their impact on light interception in functional-structural plant models (FSPMs). Standard manual measurements on destructively collected l...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1521242/full |
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| author | Dina Otto Sebastian Munz Emir Memic Jens Hartung Simone Graeff-Hönninger |
| author_facet | Dina Otto Sebastian Munz Emir Memic Jens Hartung Simone Graeff-Hönninger |
| author_sort | Dina Otto |
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| description | The precise determination of leaf shape is crucial for the quantification of morphological variations between individual leaf ranks and cultivars and simulating their impact on light interception in functional-structural plant models (FSPMs). Standard manual measurements on destructively collected leaves are time-intensive and prone to errors, particularly in maize (Zea mays L.), which has large, undulating leaves that are difficult to flatten. To overcome these limitations, this study presents a new camera method developed as an image-based computer vision approach method for maize leaf shape analysis. A field experiment was conducted with seven commonly used silage maize cultivars at the experimental station Heidfeldhof, University of Hohenheim, Germany, in 2022. To determine the dimensions of fully developed leaves per rank and cultivar, three destructive measurements were conducted until flowering. The new camera method employs a GoPro Hero8 Black camera, integrated within an LI-3100C Area Meter, to capture 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, comparing results against standard manual measurements to assess accuracy and reliability. Leaf width functions were fitted to quantify leaf shape parameters. Statistical analysis comparing cultivars and leaf ranks identified significant differences in leaf shape parameters (p < 0.01) for term alpha and term a. Simulations within a FSPM demonstrated that variations in leaf shape can alter light interception by up to 7%, emphasizing the need for precise parameterization in crop growth models. The new camera method provides a basis for future studies investigating rank-dependent leaf shape effects, which can offer an accurate representation of the canopy in FSPMs and improve agricultural decision-making. |
| format | Article |
| id | doaj-art-866ce8ca118d4e70a87750a124fb3301 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Plant Science |
| spelling | doaj-art-866ce8ca118d4e70a87750a124fb33012025-08-20T03:47:19ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-06-011610.3389/fpls.2025.15212421521242A computer vision approach for quantifying leaf shape of maize (Zea mays L.) and simulating its impact on light interceptionDina Otto0Sebastian Munz1Emir Memic2Jens Hartung3Simone Graeff-Hönninger4Institute of Crop Science, Agronomy Department, University of Hohenheim, Stuttgart, GermanyInstitute of Crop Science, Agronomy Department, University of Hohenheim, Stuttgart, GermanyInstitute of Crop Science, Agronomy Department, University of Hohenheim, Stuttgart, GermanyDepartment Sustainable Agriculture and Energy Systems, University of Applied Science, Freising, GermanyInstitute of Crop Science, Agronomy Department, University of Hohenheim, Stuttgart, GermanyThe precise determination of leaf shape is crucial for the quantification of morphological variations between individual leaf ranks and cultivars and simulating their impact on light interception in functional-structural plant models (FSPMs). Standard manual measurements on destructively collected leaves are time-intensive and prone to errors, particularly in maize (Zea mays L.), which has large, undulating leaves that are difficult to flatten. To overcome these limitations, this study presents a new camera method developed as an image-based computer vision approach method for maize leaf shape analysis. A field experiment was conducted with seven commonly used silage maize cultivars at the experimental station Heidfeldhof, University of Hohenheim, Germany, in 2022. To determine the dimensions of fully developed leaves per rank and cultivar, three destructive measurements were conducted until flowering. The new camera method employs a GoPro Hero8 Black camera, integrated within an LI-3100C Area Meter, to capture 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, comparing results against standard manual measurements to assess accuracy and reliability. Leaf width functions were fitted to quantify leaf shape parameters. Statistical analysis comparing cultivars and leaf ranks identified significant differences in leaf shape parameters (p < 0.01) for term alpha and term a. Simulations within a FSPM demonstrated that variations in leaf shape can alter light interception by up to 7%, emphasizing the need for precise parameterization in crop growth models. The new camera method provides a basis for future studies investigating rank-dependent leaf shape effects, which can offer an accurate representation of the canopy in FSPMs and improve agricultural decision-making.https://www.frontiersin.org/articles/10.3389/fpls.2025.1521242/fullleaf shapeleaf widthmaize (Zea mays L.)computer visionFSPMlight interception |
| spellingShingle | Dina Otto Sebastian Munz Emir Memic Jens Hartung Simone Graeff-Hönninger A computer vision approach for quantifying leaf shape of maize (Zea mays L.) and simulating its impact on light interception Frontiers in Plant Science leaf shape leaf width maize (Zea mays L.) computer vision FSPM light interception |
| title | A computer vision approach for quantifying leaf shape of maize (Zea mays L.) and simulating its impact on light interception |
| title_full | A computer vision approach for quantifying leaf shape of maize (Zea mays L.) and simulating its impact on light interception |
| title_fullStr | A computer vision approach for quantifying leaf shape of maize (Zea mays L.) and simulating its impact on light interception |
| title_full_unstemmed | A computer vision approach for quantifying leaf shape of maize (Zea mays L.) and simulating its impact on light interception |
| title_short | A computer vision approach for quantifying leaf shape of maize (Zea mays L.) and simulating its impact on light interception |
| title_sort | computer vision approach for quantifying leaf shape of maize zea mays l and simulating its impact on light interception |
| topic | leaf shape leaf width maize (Zea mays L.) computer vision FSPM light interception |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1521242/full |
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