Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm

Huazhe Zhang, Nian Liu, Juan Xia, Lejun Chen and Shengde Chen ()
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Huazhe Zhang: College of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou 511363, China
Nian Liu: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Juan Xia: College of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou 511363, China
Lejun Chen: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Shengde Chen: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 3, 1-25

Abstract: Plant genomics have progressed significantly due to advances in information technology, but phenotypic measurement technology has not kept pace, hindering plant breeding. As maize is one of China’s three main grain crops, accurately measuring plant height is crucial for assessing crop growth and productivity. This study addresses the challenges of plant segmentation and inaccurate plant height extraction in maize populations under field conditions. A three-dimensional dense point cloud was reconstructed using the structure from motion–multi-view stereo (SFM-MVS) method, based on multi-view image sequences captured by an unmanned aerial vehicle (UAV). To improve plant segmentation, we propose a column space approximate segmentation algorithm, which combines the column space method with the enclosing box technique. The proposed method achieved a segmentation accuracy exceeding 90% in dense canopy conditions, significantly outperforming traditional algorithms, such as region growing (80%) and Euclidean clustering (75%). Furthermore, the extracted plant heights demonstrated a high correlation with manual measurements, with R 2 values ranging from 0.8884 to 0.9989 and RMSE values as low as 0.0148 m. However, the scalability of the method for larger agricultural operations may face challenges due to computational demands when processing large-scale datasets and potential performance variability under different environmental conditions. Addressing these issues through algorithm optimization, parallel processing, and the integration of additional data sources such as multispectral or LiDAR data could enhance its scalability and robustness. The results demonstrate that the method can accurately reflect the heights of maize plants, providing a reliable solution for large-scale, field-based maize phenotyping. The method has potential applications in high-throughput monitoring of crop phenotypes and precision agriculture.

Keywords: field corn; point cloud processing; column space algorithm; growth parameters (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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