Measurement of Maize Leaf Phenotypic Parameters Based on 3D Point Cloud

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 9; p. 2854
• Main Authors: Su, Yuchen, Li, Ran, Wang, Miao, Li, Chen, Ou, Mingxiong, Liu, Sumei, Hou, Wenhui, Wang, Yuwei, Liu, Lu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.04.2025; MDPI
• Subjects: 3D point cloud; Accuracy; Algorithms; Cameras; Clustering; clustering segmentation; Corn; Crops; Deep learning; digital modeling; Genotype & phenotype; Imaging, Three-Dimensional - methods; Lasers; Leaves; maize phenotype; Measurement; Morphology; Optical radar; Phenotype; Plant Leaves - anatomy & histology; Remote sensing; stem and leaf segmentation; Zea mays - anatomy & histology; Zea mays - growth & development; 3D point cloud; maize phenotype; digital modeling; clustering segmentation; stem and leaf segmentation

Plant height (PH), leaf width (LW), and leaf angle (LA) are critical phenotypic parameters in maize that reliably indicate plant growth status, lodging resistance, and yield potential. While various lidar-based methods have been developed for acquiring these parameters, existing approaches face limi...