# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 1 Introduction  
Plant height, one of the most significant phenotypic indicators, represents the development and vigor of crops, which is crucial for variety selection and productivity prediction [1, 2, 3]. Crop phenotypic data forms the foundation of crop selection and field management [4]. Conventional techniques for measuring plant height rely on manual labor or two-dimensional photographs, but they have issues with accuracy and efficiency. Recently, UAV-based remote-sensing technologies have demonstrated an ability to improve the efficiency and accuracy of plant phenotyping, particularly for large-scale applications [5]. However, challenges such as variability in environmental conditions, densecanopy structures, and overlapping plants remain major hurdles for accurate plant height measurement in the field. With the advancement of three-dimensional reconstruction technology, point cloud data may now be used to determine crop plant height [6]. Studies such as those by [PERSON] et al. [7] and [PERSON] et al. [8] have successfully applied UAV-based 3D reconstruction methods to estimate plant height in wheat and maize, respectively, using structure from motion-multi-view stereo (SFM-MVS) techniques. These methods achieved high accuracy while maintaining cost-efficiency compared to active sensors like LiDAR. In particular, maize is China's primary grain crop, and measuring the plant height precisely is essential to increasing productivity and satisfying demand [9; 10]. The two primary categories of 3D reconstruction approaches are active and passive. Of these, passive reconstruction techniques, such as structure from motion (SFM), which are based on multi-view geometry, offer the benefits of low cost, high accuracy, and ease of use [11]. When paired with the multi-view geometry technique, a UAV fitted with a high-resolution camera to capture image sequences can effectively and non-destructively acquire 3D point cloud data on crops [12; 13; 14]. However, further work is required to address the limitations of SFM-based methods in handling complex environments, such as dense crop canopies and variable lighting conditions, which can affect segmentation accuracy and height estimation [15]. Although point cloud-based crop height measurements have gradually gained popularity in agricultural research, the majority of these measurements are currently limited to single-plant measurements at small scales in the field or indoors, and fewer measurements are made on field groups with more shade and closer spacing between research plants [16; 17]. Recent studies have demonstrated that integrating multispectral or LiDAR data with SFM techniques can improve segmentation accuracy in complex field conditions [18; 19]. Additionally, algorithmic improvements, such as region-growing segmentation and machine-learning-based approaches, have been explored to enhance point cloud processing in dense crop populations [20]. The scalability of UAV-based point cloud methods, including the proposed approach, is a critical factor for their application in larger agricultural fields and diverse crop types. For larger fields, the main challenges include processing increasingly large datasets and ensuring consistent accuracy under varying environmental conditions (e.g., uneven terrain and heterogeneous lighting). These challenges may be addressed by integrating parallel processing frameworks, optimizing data acquisition flight paths, and leveraging cloud-based data-processing platforms. Furthermore, the proposed column space approximate segmentation algorithm can be adapted to other crop types by adjusting parameters such as point cloud density thresholds or incorporating additional input data, such as multispectral or LiDAR data, to handle differences in canopy structure and plant morphology. Future studies should focus on validating the method across various crops, such as wheat, rice, or soybean, to ensure broader applicability and scalability.