Zhou Jing, Cui Mingren, Wu Yushan, Gao Yudi, Tang Yijia, Jiang Bowen, Wu Min, Zhang Jian, Hou Lixin
College of Information Technology, Jilin Agricultural University, Changchun, China.
Faculty of Agronomy, Jilin Agricultural University, Changchun, China.
Front Plant Sci. 2024 Apr 22;15:1371252. doi: 10.3389/fpls.2024.1371252. eCollection 2024.
Stem diameter is a critical phenotypic parameter for maize, integral to yield prediction and lodging resistance assessment. Traditionally, the quantification of this parameter through manual measurement has been the norm, notwithstanding its tedious and laborious nature. To address these challenges, this study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter. This technology offers a practical solution for conducting rapid and non-destructive phenotyping. Firstly, RGB images, depth images, and 3D point clouds of maize stems were captured using an RGB-D camera, and precise alignment between the RGB and depth images was achieved. Subsequently, the contours of maize stems were delineated using 2D image processing techniques, followed by the extraction of the stem's skeletal structure employing a thinning-based skeletonization algorithm. Furthermore, within the areas of interest on the maize stems, horizontal lines were constructed using points on the skeletal structure, resulting in 2D pixel coordinates at the intersections of these horizontal lines with the maize stem contours. Subsequently, a back-projection transformation from 2D pixel coordinates to 3D world coordinates was achieved by combining the depth data with the camera's intrinsic parameters. The 3D world coordinates were then precisely mapped onto the 3D point cloud using rigid transformation techniques. Finally, the maize stem diameter was sensed and determined by calculating the Euclidean distance between pairs of 3D world coordinate points. The method demonstrated a Mean Absolute Percentage Error () of 3.01%, a Mean Absolute Error () of 0.75 mm, a Root Mean Square Error () of 1.07 mm, and a coefficient of determination (²) of 0.96, ensuring accurate measurement of maize stem diameter. This research not only provides a new method of precise and efficient crop phenotypic analysis but also offers theoretical knowledge for the advancement of precision agriculture.
茎直径是玉米的一个关键表型参数，对于产量预测和抗倒伏性评估至关重要。传统上，通过人工测量来量化这个参数一直是常态，尽管其过程繁琐且费力。为应对这些挑战，本研究引入了一种基于田间的非侵入性系统，该系统利用RGB-D相机的深度信息来测量玉米茎直径。这项技术为进行快速且无损的表型分析提供了一个切实可行的解决方案。首先，使用RGB-D相机捕获玉米茎的RGB图像、深度图像和三维点云，并实现了RGB图像与深度图像之间的精确对齐。随后，利用二维图像处理技术勾勒出玉米茎的轮廓，接着采用基于细化的骨架化算法提取茎的骨架结构。此外，在玉米茎的感兴趣区域内，利用骨架结构上的点构建水平线，从而得到这些水平线与玉米茎轮廓交点处的二维像素坐标。随后，通过将深度数据与相机的内参相结合，实现了从二维像素坐标到三维世界坐标的反投影变换。然后，使用刚体变换技术将三维世界坐标精确映射到三维点云。最后，通过计算三维世界坐标点对之间的欧几里得距离来感知和确定玉米茎直径。该方法的平均绝对百分比误差（ ）为3.01%，平均绝对误差（ ）为0.75毫米，均方根误差（ ）为1.07毫米，决定系数（ ²）为0.96，确保了对玉米茎直径的准确测量。本研究不仅提供了一种精确高效的作物表型分析新方法，也为精准农业的发展提供了理论知识。
