Smart farming is a growing field as technology advances. Plant characteristics are crucial indicators for monitoring plant growth. Research has been done to estimate characteristics like leaf area index, leaf disease, and plant height. However, few methods have been applied to non-destructive measurements of leaf size. In this paper, an automated non-destructive imaged-based measuring system is presented, which uses 2D and 3D data obtained using a Zivid 3D camera, creating 3D virtual representations (digital twins) of the tomato plants. Leaves are detected from corresponding 2D RGB images and mapped to their 3D point cloud using the detected leaf masks, which then pass the leaf point cloud to the plane fitting algorithm to extract the leaf size to provide data for growth monitoring. The performance of the measurement platform has been measured through a comprehensive trial on real-world tomato plants with quantified performance metrics compared to ground truth measurements. Three tomato leaf and height datasets (including 50+ 3D point cloud files of tomato plants) were collected and open-sourced in this project. The proposed leaf size estimation method demonstrates an RMSE value of 4.47mm and an R^2 value of 0.87. The overall measurement system (leaf detection and size estimation algorithms combine) delivers an RMSE value of 8.13mm and an R^2 value of 0.899.

翻译：智慧农业是随着技术进步而快速发展的领域。植物特征是其生长监测的关键指标。已有研究致力于估算叶面积指数、叶片病害和株高等特征，但鲜有方法应用于叶片的无损尺寸测量。本文提出了一种基于图像的自动化无损测量系统，该系统利用Zivid 3D相机获取的二维和三维数据，创建番茄植株的三维虚拟表征（数字孪生）。系统从对应的二维RGB图像中检测叶片，利用检测到的叶片掩膜将其映射至三维点云，进而通过平面拟合算法提取叶片尺寸，为生长监测提供数据。通过在实际番茄植株上进行的综合试验，并与地面实测数据对比量化性能指标，评估了测量平台的性能。本项目收集并开源了三个番茄叶片与株高数据集（包含50余份番茄植株三维点云文件）。所提出的叶片尺寸估计算法实现了RMSE值为4.47mm、R²值为0.87的性能。整体测量系统（叶片检测与尺寸估计算法协同）的RMSE值为8.13mm，R²值为0.899。