用图像运动重建3D模型，自动估算矮生番茄叶面积。
Using 3D reconstruction from image motion to predict total leaf area in dwarf tomato plants
- 通过多帧图像构建植物3D点云，结合机器学习预测叶面积。
- 最佳模型达R²=0.80，MAE为489 cm²，跨实验验证稳定。
- 适合城市农业与精准种植，可自动化管理作物生长。
准确估算总叶面积（TLA）对评估植物生长、光合作用和蒸腾作用至关重要，但丛生植物如矮生番茄因冠层复杂，传统方法常费力、破坏植株或难以捕捉结构细节。本研究提出一种非破坏性方法，结合序列RGB图像的3D重建与机器学习，针对三种矮生番茄品种（Mohamed、Hahms Gelbe Topftomate、Red Robin）在温室条件下进行测试。两个实验（春夏季与秋冬季）共包含73株植物，通过“洋葱法”获取418组TLA测量值。高分辨率视频记录，每株使用500帧进行3D重建。采用四种算法（Alpha Shape、Marching Cubes、Poisson's、Ball Pivoting）处理点云，七种回归模型（多元线性回归、Lasso、Ridge、Elastic Net、随机森林、极端梯度提升、多层感知机）进行预测。最优组合为Alpha Shape（α=3）+极端梯度提升，达到R²=0.80，MAE=489 cm²。跨实验验证显示结果稳健（R²=0.56，MAE=579 cm²）。特征重要性分析表明高度、宽度和表面积是关键预测因子。该方法可扩展、自动化，适用于城市农业与精准农业，助力自动修剪、资源优化与可持续生产，且在不同环境与冠层结构下表现鲁棒。
原文摘要 · Abstract (English)
Accurate estimation of total leaf area (TLA) is crucial for evaluating plant growth, photosynthetic activity, and transpiration. However, it remains challenging for bushy plants like dwarf tomatoes due to their complex canopies. Traditional methods are often labor-intensive, damaging to plants, or limited in capturing canopy complexity. This study evaluated a non-destructive method combining sequential 3D reconstructions from RGB images and machine learning to estimate TLA for three dwarf tomato cultivars: Mohamed, Hahms Gelbe Topftomate, and Red Robin -- grown under controlled greenhouse conditions. Two experiments (spring-summer and autumn-winter) included 73 plants, yielding 418 TLA measurements via an "onion" approach. High-resolution videos were recorded, and 500 frames per plant were used for 3D reconstruction. Point clouds were processed using four algorithms (Alpha Shape, Marching Cubes, Poisson's, Ball Pivoting), and meshes were evaluated with seven regression models: Multivariable Linear Regression, Lasso Regression, Ridge Regression, Elastic Net Regression, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron. The Alpha Shape reconstruction ($α= 3$) with Extreme Gradient Boosting achieved the best performance ($R^2 = 0.80$, $MAE = 489 cm^2$). Cross-experiment validation showed robust results ($R^2 = 0.56$, $MAE = 579 cm^2$). Feature importance analysis identified height, width, and surface area as key predictors. This scalable, automated TLA estimation method is suited for urban farming and precision agriculture, offering applications in automated pruning, resource efficiency, and sustainable food production. The approach demonstrated robustness across variable environmental conditions and canopy structures.
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