Using 3D reconstruction from image motion to predict total leaf area in dwarf tomato plants

Abstract

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.

Plant phenotyping relevance

RGB画像からの3D再構成と機械学習により、植物形質である総葉面積を非破壊・自動推定する方法を開発・検証しており、フェノタイピング手法が研究の中心です。

Code and data availability

The supplied blocks describe the paper's TLA dataset, video-derived images, and ML pipeline, but contain no public deposit, availability statement, or authors' URL for the phenotype data, images, code, or models. Figure 2 references '(link: the video)' without any resolvable URL, and all listed URLs are cited priorwork

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