Leaf Area Estimation in High-Wire Tomato Cultivation Using Plant Body Scanning

Abstract

Accurate estimation of the leaf area index (LAI), a key indicator of canopy development and light interception, is essential for improving productivity in greenhouse tomato cultivation. This study presents a non-destructive LAI estimation method using side-view images captured by a vertical scanning system. The system recorded the full vertical profile of tomato plants grown under two deleafing strategies: modifying leaf height (LH) and altering leaf density (LD). Vegetative and leaf areas were extracted using color-based masking and semantic segmentation with the Segment Anything Model (SAM), a general-purpose deep learning tool. Regression models based on leaf or all vegetative pixel counts showed strong correlations with destructively measured LAI, particularly under LH conditions (R2 > 0.85; mean absolute percentage error ≈ 16%). Under LD conditions, accuracy was slightly lower due to occlusion and leaf orientation. Compared with prior 3D-based methods, the proposed 2D approach achieved comparable accuracy while maintaining low cost and a labor-efficient design. However, the system has not been tested in real production, and its generalizability across cultivars, environments, and growth stages remains unverified. This proof-of-concept study highlights the potential of side-view imaging for LAI monitoring and calls for further validation and integration of leaf count estimation.

Plant phenotyping relevance

トマトのLAIという植物形質を、垂直スキャン画像・画像分割・回帰モデルで非破壊推定する手法を開発し、破壊測定との精度検証も行っており、フェノタイピング手法が研究の中心である。

Code and data availability

The supplied blocks describe a tomato LAI estimation pipeline (vertical scanning images, SAM segmentation, regression models) but contain no data availability statement, no public dataset/image deposit, and no author code repository. The only URLs present in the text are citations to prior work (an IEEE conferencepaper

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