Non-destructive Biomass and SPAD Estimation Based on 3D Photogrammetry for Faba Bean

Abstract

Faba bean (Vicia faba L.) is a valuable legume crop with high protein content and the ability to fix atmospheric nitrogen through symbiotic bacteria in its root nodules, contributing significantly to both human nutrition and agricultural sustainability. Chlorophyll concentration in leaves serves as a reliable indicator of nitrogen status and photosynthetic capacity, while biomass production reflects overall plant growth and resource use efficiency. This study aims to develop a nondestructive and accurate method for simultaneously estimating chlorophyll content meter (SPAD) and above-ground biomass in faba bean using three-dimensional (3D) photogrammetric imaging combined with deep learning techniques. Point cloud data were obtained from hand-held camera scans of five faba bean genotypes and processed using the PointNet neural network architecture. Results showed that SPAD estimation achieved high accuracy (7.52% relative error) based solely on 3D structural features, while biomass prediction benefited from the integration of real and synthetic datasets, reducing relative error significantly from 24.15 % to 18.04 %. The study highlights the potential of 3D imaging and point cloud-based photogrammetry as effective tools for plant phenotyping, offering a scalable and non-invasive approach for monitoring physiological traits and genotype performance in faba bean.

Plant phenotyping relevance

3DフォトグラメトリとPointNetを用いて、ソラマメのSPADと地上部バイオマスを非破壊推定する手法を開発・評価しており、表現型取得・推定が研究の中心である。

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