Soybean Canopy Stress Classification Using 3D Point Cloud Data

Abstract

Automated canopy stress classification for field crops has traditionally relied on single-perspective, two-dimensional (2D) photographs, usually obtained through top-view imaging using unmanned aerial vehicles (UAVs). However, this approach may fail to capture the full extent of plant stress symptoms, which can manifest throughout the canopy. Recent advancements in LiDAR technologies have enabled the acquisition of high-resolution 3D point cloud data for the entire canopy, offering new possibilities for more accurate plant stress identification and rating. This study explores the potential of leveraging 3D point cloud data for improved plant stress assessment. We utilized a dataset of RGB 3D point clouds of 700 soybean plants from a diversity panel exposed to iron deficiency chlorosis (IDC) stress. From this unique set of 700 canopies exhibiting varying levels of IDC, we extracted several representations, including (a) handcrafted IDC symptom-specific features, (b) canopy fingerprints, and (c) latent feature-based features. Subsequently, we trained several classification models to predict plant stress severity using these representations. We exhaustively investigated several stress representations and model combinations for the 3-D data. We also compared the performance of these classification models against similar models that are only trained using the associated top-view 2D RGB image for each plant. Among the feature-model combinations tested, the 3D canopy fingerprint features trained with a support vector machine yielded the best performance, achieving higher classification accuracy than the best-performing model based on 2D data built using convolutional neural networks. Our findings demonstrate the utility of color canopy fingerprinting and underscore the importance of considering 3D data to assess plant stress in agricultural applications.

Plant phenotyping relevance

3D点群と特徴抽出・分類モデルを用いてダイズ個体の鉄欠乏症ストレス重症度を推定し、2D画像手法と比較検証しており、植物表現型取得・推定法が中心である。

Code and data availability

The authors publicly release the paper's soybean IDC 3D point cloud dataset and analysis scripts (including the 2D projection generation script) via their GitHub repository, explicitly stated in the Data Availability Statement and Methods sections.