Early Detection of Muskmelon Powdery Mildew Using Time-Series 3D Multispectral Point Clouds

Abstract

Melon (Cucumis melo L.) is a globally significant horticultural crop, characterized by high nutritional value and substantial commercial status. However, frequent outbreaks of powdery mildew severely threaten its yield and fruit quality. Current early detection methods primarily focus on detached leaf assays, which often lack sufficient model generalization. This study proposes a temporal 3D multispectral point cloud reconstruction method for melon plants by integrating multispectral imaging with 3D reconstruction technology. An Artificial Neural Network (ANN) model for 3D spatial light field distribution was developed based on a hemispherical white reference to achieve precise reflectance calibration of the multispectral point clouds. Post-calibration, the coefficient of variation (CV) for the spectral reflectance of the hemispherical reference in 3D space was reduced to less than 2.4%. On this basis, an early classification model for melon powdery mildew was constructed using Partial Least Squares Discriminant Analysis (PLS-DA) based on the mean reflectance spectra of individual plant point clouds. The results demonstrate that the average recognition accuracy reaches 85.94% from 4 days post-inoculation onwards, enabling disease early warning three days in advance. This research provides critical theoretical support and technical reference for the non-destructive early monitoring and precision smart plant protection of crops in facility agriculture.

Plant phenotyping relevance

メロン個体の病徴状態を対象に、時系列3Dマルチスペクトル点群の再構成・反射率校正と早期病害分類を開発しており、植物表現型取得手法が中心である。

Code and data availability

The supplied blocks describe the paper's own time-series 3D multispectral point cloud dataset, YOLOv8/ANN/PLS-DA models, and analysis workflow, but contain no data availability statement, public repository deposit, or author-provided URL for datasets, images, code, or trained models. No paper-specific public asset is,

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