In-Situ Monitoring and Prediction of Frost Growth on Plant Leaves Based on Dielectric Spectrum Analysis and an SWT-SSA-LSTM Model

Abstract

Accurate and in-situ monitoring of frost growth on plant leaves is crucial for disaster prevention in smart agriculture. To address the limitations of traditional methods in quantification and continuity, this study proposes a novel monitoring paradigm integrating dynamic dielectric spectrum analysis with hybrid intelligent algorithms. A mesh-electrode-based capacitive sensor was designed to capture in-situ and continuous dielectric spectrum changes on leaf surfaces. Subsequently, a hybrid SWT-SSA-LSTM model was constructed for high-fidelity denoising and prediction of the original signals. Field experiments demonstrated that this system could quantify frost layer mass and thickness with high precision. The established nonlinear regression models achieved coefficients of determination of 0.924 and 0.975, respectively. The prediction model exhibited outstanding performance, with a root mean square error as low as 1.475. This study establishes a complete technical closed-loop from physical perception to intelligent prediction, providing an innovative solution for precise frost monitoring in agriculture.

Plant phenotyping relevance

植物葉面の霜の質量・厚さという状態を、誘電スペクトルセンサーと予測モデルで連続的に定量する手法を開発・検証しており、フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe the field experiments, capacitive sensing system, frost mass/thickness measurements, and the SWT-SSA-LSTM model, but contain no data availability statement, public dataset deposit, image/sensor data release, or author code/model repository with a public URL. No paper-specific public asset (

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