Integration of UAV Multi-Source Data for Accurate Plant Height and SPAD Estimation in Peanut

Abstract

Plant height and SPAD values are critical indicators for evaluating peanut morphological development, photosynthetic efficiency, and yield optimization. Recent unmanned aerial vehicle (UAV) technology advancements have enabled high-throughput phenotyping at field scales. As a globally strategic oilseed crop, peanut plays a vital role in ensuring food and edible oil security. This study aimed to develop an optimized estimation framework for peanut plant height and SPAD values through machine learning-driven integration of UAV multi-source data while evaluating model generalizability across temporal and spatial domains. Multispectral UAV and ground data were collected across four growth stages (2023–2024). Using spectral indices and Texture features, four models (PLSR, SVM, ANN, RFR) were trained on 2024 data and independently validated with 2023 datasets. The ensemble machine learning models (RFR) significantly enhanced estimation accuracy (R2 improvement: 3.1–34.5%) and robustness compared to the linear model (PLSR). Feature stability analysis revealed that combined spectral-textural features outperformed single-feature approaches. The SVM model achieved superior plant height prediction (R2 = 0.912, RMSE = 2.14 cm), while RFR optimally estimated SPAD values (R2 = 0.530, RMSE = 3.87) across heterogeneous field conditions. This UAV-based multi-modal integration framework demonstrates significant potential for temporal monitoring of peanut growth dynamics.

Plant phenotyping relevance

UAVマルチソースデータと機械学習により、ピーナッツの草丈およびSPAD値を推定するフレームワークを開発・検証しており、表現型取得・抽出手法が研究の中心である。

Code and data availability

The paper's UAV multispectral imagery, plant height/SPAD measurements, and analysis are not publicly deposited; the Data Availability Statement says data are available only upon request, and no code or model repository is provided.

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