Monitoring Leaf Rust and Yellow Rust in Wheat with 3D LiDAR Sensing

Abstract

Leaf rust and yellow rust are globally significant fungal diseases that severely impact wheat production, causing yield losses of up to 60% in highly susceptible cultivars. Early and accurate detection is crucial for integrating precision crop protection strategies to mitigate these losses. This study investigates the potential of 3D LiDAR technology for monitoring rust-induced physiological changes in wheat by analyzing variations in plant height, biomass, and light reflectance intensity. Results showed that grain yield decreased by 10–50% depending on cultivar susceptibility, with the durum wheat cultivar ‘Kiko Nick’ and bread wheat ‘Califa’ exhibiting the most severe reductions (~50–60%). While plant height and biomass remained relatively unaffected, LiDAR-derived intensity values strongly correlated with disease severity (R2 = 0.62–0.81, depending on the cultivar and infection stage). These findings demonstrate that LiDAR can serve as a non-destructive, high-throughput tool for early rust detection and biomass estimation, highlighting its potential for integration into precision agriculture workflows to enhance disease monitoring and improve wheat yield forecasting. To promote transparency and reproducibility, the dataset used in this study is openly available on Zenodo, and all processing code is accessible via GitHub, cited at the end of this manuscript.

Plant phenotyping relevance

LiDARによる小麦の病害状態・バイオマス等の非破壊推定を中心に評価しており、植物フェノタイピング手法の実質的な適用・検証に該当する。

Code and data availability

The paper's LiDAR-derived wheat rust phenotyping dataset is openly available on Zenodo (DOI 10.5281/zenodo.14889285), and the authors' point-cloud processing and parameter-extraction code is publicly available on GitHub (eapolo/agrolidarwheatrust). Both are explicitly stated in the Data Availability Statement.