Monitoring Leaf Rust and Yellow Rust in Wheat with 3D LiDAR Sensing
2025
Jaime Nolasco Rodríguez-Vázquez | Orly Enrique Apolo-Apolo | Fernando Martínez-Moreno | Luis Sánchez-Fernández | Manuel Pérez-Ruiz
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&ndash:50% depending on cultivar susceptibility, with the durum wheat cultivar &lsquo:Kiko Nick&rsquo: and bread wheat &lsquo:Califa&rsquo: exhibiting the most severe reductions (~50&ndash:60%). While plant height and biomass remained relatively unaffected, LiDAR-derived intensity values strongly correlated with disease severity (R2 = 0.62&ndash: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.
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