The potential of hyperspectral measurements for early disease detection has been investigated by many experts over the last 5 years. One of the difficulties is obtaining enough data for training and building a hyperspectral training library. When the goal is to detect disease at a previsible stage, before the pathogen has manifested either its first symptoms or in the area surrounding the existing symptoms, it is impossible to objectively delineate the regions of interest containing the previsible pathogen growth from the areas without the pathogen growth. To overcome this, we propose an image labelling and segmentation algorithm that is able to (a) more objectively label the visible symptoms for the construction of a training library and (b) extend this labelling to the pre-visible symptoms. This algorithm is used to create hyperspectral training libraries for late blight disease (Phytophthora infestans) in potatoes and two types of leaf rust (Puccinia triticina and Puccinia striiformis) in wheat. The model training accuracies were compared between the automatic labelling algorithm and the classic visual delineation of regions of interest using a logistic regression machine learning approach. The modelling accuracies of the automatically labelled datasets were higher than those of the manually labelled ones for both potatoes and wheat, at 98.80% for P. infestans in potato, 97.69% for P. striiformis in soft wheat, and 96.66% for P. triticina in durum wheat.

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.

Leaf rust is a major wheat disease in southern Spain, where durum wheat is an important crop. Until the 2019/2020 season, this disease was effectively managed, as the most widely planted cultivars in southern Spain had effective resistance genes. A problem arose in the spring of 2020, when every farm field and durum wheat trial examined displayed leaf rust symptoms. Leaves had few but large uredinial pustules, different from those of the normal leaf rust caused by Puccinia triticina, and telia developed rapidly after only a few days. The symptoms clearly fitted with P. tritici-duri, another wheat leaf rust species already reported in the western Mediterranean Basin. This species is not new in southern Spain but has never been observed at such high severity on almost every durum wheat cultivar grown in that region. Leaf rust severity was assessed in durum wheat field trials in the 2020, 2021 and 2022 growing seasons in four provinces of southern Spain. During 2020 and 2021, 13 single pustule isolates of leaf rust were also collected from different cultivars of durum wheat. Inoculation of the isolates on a differential host set showed that four different races were present, two being of P. tritici-duri. Only cultivar Calero showed consistent resistance to the races of P. tritici-duri employed in this study.

Effective mitigation of the current threat from yellow rust and the potential threat from stem rust to wheat production in the south of Spain requires the characterization of the lineages/races currently present in the region. Results from this study clearly indicated that the main yellow rust lineages currently present in the south of Spain are PstS10, PstS13, and PstS14, to which several widely grown commercial cultivars are resistant. Even though stem rust is not yet present during the regular cropping season, the main lineages/races Clade IV-B and Clade IV-F, were identified, much like in most of Europe and parts of North Africa. The evaluation of differential series and special breeding lines with known genes under local conditions has indicated the availability of several genetic options that could be used in breeding/selection programs to provide effective levels of resistance to either disease in the future. However, in undertaking these efforts, it is important to consider not only the lineages currently present locally but also resistance options effective against lineages/races that are rapidly developing elsewhere and could very likely reach the south of Spain in the near future.