Water stress is a critical factor affecting the health and productivity of ornamental plants, yet early detection remains challenging. This study aims to investigate the spectral responses of four ornamental plant taxa—Rosa hybrid (rose), Itea virginica (itea), Spiraea nipponica (spirea), and Weigela florida (weigela)—under varying levels of water stress using hyperspectral imaging and principal component analysis (PCA). Hyperspectral data were collected across multiple wavelengths and PCA was applied to identify key spectral bands associated with different stress levels. The analyses revealed that the first two principal components captured a majority of variance in the data, with specific wavelengths around 680 nm, 760 nm, and 810 nm playing a significant role in distinguishing between the stress levels. Score plots demonstrated clear separation between different stress treatments, indicating that spectral signatures evolve distinctly over time as water stress progresses. Influence plots identified observations with disproportionate impacts on the PCA model, ensuring the robustness of the analysis. Findings suggest that hyperspectral imaging, combined with PCA, is a powerful tool for early detection and monitoring of water stress in ornamental plants, providing a basis for improved water management practices in horticulture.

Plant diseases pose a significant threat to agriculture. Precise and appropriately timed detection and identification of plant diseases is crucial for disease management, and for the selection of resistant and tolerant varieties. The detection of plant diseases by the human eye is dependent on the experience of the expert and on external influences such as environmental conditions. New sensor systems, artificial intelligence, and robotics – summarised under the term “digital technologies" – can help make complex assessments more efficient. There has been immense progress in the field of digital plant disease detection in the last two decades, through interdisciplinary research and technological advances. Understanding plant-pathogen interactions and visualising the underlying biochemical and biophysical processes with optical sensors enables plant disease detection and characterization. These innovative digital tools contribute to an objective and automated assessment of crop traits and are helping shape the future of smart farming and plant phenotyping.

This work established a hyperspectral library of important foliar diseases of wheat induced by different fungal pathogens, representing a time series from infection to symptom appearance for the purpose of detecting spectral changes. The data were generated under controlled conditions at the leaf scale. The transition from healthy to diseased leaf tissue was assessed, and spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that indicate a specific developmental stage during pathogenesis, defined as turning points, were combined into a spectral library. Machine learning analysis methods were applied and compared to test the potential of this library to detect and quantify foliar diseases in hyperspectral images. All evaluated classifiers had high accuracy (≤99%) for the detection and identification of both biotrophic and necrotrophic fungi. The potential of applying spectral analysis methods in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques for plant diseases under field conditions.

Monitoring of plants using non-destructive sensors is an established tool for plant assessment and description of development and performance. These methods were established in laboratory and greenhouse scale. Furthermore, high throughput analysis is also well established. Transferring these methods to the field enables validation of results from laboratory under controlled conditions and an integration into the farming processes. Therefore, sensors have to be mounted on mobile carrier platforms. Manual moveable tripods, wheel based carriers (like robots, experimental carriers or tractors) and airborne devices (drones, helicopters, planes or satellites) can be used with and without a human controller. Depending on the carrier, the measuring setup, the distance between sensor and plant, throughput and thus resolution and the ability to resolve details varies. The following text introduces three different moveable carrier platforms, (i) on a tripod, (ii) on a mobile traversing with a linear stage (phytobike) and (iii) using an unmanned aerial vehicles (UAV) to measure field parameters from different scales in the field. Using the drone enables to quantify the plant coverage in sugar beet whereas tripod measurements enables a differentiation between healthy plots and these inoculated with Cercospora beticola. The smallest distance between plant and sensor is reached using the phytobike. Here the detection and quantification of areas in a wheat field infected with yellow rust is possible. With this platform, even the assessment of infected areas with yellow rust is feasible.

Monitoring von Pflanzen mit nicht-destruktiver Sensorik ist ein etabliertes Werkzeug bei der Begutachtung und Beschreibung pflanzlicher Entwicklung und Leistungsfähigkeit. Diese Methoden werden im Labor- und Gewächshaus auf Organ-, Pflanzen-, und Kleinbestandebene eingesetzt. Hochdurchsatzverfahren sind ebenfalls etabliert. Die Übertragung dieser Techniken auf die Feldebene ermöglicht die Validierung von Resultaten aus kontrollierten Bedingungen und eine Integration in Prozesse des Anbaumanagements. Dazu müssen die Sensoren auf mobilen Trägerplattformen angebracht werden. Hierbei kommen neben manuell verschiebbaren Stativen auch radgestützte Versuchsträger (Roboter, Geräteträger und Traktoren) oder luftgestützte Plattformen (Drohnen, Hubschrauber, Flugzeuge, Satelliten) jeweils bemannt oder unbemannt zum Einsatz. Je nach Trägerplattform variiert der Messaufbau, der Abstand zwischen Sensor und Pflanze, der Durchsatz und somit die Auflösung bzw. die Möglichkeit, Details zu erkennen. Im folgenden Text werden Versuche mit drei unterschiedlichen experimentellen Trägerplattformen – (i) stativgebunden, (ii) auf einer mobilen Verfahreinheit mit Linearachse (Phytobike) und (iii) Drohne – dargestellt, um pflanzliche Parameter auf verschiedenen Skalenebenen im Feld zu untersuchen. Mit einer Drohne wurde in Zuckerrüben der Bedeckungsgrad quantifiziert, während mit Stativmessungen eine Unterscheidung zwischen gesunden und mit Cercospora beticola inokulierten Parzellen dargestellt wurde. Der geringste Abstand zwischen Pflanze und Sensor wird mit dem System Phytobike erreicht. Damit konnten auf Weizenblättern Gelbrostsymptome detektiert und quantifiziert werden.

The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.

The application of hyperspectral imaging technology for plant disease detection in the field is still challenging. Existing equipment and analysis algorithms are adapted to highly controlled environmental conditions in the laboratory. However, only real time information from the field scale is able to guide plant protection measures and to optimize the use of resources. At the field scale, many parameters such as the optimal measurement distance, informative feature sets, and suitable algorithms have not been investigated. In this study, the hyperspectral detection and quantification of yellow rust in wheat was evaluated using two measurement platforms: a ground-based vehicle and an unmanned aerial vehicle (UAV). Different disease development stages and disease severities were provided in a plot-based field experiment. Measurements were performed weekly during the vegetation period. Data analysis was performed by three prediction algorithms with a focus on the selection of optimal feature sets. In this context, the across-scale application of optimized feature sets, an approach of information transfer between scales, was also evaluated. Relevant aspects for an on-line disease assessment in the field integrating affordable sensor technology, sensor spatial resolution, compact analysis models, and fast evaluation have been outlined and reflected upon. For the first time, a hyperspectral imaging observation experiment of a plant disease was comparatively performed at two scales, ground canopy and UAV.

This study establishes a method to detect and distinguish between brown rust and yellow rust on wheat leaves based on hyperspectral imaging at the leaf scale under controlled laboratory conditions. A major problem at this scale is the generation of representative and correctly labelled training data, as only mixed spectra comprising plant and fungal material are observed. For this purpose, the pure spectra of rust spores of Puccinia triticina and P. striiformis, causal agents of brown and yellow rust, respectively, were used to serve as a spectral fingerprint for the detection of a specific leaf rust disease. A least‐squares factorization was used on hyperspectral images to unveil the presence of the spectral signal of rust spores in mixed spectra on wheat leaves. A quantification of yellow and brown rust, chlorosis and healthy tissue was verified in time series experiments on inoculated plants. The detection of fungal crop diseases by hyperspectral imaging was enabled without pixel‐wise labelling at the leaf scale by using reference spectra from spore‐scale observations. For the first time, this study shows an interpretable decomposition of the spectral reflectance mixture during pathogenesis. This novel approach will support a more sophisticated and precise detection of foliar diseases of wheat by hyperspectral imaging.

The characterization of plant disease symptoms by hyperspectral imaging is often limited by the missing ability to investigate early, still invisible states. Automatically tracing the symptom position on the leaf back in time could be a promising approach to overcome this limitation. Therefore we present a method to spatially reference time series of close range hyperspectral images. Based on reference points, a robust method is presented to derive a suitable transformation model for each observation within a time series experiment. A non-linear 2D polynomial transformation model has been selected to cope with the specific structure and growth processes of wheat leaves. The potential of the method is outlined by an improved labeling procedure for very early symptoms and by extracting spectral characteristics of single symptoms represented by Vegetation Indices over time. The characteristics are extracted for brown rust and septoria tritici blotch on wheat, based on time series observations using a VISNIR (400–1000 nm) hyperspectral camera.

Hyperspectral imaging sensors are promising tools for monitoring crop plants or vegetation in different environments. Information on physiology, architecture or biochemistry of plants can be assessed non-invasively and on different scales. For instance, hyperspectral sensors are implemented for stress detection in plant phenotyping processes or in precision agriculture. Up to date, a variety of non-imaging and imaging hyperspectral sensors is available. The measuring process and the handling of most of these sensors is rather complex. Thus, during the last years the demand for sensors with easy user operability arose. The present study introduces the novel hyperspectral camera Specim IQ from Specim (Oulu, Finland). The Specim IQ is a handheld push broom system with integrated operating system and controls. Basic data handling and data analysis processes, such as pre-processing and classification routines are implemented within the camera software. This study provides an introduction into the measurement pipeline of the Specim IQ as well as a radiometric performance comparison with a well-established hyperspectral imager. Case studies for the detection of powdery mildew on barley at the canopy scale and the spectral characterization of Arabidopsis thaliana mutants grown under stressed and non-stressed conditions are presented.

The detection and identification of plant diseases is crucial for an appropriate and targeted application of plant protection measures in crop production. Recently, intensive research has been conducted to develop innovative and technology-based optical methods for plant disease detection. In contrast to common visual rating and detection methods, optical sensors are able to measure pathogen-induced changes in the plant physiology non-invasively and objectively. Several studies showed that especially hyperspectral sensors are valuable tools for disease detection, identification and quantification on different scales from the tissue to the canopy level. This review describes the basic principles of hyperspectral measurements and different types of available hyperspectral sensors. Possible applications of hyperspectral sensors on different scales for disease detection and plant protection are discussed and evaluated. The advantages and disadvantages on each particular scale, as well as the impact of external factors, such as: light, wind, viewing angle, for measurements in laboratories, greenhouses and fields, are critically assessed in order to support researchers and agriculture technicians. Additionally, a comprehensive literature review about the use of hyperspectral sensors on these different scales for plant disease detection reflects the possibilities of non-invasive measurement systems. This highlights advantages of hyperspectral sensors when investigating plant–pathogen interactions through multiple examples. By some approaches, detection before visible symptoms appear is feasible. The potential of hyperspectral sensors as a tool for disease identification and quantification, based on disease characteristic changes in the plants spectral signature, is discussed as well. The review is concluded with an overview on different data analysis methods, which are required to extract key information from gathered hyperspectral datasets.

South American leaf blight (SALB), the main disease found in the rubber tree crops of Latin America, is caused by the fungus Pseudocercospora ulei. This study aims to determine the photosynthetic response of two clones of H. brasiliensis with different resistances to SALB from P. ulei under controlled conditions, by means of a temporal analysis of gas exchange and chlorophyll a fluorescence. The results show that the effect on photosynthesis was proportional to the temporal progress and intensity of the disease symptoms to this effect, the maximum significant decrease (p < 0.05) in photosynthetic rates in the clone FX 3864 (susceptible) (88.3%) and in FX 4098 (moderately resistant) (45.2%) was observed 8 days after inoculation in B leaflets that were 18 days old. Meanwhile, a significant difference was found between the two clones' ability to capture, use, and dissipate light energy through photosystem II, which was evidenced by the minimum photosynthetic activity registered in the susceptible clone.