A Hyperspectral Library of Foliar Diseases of Wheat.

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.

Assessment of Infection and Mycotoxin Contamination of Wheat Kernels and Flour Using Hyperspectral Imaging.

head blight (FHB) epidemics in wheat and contamination with mycotoxins has become an increasing problem over the last decades. This prompted the need for non-invasive and non-destructive techniques to screen cereal grains for infection, which is usually accompanied by mycotoxin contamination. This study tested the potential of hyperspectral imaging to monitor the infection of wheat kernels and flour with three species.

Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed!

Determination and characterization of resistance reactions of crops against fungal pathogens are essential to select resistant genotypes. In plant breeding, phenotyping of genotypes is realized by time consuming and expensive visual plant ratings. During resistance reactions and during pathogenesis plants initiate different structural and biochemical defence mechanisms, which partly affect the optical properties of plant organs.

Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Head Blight of Wheat on Spikelet Scale.

Optical sensors have shown high capabilities to improve the detection and monitoring of plant disease development. This study was designed to compare the feasibility of different sensors to characterize head blight (FHB) caused by and . Under controlled conditions, time-series measurements were performed with infrared thermography (IRT), chlorophyll fluorescence imaging (CFI), and hyperspectral imaging (HSI) starting 3 days after inoculation (dai).

Discovering coherency of specific gene expression and optical reflectance properties of barley genotypes differing for resistance reactions against powdery mildew.

Hyperspectral imaging has proved its potential for evaluating complex plant-pathogen interactions. However, a closer link of the spectral signatures and genotypic characteristics remains elusive. Here, we show relation between gene expression profiles and specific wavebands from reflectance during three barley-powdery mildew interactions.

Screening of Barley Resistance Against Powdery Mildew by Simultaneous High-Throughput Enzyme Activity Signature Profiling and Multispectral Imaging.

Molecular marker analysis allow for a rapid and advanced pre-selection and resistance screenings in plant breeding processes. During the phenotyping process, optical sensors have proved their potential to determine and assess the function of the genotype of the breeding material. Thereby, biomarkers for specific disease resistance traits provide valuable information for calibrating optical sensor approaches during early plant-pathogen interactions.

Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform.

Background: Phenotyping is a bottleneck for the development of new plant cultivars. This study introduces a new hyperspectral phenotyping system, which combines the high throughput of canopy scale measurements with the advantages of high spatial resolution and a controlled measurement environment. Furthermore, the measured barley canopies were grown in large containers (called Mini-Plots), which allow plants to develop field-like phenotypes in greenhouse experiments, without being hindered by pot size.

Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection.

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.

A multi-resolution approach for an automated fusion of different low-cost 3D sensors.

The 3D acquisition of object structures has become a common technique in many fields of work, e.g., industrial quality management, cultural heritage or crime scene documentation.

Low-cost 3D systems: suitable tools for plant phenotyping.

Over the last few years, 3D imaging of plant geometry has become of significant importance for phenotyping and plant breeding. Several sensing techniques, like 3D reconstruction from multiple images and laser scanning, are the methods of choice in different research projects. The use of RGBcameras for 3D reconstruction requires a significant amount of post-processing, whereas in this context, laser scanning needs huge investment costs.

Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis.

Early water stress recognition is of great relevance in precision plant breeding and production. Hyperspectral imaging sensors can be a valuable tool for early stress detection with high spatio-temporal resolution. They gather large, high dimensional data cubes posing a significant challenge to data analysis.