Sensor-based phenotyping of above-ground plant-pathogen interactions

Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease

Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli

The bacterial infection of seeds is one of the most important quality factors affecting yield. Conventional detection methods for bacteria-infected seeds, such as biological, serological, and molecular tests, are not feasible since they require expensive equipment, and furthermore, the testing processes are also time-consuming. In this study, we use the Raman hyperspectral imaging technique to distinguish bacteria-infected seeds from healthy seeds as a rapid, accurate, and non-destructive detection tool. We utilize Raman hyperspectral imaging data in the spectral range of 400–1800 cm−1 to determine the optimal band-ratio for the discrimination of watermelon seeds infected by the bacteria Acidovorax citrulli using ANOVA. Two bands at 1076.8 cm−1 and 437 cm−1 are selected as the optimal Raman peaks for the detection of bacteria-infected seeds. The results demonstrate that the Raman hyperspectral imaging technique has a good potential for the detection of bacteria-infected watermelon seeds and that it could form a suitable alternative to conventional methods

System of System Integration for Hyperspectral Imaging Microscopy

Hyperspectral imaging (HSI) has become a leading tool in the medical field due to its capabilities for providing assessments of tissue pathology and separation of fluorescence signals. Acquisition speeds have been slow due to the need to acquire signal in many spectral bands and the light losses associated with technologies of spectral filtering. Traditional methods resulted in limited signal strength which placed limitations on time sensitive and photosensitive assays. For example, the distribution of cyclic adenosine monophosphate (cAMP) is largely undetermined because current microscope technologies lack the combination of speed, resolution, and spectral ability to accurately measure Forster resonance energy transfer (FRET). The work presented in this dissertation assesses the feasibility of integrating excitation-scanning hyperspectral imaging methods in widefield and confocal microscopy as a potential solution to improving acquisition speeds without compromising sensitivity and specificity. Our laboratory has previously proposed excitation-scanning approaches to improve signal-to-noise ratio (SNR) and showed that by using excitation-scanning, most-to-all emitted light at each excitation wavelength band can be detected which in turn, increases the SNR. This dissertation describes development and early feasibility studies for two novel prototype concepts as an alternative excitation-scanning HSI technology that may xvi increase acquisition speeds without compromising sensitivity or specificity. To achieve this, two new technologies for excitation-scanning HSI were conceptually designed: - LED-based spectral illumination for widefield microscopy - Supercontinuum-laser-based spectral illumination for spinning disk confocal microscopy. Next, design concepts were theoretically evaluated and optimized, leading to prototype testing. To evaluate the performance of each concept, prototype systems were integrated with other systems and subsystems, calibrated and feasibility assays were executed. This dissertation is divided into three main sections: 1) early development feasibility results of an excitation-scanning widefield system of systems prototype utilizing LED-based HSI, 2) Excitation-scanning HSI and image analysis methods used for endmember identification in fluorescence microscopy studies, and 3) early development feasibility of an excitation-scanning confocal SoS prototype utilizing a supercontinuum laser light source. Integration and testing results proved initial feasibility of both LED-based and broadband-based SoSs. The LED-based light source was successfully tested on a widefield microscope, while the broadband light source system was successfully tested on a confocal microscope. Feasibility for the LED-based system showed that further optical transmission optimization is needed to achieve high acquisition rates without compromising sensitivity or specificity. Early feasibility study results for the broadband-based system showed a successful proof of concept. Findings presented in this dissertation are expected to impact the fields of cellular physiology, medical sciences, and clinical diagnostics by providing the ability for high speed, high sensitivity microscopic imaging with spectroscopic discrimination