Bioluminescence is used as marker e.g., in genetic or plant pathological studies. We developed a method to monitor bioluminescence at the whole plant level combined with phenotypic analysis of the plant. Using a CCD camera mounted in a cabinet shielding all external light we can image weak luminescence emissions from samples and map these to RGB images. Image processing delivers temporal and spatial data on the distribution of the luminescence together with phenotypic features of the plants. With this technology, microbial colonization of plants can be monitored. Arabidopsis plants were inoculated with Pseudomonas and Xanthomonas plant-pathogenic bacteria labelled with gene cassette for autonomous luminescence and disease progression was monitored over time. Luminescence imaging revealed accumulation of the bacteria in different plant tissues while the RGB images served to monitor plant growth and occurrence of disease symptoms. Applying this method, resistant plants could be selected from a mutant population. Disease responses of susceptible plants were compared to the responses of resistant plants. In the case of Pseudomonas, bacterial abundance reached its maximum during two to four days after inoculation, at a time when water soaking of the leaves could be observed as well with the RGB camera. At later stages-five to seven days after inoculation, disease symptoms in terms of leaf yellowing and tissue collapse occurred while bacterial populations appeared to decrease. With this method it was possible to monitor pathogen development and disease progression non-invasively at whole-plant level over time.

Cyst nematodes comprise a group of soil-born pests that globally cause severe damage to cash crops like soybeans, cereals, potatoes and sugar beet. Density of nematode cysts in the soil is a key factor to determine the risk for crop cultivation. The nematodes threaten crop production they colonize crop roots and parasitize them, resulting in significant yield losses. Vice versa, development of nematode resistant cultivars require determination of cysts in-vitro to discriminate resistant from susceptible plant genotypes. Determining cysts per soil sample can be achieved by imaging and image processing. After extraction of cysts from soil samples, they are displayed on filter papers, which are traditionally scored by human eye. By developing an imaging setup for this sample type and a related image processing procedure, it was possible to optimize this scoring process. Imaging and image processing is faster, more comparable, and better documented than visual scoring by human operators. The imaging and image processing method was referenced with the traditional rating method as it is common practice in the German agricultural monitoring lab at the Julius Kühn Institute. The counting accuracy was highly comparable to the accuracy of visual counting. The optical sensing method delivers a set of additional data beyond the cyst count for phenotyping nematode populations. These comprise length, width, and area of the cyst that enable analyzing cyst dimensions. Sensing measured dimensions were found to be in accordance to literature data. Moreover, image processing enables analyzing cyst colors, a feature that could be important for age determination.