LemnaGrid is LemnaTec’s programming platform for image processing. With a large library of functions many steps in image processing can be programmed on a graphical surface. The advantage of this programming platform is that learning the basic steps is done relatively quickly so that users can modify image processing procedures or set up new ones even if they are no computer programmers or software engineers. Thus, biologists, lab technicians, or agronomists can do image processing on their own. This is particularly useful once it comes to plant diseases, as here variability of samples not only comes from the plants but particularly is driven by the pathogens. So, the demand for adapting image processing to new challenges increases. Despite LemnaTec always offers developing analysis procedures as service, self-programming with LemnaGrid is a cost-effective and fast option to address diverse analytical tasks.
The functions provided for programming enable segmenting the plants, but they can similarly quantify aberrant parts of the plants. Such aberrant parts frequently are disease symptoms, such as leaf spots, rust pustules, mildew, and other tissue damages. Such symptoms frequently have different colors compared to the surrounding heathy tissue, and sometimes the whole leaf changes color in comparison to healthy leaves. With many tools for color space analyses, thresholds, and filters LemnaGrid can address symptom rating from images.
The advantage of image-based symptom-rating is evident. Who ever tried to rate disease severity by eye knows that this process is time-consuming and tedious. Moreover, bias occurring between different persons looking at a particular sample type calls for standardizing and objectifying. These issues can be achieved using imaging systems.
In the example, disease symtoms together with yellowing around the symptoms were captured by image processing. This enables determining the fraction of diseased area and provides a measure of diseaes severity. Such assessements deliver disease severity data in one step together with a photographic documentation, so that throughput can be increased. In contrast, traditional visual scoring is time laborious and user-dependent. With such disease severity assessments, it is possible to rate resistance features of genotypes or to assess effectiveness of plant treatments.