Open Access

Within the consortium “Experimentation Field Agro-Nordwest”, a practical concept for knowledge and technology transfer of digital competence in agriculture was created. For this purpose, the web-based e-learning system “SensX” was set up, consisting of videos, presentations and instructions. In addition, the classical e-learning concept was extended by data sets, student experiments and sensor data of plants acquired by a remote phenotyping robot. This resulted in a massive open online course (MOOC), which was tested with agricultural and biotechnology students in higher education at the University of Applied Sciences Osnabrück over two years. The evaluation process of “SensX” included an empirical survey, qualitative interviews of the participating students by an external institution and an evaluation of the concept by the lecturers.

Hintergrund: Die Wahl der Versorgungsform, stationär oder ambulant, unterliegt im deutschen Gesundheitswesen derzeit einem tiefgreifenden Wandel. Eine neue Qualität erhält die Öffnung der Krankenhäuser mit der Neufassung des § 116b SGB V. Die Studie untersucht die Frage, welches ambulante Potenzial sich aus bisher vollstationär behandelten Fällen in der Rheumatologie ergibt. Methode: Die Auswertung basiert auf einem Datensatz für die Jahre 2004 bis 2008. Dieser enthält anonymisiert die Abrechnungsdaten von rund 23,6 Mio. GKV-Versicherten. Die Auswahl von Patienten mit rheumatologischen Erkrankungen erfolgte anhand der in § 116b SGB V angegebenen ICD-10-Diagnosen. Ergebnisse: Im untersuchten Zeitraum wurde ein Anstieg der rheumatologischen Fälle um 13,9% beobachtet bei einem Rückgang der durchschnittlichen Verweildauer von 9,46 Tagen auf 8,08 Tage und der behandelnden Krankenhäuser um 3,1%. Der Anteil der rheumatologischen Fälle mit kurzer Verweildauer (≤2 Tage) nahm um 32,3% zu. Als „ambulantes Potenzial“ definieren wir den Anteil dieser Kurzlieger an der Gesamtzahl der vollstationären rheumatologischen Fälle, er stieg von 25,7% auf 29,9% Diskussion: Nicht alle Kurzlieger können problemlos in eine ambulante Versorgung überführt werden; diese erfordert spezialisierte Strukturen und Personal. Eine Zentrenbildung findet bisher nicht statt. Die Studie erlaubt keine Aussagen zur Qualität der Versorgung in den betrachteten Krankenhäusern. Eine Verknüpfung von Versorgungsdaten mit Qualitätsdaten wäre sinnvoll.

Background The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable. Results An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed. Conclusion The technical realization of “Phenomenon” allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.

Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana, and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.