Initial steps for high-throughput phenotyping in vineyards

The evaluation of phenotypic characters of grapevines is required directly in vineyards and is strongly limited by time, costs and the subjectivity of person in charge. Sensor-based techniques are prerequisite in order to allow non-invasive phenotyping of individual plant traits, to increase the quantity of object records and to reduce error variation. Thus, a Prototype-Image-Acquisition-System (PIAS) was developed for semi-automated capture of geo-referenced images in an experimental vineyard. Different strategies were tested for image interpretation using MATLAB®. The interpretation of images from the vineyard with real background is more practice-oriented but requires the calculation of depth maps. Different image analysis tools were verified in order to enable contactless and non-invasive detection of bud burst and quantification of shoots at an early developmental stage (BBCH 10) and enable fast and accurate determination of the grapevine berry size at BBCH 89. Depending on the time of image acquisition at BBCH 10 up to 94 % of green shoots were visible in images. The mean berry size (BBCH 89) was recorded non-invasively with a precision of 1 mm.

Effects of canopy architecture and microclimate on grapevine health in two training systems

Semi minimal pruned hedge (SMPH) is a time and cost saving grapevine training system, which is becoming more and more popular in German viticulture. In this study we compared the canopy architecture and its effect on the microclimate of SMPH trained grapevines with those of plants trained in vertical shoot positioning (VSP). We detected a 3 % points higher humidity and a 0.9 °C lower mean temperature within the complex canopy architecture of SMPH trained vines compared to VSP. Moreover, we investigated the influence of the differing microclimate, canopy and bunch architecture, as well as berry skin characteristics of the two training systems on the incidence of the major fungal grapevine diseases Downy Mildew, Powdery Mildew and Botrytis Bunch Rot, as well as on the occurrence and damage of the invasive insect pest Drosophila suzukii. We demonstrate that SMPH trained vines can be more susceptible to Downy Mildew and Powdery Mildew than VSP trained vines. The incidence of Botrytis Bunch Rot can be higher in the latter system, even if berry skin characteristics are the same in both training systems. We trapped a higher number of D. suzukii in SMPH canopies, however no increased berry damage was observed. Based on our results we recommend a more adapted plant protection regime for SMPH trained vines due to their higher susceptibility to the major fungal diseases. Furthermore, we propose a combination of SMPH and fungal resistant grapevine cultivars, e.g. 'Reberger', to achieve a more competitive, environmentally friendly and high quality grapevine production

Data transmission using GaAs-based InAs-InGaAs quantum dot LEDs emitting at 1.3 µm wavelength

Vertical emission light-emitting diodes (LEDs), based on long-wavelength, self-assembled InAs-InGaAs quantum dots (QDs) grown on GaAs substrate, are used to demonstrate up to 1 Gbit/s digital data transmission. The devices are characterized in terms of small-signal modulation, showing bandwidths beyond 1 GHz. [on SciFinder (R)

Constructing a Recipe Web from Historical Newspapers

Historical newspapers provide a lens on customs and habits of the past. For example, recipes published in newspapers highlight what and how we ate and thought about food. The challenge here is that newspaper data is often unstructured and highly varied, digitised historical newspapers add an additional challenge, namely that of fluctuations in OCR quality. Therefore, it is difficult to locate and extract recipes from them. We present our approach based on distant supervision and automatically extracted lexicons to identify recipes in digitised historical newspapers, to generate recipe tags, and to extract ingredient information. We provide OCR quality indicators and their impact on the extraction process. We enrich the recipes with links to information on the ingredients. Our research shows how combining natural language processing, machine learning, and semantic web can be used to construct a rich dataset from heterogeneous newspapers for the historical analysis of food culture