The Chinese mitten crab is one of the top invasive species in Europe. In Flanders (Belgium), they are associated with river ecosystem degradation, especially the loss of aquatic vegetation and associated ecosystem services. Management measures have therefore been put in place to reduce the number of crabs migrating between the sea and freshwater areas and ultimately control the population. Although we are still long way from the goal, a low‐cost method has been applied to successfully catch migrating crabs. In this article, we outline the design and functioning of the trap. We monitored the population in a lowland river, measured migration speeds and calculated crab densities. With over 1 million crabs caught in 2 years, the trap proved to be very effective. Median anadromous (spring) and catadromous (autumn) migration speeds were 0.69 and 0.96 km day−1, respectively. Anadromous migrating crab density was calculated to be up to 3.20 ind. m−2 river bed. Resident crab density was calculated to be up to 2.05 ind. m−2 river bed. We conclude that this trap is a very useful tool for water managers to catch Chinese mitten crabs in rivers and discuss the pathways towards reducing the population and protect the entire freshwater catchment.

The aim of this research topic and paper is to investigate the application possibilities of vision technology in the textile industry. These include RGB, active thermography and hyperspectral imaging techniques. In the future, this approach will be supplemented by a machine learning algorithm (e.g., in Matlab or Python) to enable the detection of defects in textiles and to correctly categorize these defects. In the first place, the various options for building such a convolutional neural network are discussed. The focus was on the models used in the literature. Based on the effectiveness of these ML models and the feasibility to build them, choices can be made to determine the most suitable models. Sufficient samples are an important link to properly train a model. Because there is a shortage of open data, it is also discussed how samples obtained from the textile industry, were measured in the lab. At first, we will limit ourselves to the five most common defects. In a later phase of research, the results with this dataset and the open datasets are benchmarked against the results from the literature.