RESUMEN
The proof of origin of wood logs is becoming more and more important. In the context of Industry 4.0 and to combat illegal logging, there is an increased interest to track each individual log. There were already previous publications on wood log tracing using image data from logs, but these publications used experimental setups that cannot simulate a practical application where logs are tracked between different stages of the wood processing chain, like e.g. from the forest to the sawmill. In this work, we employ image data from the same 100 logs that were acquired at different stages of the wood processing chain (two datasets at the forest, one at a laboratory and two at the sawmill including one acquired with a CT scanner). Cross-dataset wood tracking experiments are applied using (a) the two forest datasets, (b) one forest and the RGB sawmill dataset and (c) different RGB datasets and the CT sawmill dataset. In our experiments we employ two CNN based method, 2 shape descriptors and two methods from the biometric areas of iris and fingerprint recognition. We will show that wood log tracing between different stages of the wood processing chain is feasible, even if the images at different stages are obtained at different image domains (RGB-CT). But it only works if the log cross sections from different stages of the wood processing chain either offer a good visibility of the annual ring pattern or share the same woodcut pattern.
RESUMEN
Precision fish farming is an emerging concept in aquaculture research and industry, which combines new technologies and data processing methods to enable data-based decision making in fish farming. The concept is based on the automated monitoring of fish, infrastructure, and the environment ideally by contactless methods. The identification of individual fish of the same species within the cultivated group is critical for individualized treatment, biomass estimation and fish state determination. A few studies have shown that fish body patterns can be used for individual identification, but no system for the automation of this exists. We introduced a methodology for fully automatic Atlantic salmon (Salmo salar) individual identification according to the dot patterns on the skin. The method was tested for 328 individuals, with identification accuracy of 100%. We also studied the long-term stability of the patterns (aging) for individual identification over a period of 6 months. The identification accuracy was 100% for 30 fish (out of water images). The methodology can be adapted to any fish species with dot skin patterns. We proved that the methodology can be used as a non-invasive substitute for invasive fish tagging. The non-invasive fish identification opens new posiblities to maintain the fish individually and not as a fish school which is impossible with current invasive fish tagging.
