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Image-based methods for species identification offer cost-efficient solutions for biomonitoring. This is particularly relevant for invertebrate studies, where bulk samples often represent insurmountable workloads for sorting, identifying, and counting individual specimens. On the other hand, image-based classification using deep learning tools have strict requirements for the amount of training data, which is often a limiting factor. Here, we examine how classification accuracy increases with the amount of training data using the BIODISCOVER imaging system constructed for image-based classification and biomass estimation of invertebrate specimens. We use a balanced dataset of 60 specimens of each of 16 taxa of freshwater macroinvertebrates to systematically quantify how classification performance of a convolutional neural network (CNN) increases for individual taxa and the overall community as the number of specimens used for training is increased. We show a striking 99.2% classification accuracy when the CNN (EfficientNet-B6) is trained on 50 specimens of each taxon, and also how the lower classification accuracy of models trained on less data is particularly evident for morphologically similar species placed within the same taxonomic order. Even with as little as 15 specimens used for training, classification accuracy reached 97%. Our results add to a recent body of literature showing the huge potential of image-based methods and deep learning for specimen-based research, and furthermore offers a perspective to future automatized approaches for deriving ecological data from bulk arthropod samples.
Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. In turn, this affects the scale of efforts to map invertebrate diversity altogether. Given recent advances in computer vision, we propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We describe a robot-enabled image-based identification machine, which can automate the process of invertebrate identification, biomass estimation and sample sorting.We use the imaging device to generate a comprehensive image database of terrestrial arthropod species. We use this database to test the classification accuracy i.e. how well the species identity of a specimen can be predicted from images taken by the machine. We also test sensitivity of the classification accuracy to the camera settings (aperture and exposure time) in order to move forward with the best possible image quality. We use state-of-the-art Resnet-50 and InceptionV3 Convolutional Neural Networks (CNNs) for the classification task.The results for the initial dataset are very promising (ACC = 0.980). The system is general and can easily be used for other groups of invertebrates as well. As such, our results pave the way for generating more data on spatial and temporal variation in invertebrate abundance, diversity and biomass.
<p>The properties and formation of clouds are one of the largest sources of uncertainties in climate models. Hereby, ice nucleating particles (INPs) play a major role since they directly affect the ice formation in clouds. To better characterize the impact of INPs, measuring devices are necessary to reliably determine the freezing temperatures of various aerosols.</p><p>We have developed a new ice nucleation assay, AU-Micro-INC, to measure the freezing temperatures with high accuracy. 96-well and 384-well plates can be inserted into a gallium matrix which ensures good thermal contact to the underlying cooling system. A Peltier element in combination with a vapor chamber provide a homogeneous cooling of the system. The freezing temperatures are measured with an infrared thermal camera with high precision.</p><p>The setup is validated using well-studied samples such as Snomax<sup>&#174;</sup> and Illite NX.&#160;Further, the new setup is applied to sea water, sea surface microlayer, and sea ice samples previously collected in Kobbefjord and Nuup Kangerluaand in proximity of Nuuk, Greenland and preliminary data will be shown.&#160;</p>
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