Jeppe Have Rasmussen

• Briefer, Elodie F.; Sypherd, Ciara C. -R.; Linhart, Pavel; Leliveld, Lisette M. C.; Padilla_de_la_Torre, Monica & Read, Eva R. [Show all 16 contributors for this article] (2022). Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production. Scientific Reports. ISSN 2045-2322. 12. doi: 10.1038/s41598-022-07174-8. Full text in Research Archive Show summary

  Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm.

• Rasmussen, Jeppe Have; Moyano, Marta; Fuiman, Lee A. & Oomen, Rebekah Alice (2022). FishSizer: Software solution for efficiently measuring larval fish size. Ecology and Evolution. ISSN 2045-7758. 12(3), p. 1–10. doi: 10.1002/ece3.8672. Full text in Research Archive Show summary

  Length and depth of fish larvae are part of the fundamental measurements in many marine ecology studies involving early fish life history. Until now, obtaining these measurements has required intensive manual labor and the risk of inter- and intra-observer variability. We developed an open-source software solution to semi-automate the measurement process and thereby reduce both time consumption and technical variability. Using contrast-based edge detection, the software segments images of a fish larva into “larva” and “background.” Length and depth are extracted from the “larva” segmentation while taking curvature of the larva into consideration. The graphical user interface optimizes workflow and ease of usage, thereby reducing time consumption for both training and analysis. The software allows for visual verification of all measurements. A comparison of measurement methods on a set of larva images showed that this software reduces measurement time by 66%–78% relative to commonly used software. Using this software instead of the commonly used manual approach has the potential to save researchers from many hours of monotonous work. No adjustment was necessary for 89% of the images regarding length (70% for depth). Hence, the only workload on most images was the visual inspection. As the visual inspection and manual dimension extraction works in the same way as currently used software, we expect no loss in accuracy.

• Goodwin, Morten; Halvorsen, Kim Aleksander Tallaksen; Jiao, Lei; Knausgård, Kristian Muri; Martin, Angela Helen & Moyano, Marta [Show all 10 contributors for this article] (2022). Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook. ICES Journal of Marine Science. ISSN 1054-3139. 79(2), p. 319–336. doi: 10.1093/icesjms/fsab255. Full text in Research Archive Show summary

  The deep learning (DL) revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. New methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid. Off-the-shelf algorithms find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. These endeavours require collaboration across ecological and data science disciplines, which can be challenging to initiate. To promote the use of DL towards ecosystem-based management of the sea, this paper aims to bridge the gap between marine ecologists and computer scientists. We provide insight into popular DL approaches for ecological data analysis, focusing on supervised learning techniques with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of DL to marine ecology. We present case studies on plankton, fish, marine mammals, pollution, and nutrient cycling that involve object detection, classification, tracking, and segmentation of visualized data. We conclude with a broad outlook of the field’s opportunities and challenges, including potential technological advances and issues with managing complex data sets.

• Waddell, Emily E.; Rasmussen, Jeppe Have & Sirovic, Ana (2021). Applying artificial intelligence methods to detect and classify fish calls from the northern gulf of mexico. Journal of Marine Science and Engineering (JMSE). ISSN 2077-1312. 9(10), p. 1–19. doi: 10.3390/jmse9101128. Full text in Research Archive

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• Briefer, Elodie F.; Sypherd, Ciara C.‑R.; Linhart, Pavel; Leliveld, Lisette M. C.; Padilla de la Torre, Monica & Read, Eva R. [Show all 16 contributors for this article] (2023). Author Correction: Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production (Scientific Reports, (2022), 12, 1, (3409), 10.1038/s41598-022-07174-8). Scientific Reports. ISSN 2045-2322. 13(1). doi: 10.1038/s41598-023-45242-9.

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