Automatic measurement of Acartia tonsa nauplii density, and estimation of stage distribution

Effect of cultivated copepods (Acartia tonsa) in first-feeding of Atlantic cod (Gadus morhua) and ballan wrasse (Labrus bergylta) larvae

“…; Alver et al . ), resulting in the same particle size being available to the fish regardless of feeding regime.…”

“…Thereafter, the fish larvae were fed four times a day and live feed density of was assessed by an automatic counter (Alver et al . , ). All larvae had a co‐feeding period with Artemia sp .…”

Potential effects of shoaling of oxygen minimum zone on the population of Acartia (Acanthacartia) tonsa in the Humboldt Current Upwelling System (23°S)

“…The naupliar stages were identified under a microscope (10× -40×), following the characteristics of the developmental stages described by Murphy and Cohen (1978) and Alver et al (2011). Egg diameter and cephalosome length and width of nauplii (N1 and N2) were measured (μm ± 1 SD) under a stereomicroscope (40× -80×) equipped with a digital camera and the software Micrometric SE Premium 4.…”

Automated Software for Counting and Measuring Hyalella Genus Using Artificial Intelligence

“…These techniques facilitate various practical tasks in insect research that involve observation, quanti cation, classi cation, measurement, and other data that can be inferred from images (Weinstein, 2017). Some recent examples of the applications of these techniques in insects and aquatic invertebrates include the application of digital imaging processing techniques to count fruit ies (Yati y Dey, 2011); automatic ne-grained classi cation of benthic macroinvertebrates through the use of convolutional neural networks (CNN) (Raitoharju et al, 2018); automatic measurement of the density of the copepod Acartia tonsa (Alver et al, 2011); sorting, identi cation and biomass estimation of terrestrial invertebrates by image-based identi cation machine (Ärje et al, 2020); morphological descriptors in artic zooplankton community measured from in situ images (Vilgrain et al, 2021); automatic sorting and extraction of metrics such as body length and volume from 14 common terrestrial invertebrate specimens (Wührl et al, 2021); identi cation of movement patterns changes in Oryzias latipes (Park et al, 2005) and Daphnia magna (Untersteiner et al, 2003) using digital image processing; development of the software ImageJ as an accurate and exible method to automate the analysis of digital photographs of the laboratory microcosm to detect, count and measure organisms such as collembola, ants, nematodes and daphnias moving on a xed but heterogeneous substrate (Mallard et al, 2013); or changes in color of Ulva pertusa macroalgae (Lee 2020); a method to extract the mass of migratory insects based on an ellipsoidal dispersion model using a radar (Kong et al, 2019); segmentation and counting of pollen grains in microscopic images (Johnsrud 2013); the processing and classi cation of four groups of aquatic macroinvertebrates (Thraulodes, Traverella, Anacroneuria and Smicridea) through digital images processing (Serna López et al, 2020); and the registration and classi cation of Rhopalosiphum padi aphids to assess and predict crop damage by image processing, machine vision and machine learning through the Aphid CV software (Lins et al, 2020).…”