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The objectives of this study were to quantify the error of body weight prediction using automatically measured morphological traits in a 3-dimensional (3-D) vision system and to assess the influence of various sources of uncertainty on body weight prediction. In this case study, an image acquisition setup was created in a cow selection box equipped with a top-view 3-D camera. Morphological traits of hip height, hip width, and rump length were automatically extracted from the raw 3-D images taken of the rump area of dairy cows (n = 30). These traits combined with days in milk, age, and parity were used in multiple linear regression models to predict body weight. To find the best prediction model, an exhaustive feature selection algorithm was used to build intermediate models (n = 63). Each model was validated by leave-one-out cross-validation, giving the root mean square error and mean absolute percentage error. The model consisting of hip width (measurement variability of 0.006 m), days in milk, and parity was the best model, with the lowest errors of 41.2 kg of root mean square error and 5.2% mean absolute percentage error. Our integrated system, including the image acquisition setup, image analysis, and the best prediction model, predicted the body weights with a performance similar to that achieved using semi-automated or manual methods. Moreover, the variability of our simplified morphological trait measurement showed a negligible contribution to the uncertainty of body weight prediction. We suggest that dairy cow body weight prediction can be improved by incorporating more predictive morphological traits and by improving the prediction model structure.

Machine vision technology has been used in automated body condition score (BCS) classification of dairy cows. The current vision-based classifications use information acquired from a limited number of body regions of the cow. Our study aimed to improve automated BCS classification by including multiple body condition-related features extracted from 3 viewpoints in 8 body regions. The data set of this study included 44 lactating cows with their BCS evenly distributed over the scale of BCS from 1.5 to 4.5 units. The body images of these cows were recorded over 2 consecutive days using 3-dimensional cameras positioned to view the cow from the top, right side, and rear. Each image was automatically processed to identify anatomical landmarks on the body surface. Around these anatomical landmarks, the bony prominences and body surface depressions were quantified to describe 8 body condition-related features. A manual BCS of each cow was independently assigned by 2 trained assessors using the same predefined protocol. With the extracted features as inputs and manual BCS as the reference, we built a nearest-neighbor classification model to classify BCS and obtained an overall classification sensitivity of 0.72 using a 10-fold cross-validation. We conclude that the sensitivity of automated BCS classification has been improved by expanding the selection of body condition-related features extracted from multiple body regions.

A novel Gram-stain-negative, rod-shaped, non-flagellated, strictly aerobic strain with gliding motility, designated SW024 T , was isolated from surface seawater of the South Pacific Gyre (266 299 S 1376 569 W) during the Integrated Ocean Drilling Program Expedition 329. Phylogenetic analyses based on 16S rRNA gene sequences showed that the novel strain was related most closely to Aquimarina muelleri KMM 6020 T , Aquimarina macrocephali JAMB N27 T , Aquimarina addita JC2680 T , Aquimarina mytili PSC33 T , Aquimarina intermedia KMM 6258 T , Aquimarina latercula ATCC 23177 T , Aquimarina spongiae A6 T , Aquimarina agarilytica ZC1 T and Aquimarina brevivitae SMK-19 T (96.1, 95.5, 95.0, 94.4, 94.3, 94.0, 93.4, 93.3 and 93.2 % similarities, respectively), demonstrating that the novel strain belonged to the genus Aquimarina. The DNA G+C content of strain SW024 T was 30.8 mol%. The major respiratory quinone of strain SW024 T was MK-6. The dominant fatty acids were iso-C 15 : 0 , iso-C 17 : 0 3-OH, iso-C 15 : 1 G, C 16 : 0 10-methyl and/or iso-C 17 : 1 v9c, and iso-C 15 : 0 3-OH. The polar lipids of strain SW024 T were phosphatidylethanolamine, three unknown aminolipids and five unknown polar lipids. On the basis of combined phenotypic and phylogenetic analyses, strain SW024 T represents a novel species of the genus Aquimarina, for which the name Aquimarina longa sp. nov. is proposed. The type strain is SW024 T (5CGMCC 1.11007 T 5JCM 17859 T ). An emended description of A. muelleri is also proposed.

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