The Potentialities of Machine Learning for Cow-Specific Milking: Automatically Setting Variables in Milking Machines.

In dairy farming, milking-related operations are time-consuming and expensive, but are also directly linked to the farm's economic profit. Therefore, reducing the duration of milking operations without harming the cows is paramount. This study aimed to test the variation in different parameters of milking operations on non-automatic milking machines to evaluate their effect on a herd and finally reduce the milking time. Two trials were set up on a dairy farm in Northern Italy to explore the influence of the pulsation ratio (60:40 vs. 65:35 pulsation ratio) and that of the detachment flow rate (600 g/min vs. 800 g/min) on milking performance, somatic cell counts, clinical mastitis, and teats score. Moreover, the innovative aspect of this study relates to the development of an optimized least-squares support vector machine (LSSVM) classification model based on the sparrow search algorithm (SSA) to predict the proper pulsation ratio and detachment flow rate for individual cows within the first two minutes of milking. The accuracy and precision of this model were 92% and 97% for shortening milking time at different pulsation ratios, and 78% and 79% for different detachment rates. The implementation of this algorithm in non-automatic milking machines could make milking operations cow-specific.

Contactless Video-Based Heart Rate Monitoring of a Resting and an Anesthetized Pig.

Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism's state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm. The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in . The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.

An Automatic Head Surface Temperature Extraction Method for Top-View Thermal Image with Individual Broiler.

Surface temperature variation in a broiler's head can be used as an indicator of its health status. Surface temperatures in the existing thermograph based animal health assessment studies were mostly obtained manually. 2185 thermal images, each of which had an individual broiler, were captured from 20 broilers. Where 15 broilers served as the experimental group, they were injected with 0.1mL of pasteurella inoculum. The rest, 5 broilers, served as the control group. An algorithm was developed to extract head surface temperature automatically from the top-view broiler thermal image. Adaptive K-means clustering and ellipse fitting were applied to locate the broiler's head region. The maximum temperature inside the head region was extracted as the head surface temperature. The developed algorithm was tested in Matlab® (R2016a) and the testing results indicated that the head region in 92.77% of the broiler thermal images could be located correctly. The maximum error of the extracted head surface temperatures was not greater than 0.1 °C. Different trend features were observed in the smoothed head surface temperature time series of the broilers in experimental and control groups. Head surface temperature extracted by the presented algorithm lays a foundation for the development of an automatic system for febrile broiler identification.

A controlled heat stress during late gestation affects thermoregulation, productive performance, and metabolite profiles of primiparous sow.

Heat stress (HS) alters metabolic parameters and reduces productive performance in lactating sows. However, the impact of HS on metabolomic profiles of sows during late gestation is not fully understood. We present here, a study investigating the productive performance and metabolic responses in sows when exposed to HS during late gestation. Twelve first-parity Landrace × Large White F1 sows were randomly assigned into two environmental treatments including the thermoneutral (TN) (18-22 °C; n = 6) and HS (28-32 °C; n = 6) conditions from 85 d of gestation until farrowing. Rectal temperature (RT), respiration rates (RR), and surface temperature (ST) were measured every 4 h from 0800 h to 2000 h during the 2nd week. Farrowing and litter Data, as well as duration of eating, were monitored to assess sows' productive performance. Blood biochemical parameters and urinary metabolomic profiles were measured on d107 of gestation to analyze the host metabolic responses. Our results show that HS increased RT, RR, and ST (P < 0.0001). Duration of parturition was prolonged during the delivery in HS group (P < 0.05). Piglet body weight (BW) at d 10 and weaning were reduced by 18% and 17% respectively due to maternal HS (P < 0.001). Duration of eating increased as a result of HS (P < 0.001), consistent with the significant changes observed in serum ghrelin (P < 0.05). Moreover, serum ACTH, cortisol, insulin, creatinine, and BUN saw increase as well (P < 0.05). Plasma NEFA were elevated by HS (P < 0.001). Additionally, HS elevated (VIP>1, log2fold change>0.585, and P < 0.05) the relative concentrations of 5-aminovaleric acid, ß-alanine, cysteine, isoleucine, glyceric acid, erythronic acid, mannitol, erythritol, 2-methyl-1,3-butanediol, and pantothenic acid in urine. These ten metabolites mainly affected the pantothenate and CoA biosynthesis, ß-alanine metabolism, and glycerolipid metabolism in pregnant sows. In summary, our study suggests that the controlled HS during late gestation elevates thermal responses, reduces productive performance, and more importantly, enhances the catabolism of lipid and protein of first-parity pregnant sow.

[Dimensional fractal of post-paddy wheat root architecture].

To evaluate whether crop rooting system was directionally dependent, a field digitizer was used to measure post-paddy wheat root architectures. The acquired data was transferred to Pro-E, in which virtual root architecture was reconstructed and projected to a series of planes each separated in 10° apart. Fractal dimension and fractal abundance of root projections in all the 18 planes were calculated, revealing a distinctive architectural distribution of wheat root in each direction. This strongly proved that post-paddy wheat root architecture was directionally dependent. From seedling to turning green stage, fractal dimension of the 18 projections fluctuated significantly, illustrating a dynamical root developing process in the period. At the jointing stage, however, fractal indices of wheat root architecture resumed its regularity in each dimension. This wheat root architecture recovered its dimensional distinctness. The proposed method was applicable for precision modeling field state root distribution in soil.