Lameness has a considerable influence on the welfare and health of dairy cows. Many attempts have been made to develop automatic lameness detection systems using computer vision technology. However, these detection methods are easily affected by the characteristics of individual cows, resulting in inaccurate detection of lameness. Therefore, this study explores an individualized lameness detection method for dairy cattle based on the supporting phase using computer vision. This approach is applied to eliminate the influence of the characteristics of individual cows and to detect lame cows and lame hooves. In this paper, the correlation coefficient between lameness and the supporting phase is calculated, a lameness detection algorithm based on the supporting phase is proposed, and the accuracy of the algorithm is verified. Additionally, the reliability of this method using computer vision technology is verified based on deep learning. One hundred naturally walking cows are selected from video data for analysis. The results show that the correlation between lameness and the supporting phase was 0.864; 96% of cows were correctly classified, and 93% of lame hooves were correctly detected using the supporting phase-based lameness detection algorithm. The mean average precision is 87.0%, and the number of frames per second is 83.3 when the Receptive Field Block Net Single Shot Detector deep learning network was used to detect the locations of cow hooves in the video. The results show that the supporting phase-based lameness detection method proposed in this paper can be used for the detection and classification of cow lameness and the detection of lame hooves with high accuracy. This approach eliminates the influence of individual cow characteristics and could be integrated into an automatic detection system and widely applied for the detection of cow lameness.

In this study, a stable, sensitive electrochemical sensor was fabricated by the electrochemical codeposition of reduced graphene oxide (rGO) and gold nanoparticles on a glassy carbon electrode (rGO-Au_nano/GCE) using cyclic voltammetry (CV), which enabled a simple and controllable electrode modification strategy for the determination of trace As(III) by square wave anodic stripping voltammetry (SWASV). SWASV, CV, electrochemical impedance spectroscopy (EIS), X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used to characterize the electrochemical properties and morphology of the proposed sensing platform. The number of sweep segments, the deposition potential and the deposition time were optimized to obtain ideal sensitivity. The presence of rGO from the electroreduction of graphene oxide on the sensing interface effectively enlarged the specific surface area and consequently improved the preconcentration capacity for As(III). The rGO-Au_nano/GCE sensor exhibited outstanding detection performance for As(III) due to the combined effect of Au_nano and rGO formed during the electroreduction process. Under the optimized conditions, a linear range from 13.375 × 10⁻⁹ to 668.75 × 10⁻⁹ mol/L (1.0 to 50.0 μg/L) was obtained with a detection limit of 1.07 × 10⁻⁹ mol/L (0.08 μg/L) (S/N = 3). The reproducibility and reliability of the rGO-Au_nano/GCE sensor were also verified by performing 8 repetitive measurements. Finally, the rGO-Au_nano/GCE sensor was used for the analysis of real samples with satisfactory results.

The computer vision technique has been rapidly adopted in cow lameness detection research due to its noncontact characteristic and moderate price. This paper attempted to summarize the research progress of computer vision in the detection of lameness. Computer vision lameness detection systems are not popular on farms, and the accuracy and applicability still need to be improved. This paper discusses the problems and development prospects of this technique from three aspects: detection methods, verification methods and application implementation. The paper aims to provide the reader with a summary of the literature and the latest advances in the field of computer vision detection of lameness in dairy cows.

Sensors for measuring plant phenotyping: A review

Mastitis is one of the most common diseases in dairy cows and has a negative impact on their welfare and life, causing significant economic losses to the dairy industry. Many attempts have been made to develop a detection method for mastitis using thermal infrared thermography. However, the use of this detection technique to determine the health of the cow's udder is susceptible to external factors, resulting in inaccurate detection of dairy cow mastitis. Therefore, this study explored a new and comprehensive detection method of dairy cow mastitis based on infrared thermal images. This method combined the left and right udder skin surface temperature (USST) difference detection method with the ocular surface temperature and USST difference detection method with improvements. The effect of external factors on dairy cow USST was effectively reduced. In addition, after comparing different target localisation algorithms, this paper used the You Only Look Once v5 (YOLOv5) deep learning network model to obtain the temperature information of eyes and udders, and mastitis detection of dairy cows was performed. A total of 105 dairy cows passing through a passage were randomly selected from the thermal infrared video and detected by the new and comprehensive detection method, and the results of cow mastitis detection were compared with somatic cell count. The results showed that the accuracy, specificity, and sensitivity of mastitis detection were 87.62, 84.62, and 96.30%, respectively. Using the YOLOv5 deep learning network model to locate the key parts of the cow had a good effect, with an average accuracy of 96.1%, and an average frame rate of 116.3f/s. The detection accuracy of dairy cow mastitis by deep learning technology combined with the detection method in this paper reached 85.71%. The results showed that the new and comprehensive detection method based on infrared thermal images can be used for the detection of dairy cow mastitis with high detection accuracy. This method can reduce the influence of external factors and can be integrated into the automatic identification system of dairy mastitis based on YOLOv5 to realise on-site monitoring of dairy mastitis.