Dandelion (Taraxacum officinale), a common plant worldwide, is used as both a medicine and food. Fermentation is a food processing technology that has many advantages, such as low energy cost, changes in product characteristics, and enhanced product quality. The purpose of this study was to investigate the effect of solid-state fermentation (SSF) on the content, composition and antioxidant activity of dandelion flavonoids. Response surface methodology was used to optimize dandelion fermentation conditions. Under optimized conditions, the maximum flavone concentration was 66.05 ± 1.89 mg/g. The flavonoid content of the crude extract from fermented dandelion (FDF) was 183.72 ± 2.24 mg/g. The flavonoid compounds in the crude extracts were further identified by UPLC-ESI-MS/MS. A total of 229 flavonoid compounds were identified, and 57 differential flavonoids (including 27 upregulated and 30 downregulated flavonoids) between FDF and the crude extract of unfermented dandelion (DF) were observed, of which 25 were annotated to metabolic pathways. FDF displayed superior antioxidant activity to that of DF in in vitro DPPH radical-scavenging and reducing power assays. The favorable results of our investigation could provide a new way for the exploitation and utilization of dandelion, which could be promising for its application as an antioxidant and functional food additive with flavonoids as ingredients.

Milk production plays an essential role in the global economy. With the development of herds and farming systems, the collection of fine-scale data to enhance efficiency and decision-making on dairy farms still faces challenges. The behavior of animals reflects their physical state and health level. In recent years, the rapid development of the Internet of Things (IoT), artificial intelligence (AI), and computer vision (CV) has made great progress in the research of precision dairy farming. Combining data from image, sound, and movement sensors with algorithms, these methods are conducive to monitoring the behavior, health, and management practices of dairy cows. In this review, we summarize the latest research on contact sensors, vision analysis, and machine-learning technologies applicable to dairy cattle, and we focus on the individual recognition, behavior, and health monitoring of dairy cattle and precise feeding. The utilization of state-of-the-art technologies allows for monitoring behavior in near real-time conditions, detecting cow mastitis in a timely manner, and assessing body conditions and feed intake accurately, which enables the promotion of the health and management level of dairy cows. Although there are limitations in implementing machine vision algorithms in commercial settings, technologies exist today and continue to be developed in order to be hopefully used in future commercial pasture management, which ultimately results in better value for producers.

Behavior classification and recognition of sheep are useful for monitoring their health and productivity. The automatic behavior classification of sheep by using wearable devices based on IMU sensors is becoming more prevalent, but there is little consensus on data processing and classification methods. Most classification accuracy tests are conducted on extracted behavior segments, with only a few trained models applied to continuous behavior segments classification. The aim of this study was to evaluate the performance of multiple combinations of algorithms (extreme learning machine (ELM), AdaBoost, stacking), time windows (3, 5 and 11 s) and sensor data (three-axis accelerometer (T-acc), three-axis gyroscope (T-gyr), and T-acc and T-gyr) for grazing sheep behavior classification on continuous behavior segments. The optimal combination was a stacking model at the 3 s time window using T-acc and T-gyr data, which had an accuracy of 87.8% and a Kappa value of 0.836. It was applied to the behavior classification of three grazing sheep continuously for a total of 67.5 h on pasture with three different sward surface heights (SSH). The results revealed that the three sheep had the longest walking, grazing and resting times on the short, medium and tall SHH, respectively. These findings can be used to support grazing sheep management and the evaluation of production performance.

Fundamental sheep behaviours, for instance, walking, standing, and lying, can be closely associated with their physiological health. However, monitoring sheep in grazing land is complex as limited range, varied weather, and diverse outdoor lighting conditions, with the need to accurately recognise sheep behaviour in free range situations, are critical problems that must be addressed. This study proposes an enhanced sheep behaviour recognition algorithm based on the You Only Look Once Version 5 (YOLOV5) model. The algorithm investigates the effect of different shooting methodologies on sheep behaviour recognition and the model’s generalisation ability under different environmental conditions and, at the same time, provides an overview of the design for the real-time recognition system. The initial stage of the research involves the construction of sheep behaviour datasets using two shooting methods. Subsequently, the YOLOV5 model was executed, resulting in better performance on the corresponding datasets, with an average accuracy of over 90% for the three classifications. Next, cross-validation was employed to verify the model’s generalisation ability, and the results indicated the handheld camera-trained model had better generalisation ability. Furthermore, the enhanced YOLOV5 model with the addition of an attention mechanism module before feature extraction results displayed a mAP_@0.5 of 91.8% which represented an increase of 1.7%. Lastly, a cloud-based structure was proposed with the Real-Time Messaging Protocol (RTMP) to push the video stream for real-time behaviour recognition to apply the model in a practical situation. Conclusively, this study proposes an improved YOLOV5 algorithm for sheep behaviour recognition in pasture scenarios. The model can effectively detect sheep’s daily behaviour for precision livestock management, promoting modern husbandry development.

Ferulic acid (FA) has been regarded as an antioxidant in domestic animals’ feed. This study was to investigate whether dietary FA supplementation could improve growth performance by decreasing oxidative stress of lambs in cold environment. Thirty-two 3-mo-old crossbred male lambs (Dorper × Small-tail Han sheep; 30.49 ± 0.46 kg) were randomly assigned into one of the following dietary treatments: CON (control, no FA), FA80 (80 mg FA kg⁻¹ of diet), FA400 (400 mg FA kg⁻¹ of diet), and FA2000 (2000 mg FA kg⁻¹ of diet). Lambs fed the FA80 had higher (P < 0.05) average daily gain and lower (P < 0.05) feed efficiency than those fed CON and FA2000. The dry matter, organic matter, and neutral detergent fiber apparent digestibility was lower (P < 0.05) for FA2000 lambs than for lambs from other treatments. Serum total protein and albumin concentrations were greater (P < 0.05) for FA80 group than other groups. Lambs fed FA80 had higher (P < 0.05) plasma glutathione peroxidase and catalase activities and lower (P < 0.05) malondialdehyde (MDA) content than lambs fed CON. However, FA2000 group showed higher (P < 0.05) plasma MDA content than CON group. In conclusion, dietary supplementation of 80 mg FA kg⁻¹ diet could improve growth performance by decreasing oxidative stress of lambs in cold environment.

Rice bran is usually mixed into animal feeds or discarded directly because of its harsh taste and undesirable flavor. Its bioavailability and exploitation are limited. In order to enhance the antioxidant properties of rice bran, the solid-state fermentation of rice bran with mixed bacteria was adopted in addition to the optimization of the fermentation technology. The bioactive constituents of water-soluble extracts and the in vivo antioxidant activity were compared before and after fermentation. The results revealed that the DPPH free radical scavenging rate of the water-soluble rice bran extracts under optimized fermentation technology conditions reached 86.38%, which was a 54.06% increase compared to that of raw materials. The mixed-bacteria solid-state fermentation increased the levels of bioactive constituents, including the polyphenols, flavonoid, protein, uronic acid, mannose, catechinic acid, caffeic acid, and ferulic acid contents. In a zebrafish model, the water-soluble fermented rice bran extracts (FRBE) displayed superior protective effects against 2,2′-azobis (2-methylpropionamidine) dihydrochloride (AAPH)-induced reactive oxygen species (ROS) production, lipid peroxidation, and cell death, and FRBE significantly improved SOD and CAT activity against the induced AAPH. Taken together, mixed-bacteria solid-state fermentation could change the bioactive constituents and enhance the antioxidant activity of water-soluble extracts from rice bran.