Continuous Monitoring of Pigs in Fattening Using a Multi-Sensor System: Behavior Patterns

Automatic Recognition and Quantification Feeding Behaviors of Nursery Pigs Using Improved YOLOV5 and Feeding Functional Area Proposals

A novel method is proposed based on the improved YOLOV5 and feeding functional area proposals to identify the feeding behaviors of nursery piglets in a complex light and different posture environment. The method consists of three steps: first, the corner coordinates of the feeding functional area were set up by using the shape characteristics of the trough proposals and the ratio of the corner point to the image width and height to separate the irregular feeding area; second, a transformer module model was introduced based on YOLOV5 for highly accurate head detection; and third, the feeding behavior was recognized and counted by calculating the proportion of the head in the located feeding area. The pig head dataset was constructed, including 5040 training sets with 54,670 piglet head boxes, and 1200 test sets, and 25,330 piglet head boxes. The improved model achieves a 5.8% increase in the mAP and a 4.7% increase in the F1 score compared with the YOLOV5s model. The model is also applied to analyze the feeding pattern of group-housed nursery pigs in 24 h continuous monitoring and finds that nursing pigs have different feeding rhythms for the day and night, with peak feeding periods at 7:00-9:00 and 15:00-17:00 and decreased feeding periods at 12:00-14:00 and 0:00-6:00. The model provides a solution for identifying and quantifying pig feeding behaviors and offers a data basis for adjusting the farm feeding scheme.

Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming

Pork accounts for an important proportion of livestock products. For pig farming, a lot of manpower, material resources and time are required to monitor pig health and welfare. As the number of pigs in farming increases, the continued use of traditional monitoring methods may cause stress and harm to pigs and farmers and affect pig health and welfare as well as farming economic output. In addition, the application of artificial intelligence has become a core part of smart pig farming. The precision pig farming system uses sensors such as cameras and radio frequency identification to monitor biometric information such as pig sound and pig behavior in real-time and convert them into key indicators of pig health and welfare. By analyzing the key indicators, problems in pig health and welfare can be detected early, and timely intervention and treatment can be provided, which helps to improve the production and economic efficiency of pig farming. This paper studies more than 150 papers on precision pig farming and summarizes and evaluates the application of artificial intelligence technologies to pig detection, tracking, behavior recognition and sound recognition. Finally, we summarize and discuss the opportunities and challenges of precision pig farming.

The Feeding Behaviour Habits of Growing-Finishing Pigs and Its Effects on Growth Performance and Carcass Quality: A Review

Based on the available data of feeding behaviour habits (FBHs), this work aimed to discuss which type of pig, according to its FBHs, performs better and is more efficient. As pigs grow, average daily feed intake, meal size, and feeding rate increase, whereas small variations or even decreases in time spent eating and daily feeder visits have been reported. Moreover, the sex, breed, space allowance, feeder design, feed form, diet composition, and environmental conditions modify FBHs. On the other hand, the literature indicates the existence of four types of pigs: pigs that eat their daily feed intake in many short meals (nibblers) or in few large meals (meal eaters) combined with eating fast (faster eaters) or slow (slow eaters). The available scientific literature about ad libitum fed pigs suggests that pigs eating faster with bigger meals eat more, gain more weight, and are fatter than pigs eating less, slower, and with smaller meals. However, the feeding rate and the meal size do not influence feed efficiency. In conclusion, studies comparing growing-finishing pigs with similar feed intake, but different feeding rate and meal size are needed to better understand the influence of FBHs on feed efficiency.

Circadian rhythms of the mRNA abundances of clock genes and glucose transporters in the jejunum of weanling-growing pigs

Background

Whether abundance of glucose transporter mRNAs in the small intestine of pigs shows circadian rhythms and its regulation by clock genes was still unknown.

Objectives

We examined whether the abundance of glucose transporters and clock genes mRNAs in the small intestine of pigs shows circadian rhythms.

Methods

Twenty barrows (4 weeks old) were reared under 12 h bright and 12 h dark lighting conditions. During the 3‐week feeding trial, pigs were allowed free access to feed. The abundances of the mRNA of glucose transporters (SGLT1 and GLUT2) and clock genes (Bmal1, Per1, Per2, and Cry2) in the intestine were measured at four time points (ZT2, ZT8, ZT14, and ZT20).

Results

In the jejunum, the abundance of SGLT1 mRNA was higher at ZT20 and ZT2 and lower at ZT8 and ZT14 (p < 0.05). The abundances of GLUT2 mRNA in the jejunum at ZTs 20 and 2 were tended to be higher than those at ZTs 8 and 14 (p = 0.05). In the jejunum, the abundance of Bmal1 mRNA was higher at ZT8 and ZT14 than at ZT20 and ZT2 (p < 0.05). Further, the abundance of Per1 mRNA at ZT2 was higher than those at the other sampling times (p < 0.05). The abundance of Per1 mRNA at ZT8 was higher than that at ZT14 (p < 0.05), while that of Per2 mRNA was higher at ZT2 than those at ZTs 20 and 14 (p < 0.05).

Conclusion

We speculate that these circadian rhythms of abundances of glucose transporter mRNAs are regulated by the clock genes expressed in the jejunum.

Transforming the Adaptation Physiology of Farm Animals through Sensors

Simple Summary

Strategy for the protection and welfare of farm animals, and the sustainable animal production is dependent on the thorough understanding of the adaptation physiology. Real-time, continuous, and precise measurement of the multi-dimensions and complex intricacies of adaptive capacity of farm animals namely the mental, behavioral, and physiological states are possible only through the sensor-based approaches. This paper critically reviews the latest sensor technologies as assessment tools for the adaptation physiology of farm animals and explores their advantages over traditional measurement methods. Digital innovation, diagnostics, genetic testing, biosensors, and wearable animal devices are important tools that enable the development of decision support farming platforms and provides the path for predicting diseases in livestock. Sensor fusion data from a multitude of biochemical, emotional, and physiological functions of the farm animals not only helps to identify the most productive animal but also allows farmers to predict which individual animal may have greater resilience to common diseases. Insights into the cost of adoption of sensor technologies on farms including computing capacity, human resources in training, and the sensor hardware are being discussed.

Abstract

Despite recent scientific advancements, there is a gap in the use of technology to measure signals, behaviors, and processes of adaptation physiology of farm animals. Sensors present exciting opportunities for sustained, real-time, non-intrusive measurement of farm animal behavioral, mental, and physiological parameters with the integration of nanotechnology and instrumentation. This paper critically reviews the sensing technology and sensor data-based models used to explore biological systems such as animal behavior, energy metabolism, epidemiology, immunity, health, and animal reproduction. The use of sensor technology to assess physiological parameters can provide tremendous benefits and tools to overcome and minimize production losses while making positive contributions to animal welfare. Of course, sensor technology is not free from challenges; these devices are at times highly sensitive and prone to damage from dirt, dust, sunlight, color, fur, feathers, and environmental forces. Rural farmers unfamiliar with the technologies must be convinced and taught to use sensor-based technologies in farming and livestock management. While there is no doubt that demand will grow for non-invasive sensor-based technologies that require minimum contact with animals and can provide remote access to data, their true success lies in the acceptance of these technologies by the livestock industry.