Laboratory experiments were conducted using magnesium chloride (MgCl₂·6H₂O, 64% solution) to force the precipitation of phosphorus and reduce the concentration of soluble phosphorus (PO₄^3-) in two swine wastes. One of the swine wastes tested contained a high concentration of PO43- (initially ≈1,000 mg/L), and the other swine waste tested contained a low concentration of PO₄^3- (initially ≈230 mg/L). The precipitation reactions were performed to determine the required reaction time, pH, magnesium addition rate and seed material for future precipitate recovery work. For the high and low concentration waste, a 10-minute reaction time at a pH of 8.6 was sufficient to remove 98 and 96% of the PO₄^3- from solution. A molar ratio of Mg²⁺:PO₄^3- of 1.6:1 was determined to be effective for PO₄^3- removal from both the low and high strength wastes. At a molar ratio of 1.6:1, the PO₃^- in the high concentration waste was reduced from 590 to 12 mg/L. In the low concentration waste, the PO₄^3- concentration was reduced from 157 to 15 mg/L. Seeding the reaction did not significantly enhance the recovery process.

Monitoring the drinking behavior of animals can provide important information for livestock farming, including the health and well-being of the animals. Measuring drinking time is labor-demanding and, thus, it is still a challenge in most livestock production systems. Computer vision technology using a low-cost camera system can be useful in overcoming this issue. The aim of this research was to develop a computer vision system for monitoring beef cattle drinking behavior. A data acquisition system, including an RGB camera and an ultrasonic sensor, was developed to record beef cattle drinking actions. We developed an algorithm for tracking the beef cattle’s key body parts, such as head–ear–neck position, using a state-of-the-art deep learning architecture DeepLabCut. The extracted key points were analyzed using a long short-term memory (LSTM) model to classify drinking and non-drinking periods. A total of 70 videos were used to train and test the model and 8 videos were used for validation purposes. During the testing, the model achieved 97.35% accuracy. The results of this study will guide us to meet immediate needs and expand farmers’ capability in monitoring animal health and well-being by identifying drinking behavior.

Digital agriculture is increasingly being incorporated into all areas of agricultural production, but using different names. Names, including precision agriculture, digital horticulture, and precision forestry, are used within row crop, horticulture, and forestry systems, respectively. Within livestock production systems, digital agriculture is commonly called “precision livestock farming” or abbreviated PLF. The application of digital agriculture, or PLF, in swine production systems involves the digitization of all aspects of production. Examples of PLF within swine production systems include feeding, watering, ventilation, environmental control, disease detection, animal welfare, and many additional aspects of animal health management. A wide variety of sensors and algorithmic systems are used for real-time monitoring and control in pig production systems. Methods, including computer vision, sound, temperature, and movement sensing, are used to collect data that is in-turn analyzed by digital systems in order to make management decisions in real time. This chapter provides an overview of these applications and describes the current state of the science regarding the use of PLF via digital agriculture in swine production.

Precision Livestock Farming (PLF) involves the real-time monitoring of images, sounds, and other biological, physiological, and environmental parameters to assess and improve animal health and welfare within intensive and extensive production systems

The global cattle industry is experiencing significant growth, requiring advanced methods for monitoring animal health and welfare to ensure productivity and sustainability. Traditional manual monitoring techniques are labor-intensive and often impractical for large-scale operations. This review provides a comprehensive analysis of existing and emerging computer vision tools applied to the monitoring of cattle health and welfare. By systematically examining studies across major databases, this paper addresses six key research questions focusing on (1) the issues addressed by computer vision technologies, (2) data acquisition systems, (3) implemented techniques and algorithms, (4) performance outcomes, (5) challenges faced, and (6) potential applications for underexplored health and welfare aspects in cattle farming. The findings show that computer vision technologies have significantly progressed in areas such as body condition score detection, lameness detection, weight estimation, estrus detection, monitoring of feeding and drinking behavior, breathing detection, and recognition of general behaviors. Despite the progress, challenges such as variability in environmental conditions, the need for large annotated datasets, and the high cost of advanced imaging equipment persist. The review emphasizes future research opportunities to address these challenges by focusing on disease-specific monitoring. This review aims to provide veterinarians, farmers, and animal health professionals with greater insight into computer vision technologies and to promote their adoption by discussing their practical applications.