Indicators of mastitis and milk quality in dairy cows : data, modeling, and prediction in automatic milking systems

Methods for generating predictions of important and generally accepted indicators of udder inflammation and poor milk quality, such as somatic cell count (SCC) or changes in milk homogeneity, are few. The aim of this thesis was to investigate methods to identify indicators of mastitis and poor milk quality in dairy cows using data generated by automatic milking systems (AMS). The first part of the project investigated the relationship between SCC and data regularly recorded by the AMS using models that could capture nonlinear associations between the explanatory variables and the outcome. This information could be used in modeling the SCC. Furthermore, three statistical methods, generalized additive model, random forest and multilayer perceptron, were compared for their ability to predict SCC using data generated by the AMS. The results showed that equally low prediction error was obtained using generalized additive model or multilayer perceptron for prediction of SCC based on AMS data. The second part explored the dynamics of changes in milk homogeneity in cows milked in AMS using descriptive statistics for clots collected by inline filters, scored for density. Clots were found among certain cows and cow periods and appeared in new quarters over time. Models were fitted for detecting and predicting clots in single cow milkings as well as for detecting clots in milkings over a longer period. The models successfully distinguished periods of milking free of changes in milk homogeneity, although the detection and prediction performance was poor. The prediction target and severity grade of each density category is discussed

Machine-Learning-Powered Cyber-Physical Systems

In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety of services and applications across multiple domains. Machine learning processes and analyses such data to generate a feedback, which represents a status the environment is in. A feedback given to the user in order to make an informed decision is called an open-loop feedback. Thus, an open-loop CPS is characterized by the lack of an actuation directed at improving the system itself. A feedback used by the system itself to actuate a change aimed at optimizing the system itself is called a closed-loop feedback. Thus, a closed-loop CPS pairs feedback based on sensing data with an actuation that impacts the system directly. In this dissertation, we propose several applications in the context of CPS. We propose open-loop CPSs designed for the early prediction, diagnosis, and persistency detection of Bovine Respiratory Disease (BRD) in dairy calves, and for gait activity recognition in horses.These works use sensor data, such as pedometers and automated feeders, to perform valuable real-field data collection. Data are then processed by a mix of state-of-the-art approaches as well as novel techniques, before being fed to machine learning algorithms for classification, which informs the user on the status of their animals. Our work further evaluates a variety of trade-offs. In the context of BRD, we adopt optimization techniques to explore the trade-offs of using sensor data as opposed to manual examination performed by domain experts. Similarly, we carry out an extensive analysis on the cost-accuracy trade-offs, which farmers can adopt to make informed decisions on their barn investments. In the context of horse gait recognition we evaluate the benefits of lighter classifications algorithms to improve energy and storage usage, and their impact on classification accuracy. With respect to closed-loop CPS we proposes an incentive-based demand response approach for Heating Ventilation and Air Conditioning (HVAC) designed for peak load reduction in the context of smart grids. Specifically, our approach uses machine learning to process power data from smart thermostats deployed in user homes, along with their personal temperature preferences. Our machine learning models predict power savings due to thermostat changes, which are then plugged into our optimization problem that uses auction theory coupled with behavioral science. This framework selects the set of users who fulfill the power saving requirement, while minimizing financial incentives paid to the users, and, as a consequence, their discomfort. Our work on BRD has been published on IEEE DCOSS 2022 and Frontiers in Animal Science. Our work on gait recognition has been published on IEEE SMARTCOMP 2019 and Elsevier PMC 2020, and our work on energy management and energy prediction has been published on IEEE PerCom 2022 and IEEE SMARTCOMP 2022. Several other works are under submission when this thesis was written, and are included in this document as well

Prediction of poor health in small ruminants and companion animals with accelerometers and machine learning

Global warming is one of the biggest challenge of our times, and significant efforts are being undertaken by academics, industries and other actors to tackle the problem. In the agricultural field precision farming is part of the solution to environmental sustainability and has been researched increasingly in recent years. Indeed, it has the potential to effectively increase livestock yield and decrease production carbon footprint while maintaining welfare. The thesis begins with a review of developments in automated animal monitoring and then moves on to a case study of a health monitoring system for small-ruminant in South Africa. As a demonstration and validation of the potential use case of the system, the method we propose is then applied to another study which aims to study feline health. Lower and Middle Income countries will be strongly affected by the changing climate and its impacts. We devise our method based on two South African small scale sheep and goat farms where assessment of the health status of individual animals is a key step in the timely and targeted treatment of infections, which is critical in the fight against anthelmintic and antimicrobial resistance. The FAMACHA scoring system has been used successfully to detect anaemia caused by infection with the parasitic nematode Haemonchus contortus in small ruminants and is an effective way to identify individuals in need of treatment. However, assessing FAMACHA is labour-intensive and costly as individuals must be manually examined at frequent intervals. Here, we used accelerometers to measure the individual activity of extensively grazed small ruminants exposed to natural Haemonchus contortus worm infection in southern Africa over long time scales (13+ months). When combined with machine learning for missing data imputation and classification, we find that this activity data can predict poorer health as well as those individuals that respond to treatment, with precision up to 80%. We demonstrate that these classifiers remain robust over time. Interpretation of trained classifiers reveals that poorer health can be predicted mainly by the night-time activity levels in the sheep. Our study reveals behavioural patterns across two small ruminant species, which low-cost biologgers can exploit to detect subtle changes in animal health and enable timely and targeted intervention. This has real potential to improve economic outcomes and animal welfare as well as limit the use of anthelmintic drugs and diminish pressures on anthelmintic resistance in both commercial and resource-poor communal farming. The validation of the proposed techniques with a different study group will be discussed in the latter part of the thesis. We used the accelerometry data of indoor cats equipped with wearable accelerometers in conjunction with their health status to detect signs of degenerative joint disease, and adapted our machine-learning pipeline to analyse bursts of high activity in the cats. We were able to classify high-activity events with precision up to 70% despite the relatively small dataset adding further evidence to the viability of animal health monitoring with accelerometers

Innovation Meets Tradition in the Sheep and Goat Dairy Industry

The domestic sheep (Ovis aries) and goat (Capra aegagrus hircus) are small ruminant species widely distributed throughout the world. They were among the first animals to be domesticated. Owing to their small stature and versatility, sheep and goats still are one of the most important food source in many arid regions. Traditionally, autochthonous breeds with a strong milk production seasonality were reared in extensive production systems, on a smallholder farming basis. The huge number and variety of their dairy products reflect the different cultures and traditions of vast areas of the world. However, today the traditional ovine and caprine dairy production chain, from farmers to exporters, is facing the challenges of innovation, sustainability, safety, and productivity, while at the same time protecting each product’s individual characteristics. This Special Issue is dedicated to the field of ovine and caprine dairy production with ground-breaking perspectives and approaches, from physical-chemistry studies on milk and dairy, to new feeding strategies, herd management, nutritional quality, animal welfare, sustainability, and omics studies

Milk Protein

Milk Protein - New Research Approaches discusses the biology and synthesis of milk protein at both the cellular and molecular levels. It also presents related information on animal nutrition and management, including animal breeding. It is a useful resource for students, researchers, and professionals in veterinary, dairy, food, and animal science, among others

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas

Impact of Pre-Mortem Factors on Meat Quality

Meat quality is associated with the chemical composition and metabolic state of skeletal muscle. This Special Issue aims to compile the recent literature with a focus on meat quality and pre-mortem factors that affect muscle metabolism. It includes nine research articles about various types of meat, as well as one review article about beef quality

Mechatronics applications and prototyping sensors for the precision livestock farming

The study is subdivided into 5 chapters and comprises a review of the main components of Plf, the development of a prototype for EC monitoring in ewe milk, a prototype for monitoring animals body temperature, the optimization of collection rounds of goat milk and the development of a prototype for somatic cell count (SCC) through the measurement of Sodium ions in ewe milk.‬‬‬ The first chapter is a review of the advancements of the main components of Plf, i.e. software, hardware and data transmission, focusing on issues related to hardware modularity and differences between licensed and unlicensed software. From the review it emerges that image processing is one of the most used techniques in Plf systems, in that it allows the detection of behavioral, biological and pathological parameters without interfering with the animals routine activities. In this regard the area occupied by a lamb carcass was calculated by using an image analysis open source software, CellProfiler (Jones et al., 2008). The second chapter deals with the realization of an innovative portable tool for somatic cells count in ewe milk by measuring its electrical conductivity. There are over 15,000 dairy sheep farms in Sardinia, which represent both historically and economically the most important agricultural and livestock sector in the island. Indeed, Sardinia holds more than 40% of the national sheep population thanks to more than 3 million sheep heads that provide about 60% of the total national milk production. One of the most common problems in sheep farms is mastitis, an intramammary infection which may cause a quantitative reduction up to 50% in milk production and a qualitative drop, in particular of lactose and casein. One of the indirect methods for the assessment of somatic cell count (SCC) in ruminants’ milk is through the measurement of its electrical conductivity (EC). In small ruminants, EC has a reasonable correlation R2 = 0.35 with somatic cells but to date there is still not a portable tool that can estimate SCC based on the milk’s EC reading. The prototype was calibrated on Sarda ewe milk. The aim of Chapter 3 was to develop a system using a open source sensors, actuators and micro-controller. The system is able to monitoring the rectal temperature of the animals, sending data via Bluetooth to a smart phone. The micro-controller used was an ATmega32U4, the temperature was read using the LM35 analogic sensor and a Class 1 Bluetooth serial module was connected to Arduino creating a wireless serial link between an Android phone and the Arduino board. The application for receiving data on an android smart phone was created using App Inventor that is an innovative Android application creation software developed by Massachusetts Institute of Technology (MIT). This app is free available on Google Play Store under the name animal_temp. The costs of sheep milk collection rounds in Sardinia have been analysed in chapter fourth. The escalating costs incurred by the dairy processing industries for milk collection from individual farms have focused the attention on the rationalization of milk collection and transport systems. In this regard, the case of the Sardinian goat sector has characteristics that make it unique and not comparable to other logistics optimization realities. The problems of this sector are mainly represented by the particular conditions of the rural road network and the fragmented nature of livestock farms. The aim of the present study was to test a milk collection route optimization software, MilkTour, in the collection rounds of a sample cheese dairy. The software has been developed by the Land Engineering Section of the Agriculture Department of the University of Sassari. A total of 5 routes were analysed and optimized. The results have highlighted the importance of optimizing collection routes as they have a significant impact on business costs. A important contribution that has emerged is the strong correlation between collection density and the cost per litre of collected milk (€cent/l), which allows to detects the cost-effectiveness of a round of collection and its relative optimized around. The objective of chapter 5 was to study the relationship between the ione Na+ and the main components of sheep milk, in particular somatic cells. Moreover, a portable device for estimating SCC in sheep milk was designed. The study was conducted on over 2000 samples. The milk components examined were: fat, proteins, lactose, pH, sodium chloride, urea and the ions Na+. The correlation between Na + and SCC corresponded to R2 = 0.76 (P <0.01). The prototype developed incorporates two containers which receives milk samples taken from each half udder. Each container has integrated inside two sensors, one to detect the level of Na+ in the milk and the other one to compensate the milk temperature. The mathematical model, loaded into the microcontroller by a firmware written in C / C ++, analyze the data and gives back the estimate of SCC level, so it allows farmers to monitor the ewes health status by periodically comparing the somatic cell counts of each half udder. While dealing with different topics the 5 chapters can be enclose in a big new topic, called Precision Livestock Farming (Plf). Plf is the discipline that allows to monitor in real-time the numerous biological and environmental parameters concerning each individual animal of the herd. A Plf system is always made up by three components: a physical element, i.e. the hardware; an element for data processing and presentation, known as the software; and an element for the transmission of data, i.e. the network. The hardware comprises the sensors, the computers and/or microcontrollers, the data transmission and acquisition systems and the actuators. Mathematical models for data processing and the data presentation interface are included in the software loaded into the microcontroller