PRE2017 3 Groep5

Group members

Bogdans Afonins, 0969985

Andrei Pintilie, 0980402

Stijn Slot, 0964882

Andrei Agaronian, 1017525

Veselin Manev, 0939171

Project definition

Subject

The world population continues to grow and is expected to reach 9.7 billion by 2050 and 11.2 billion by 2010. The increase in population, combined with the development of poorer nations, is projected to double to global food intake. There will be a greater demand for commodities such as eggs, meat, milk, leather and wool. The supply can be increased by either increasing the number of livestock or its productivity. Farmers are forced to keep hundreds of animals, which makes the manual tracking of the individual animals not practical. Domestic animals can get sick, lost or get stolen and sometimes it is too difficult to detect this when the animal is along with hundred others. Current advances in technology can be adopted for use in the farming sector and by doing so all the previously described problems can be easily solved. This will ensure the animals welfare, increase productivity and ease the work of farmers around the world.

Objectives

The objective is to design and possibly implement a solution for the automation of the farming sector. More precisely, realize an animal tracking model, which is capable of first distinguishing the individual animals and after that determining their health status, based on their behavior.

Users

The technology we focus on is about improving the process of monitoring and tracking domestic animals. Hence the main users are people involved in the process of delivering commodities to the market and animals themselves. These are:

• Farmers
• Feedlot operators
• Slaughterhouse workers
• Veterinars
• Domestic animals

We also consider people that can possibly be involved in maintaining the technology described here.

• Maintenance engineers

User requirements

Slaughterhouse workers must be able to

• verify that a particular domestic animal or a product is market ready
• be informed if a product or an animal is infected or most probably will be infected in the near future

Feedlot operators should be

• informed if a particular animal deviates from standard behavior
• informed if a particular animal or its product is market ready
• able to examine animals health state without manually examining the animal
• informed if a particular animal is underfed/overfed

Veterinars should be able to

• have information about the animals health state, such as
  • animals temperature for some specific period of time (to be determined later)
  • ambient temperate at the farm
  • latest location that the animal visited (this can help narrowing the scope of possible diseases)
  • information about previous health issues
  • feeding statistics

Farmers should be able to

• examine statistics of the livestock, such as
  • ill animals
  • market ready animals
  • animals that do not meet quality/yield standards

Maintenance engineers should be able to

• adjust the technology for different number of animals at a farm/cowshed/etc.
• adjust the technology for varying indoor capabilities and constraints.

Domestic animals should

• not be harmed by the technology
• not be distracted by the technology

Identification technology

A set of devices we decided to use in order to automate cow tracking and health monitoring contains only regular surveillance cameras. The number of cameras depends on the size of a barn. We require that the whole indoor area is covered such that every animal can tracked at any given time. In the previous sections we discussed several promising technologies that could be or are already used for animal tracking purposes, for example, thermal cameras, RFID, GPS and other. Despite that, we narrow the scope of involved devices only to cameras. The reason behind this is simplicity in video surveillance - cameras need to be placed only once in specific areas and do not require regular maintenance. Also the animals will not feel the presence of the cameras, as it is the case for the RFID tags, where during maintenance for example the animal will be disturbed.

Background

Animal sickness

Diseases can come in many forms in animals. Currently, it is estimated that the economic impact of animal diseases is around a few billion dollars. The most affected species are cattle and swine. Therefore, there is a large incentive to try and find and cure these diseases as early as possible. Luckily, there are various clues and methods for detecting when an animal is sick.

A physical exam is most often needed for finding and treating sick animals. The role of this physical exam is to identify, treat, prevent the spreading of diseases, protect food supply and improve animal welfare. Once a cow is identified as sick (some of the measured vitals are abnormal: heart rate, respiration rate, temperature, rumen contractions), the problem should be determined. Several potential disorders are presented depending of the group of animals. The subjects are mostly “fresh” cows which are newly achieved animals. Their stress level is of huge importance to determine sick animals, especially when a new animal has to adapt to the environmental changes. When multiple types of stress are gathered together, that’s a breaking point and the immune system starts falling apart. This immune suppression makes them not react to vaccines and more likely to get infected by diseases. There are 4 areas to determine diseases in new cows: temperature, appetite, uterine discharge, hydration status. In order to determine if a cow is sick watch the attitude(lie down in corners, less energy, look depressed), the appetite(aggressive eating, not eating), check the hydration(by checking the skin). Even normal cows must be monitored.

In a paper by Weary et al, behavioral clues are used for identifying illness in animals. The scope of this paper is to teach animal caregivers how to predict and identify sick animals by using their behavior. There are several examples of behavior related to sickness: hydrophobia – rabies, head throwing seizures – sodium salt toxicity in pigs, star-gazing – polio encephalomalacia in cattle, abnormal feeding and drinking – malaise and others. The paper is concerned about what valid behavioral indicators can be used to determine sick animals. Typically it is done by clinical evaluations, controlled exposure to a pathogen, or by induction models. Recently behavioral indicators are also taken into account. A first category behavior aims to is health. There are changes in the behavior in response to: pain(physical injuries can be spotted), a general feeling of Malaise(decrease in feeding, reproductive activities, increasing rest time to conserve energy) or other diseases. It is essential to understand which behaviors respond and why. Another category in discussion is Behavior as a communication system, where behavior is used as a signal of vigor or of need. Lastly a category of behavior as predictive of illness is discussed.

In a paper by Frost and Hamm, the objective is to improve animal wealth worldwide while posing transparency on global animal disease, collecting veterinary scientific information, improving a legal framework and resources of national Veterinary Services. The group involved into this prevention program are World Trade Organization(WTO), United Nations and others(around 40). Animal health is seen as a global public good, since the eradication of a disease will have international benefits, while the failing of a country may endanger the whole planet. So, the impact of animal diseases is huge for both economically and socially. There are also diseases that transfer from animals to persons, and since the world is interconnected, everybody can get any disease from any point of the planet(international transfer of meat). The paper continues to study what is needed to control the animal disease. The key elements are biosecurity, surveillance, diagnosis and awareness, early detection and rapid response, fast treatment. An agreement OIE(Organization for Animal Health) set some standards and released a list of diseases in order to keep everybody informed about them. Countries can report new diseases to OIE, so the interoperation of countries is a must. OIE also has a tool called Evaluation of Performance of VS(PVS) which asses the level of compliance with OIE standards and aim for voluntary process of countries. Although, this tool produces a gap between quality and quantitative information, gap which must be solved.

Thermography

Thermography can be also be used for detecting when an animal is sick. Veterinarian and researcher Mari Vainionpää considers thermal imaging cameras to be a great tool to find out whether an animal is in pain, for instance. It is stated that if there are changes in the organic activity there are also changes in the amounts of heat that are emitted and these deviations in the heat pattern can be detected with a thermal imaging camera. Moreover, in Vainionpää’s experience a thermal imaging cameras can be used to reveal inflammations, bruises, tendon or muscle related injuries, superficial tumors, nerve damage, blood circulation issues It is pointed out that thermal imaging is quick and reliable as there is no need to sedate the animal, no need to touch it and using a thermal imaging camera doesn’t expose the animal to potentially harmful radiation. Moreover, it is proposed that the animal paw and teeth can be thermally viewed to gain additional data for more precise results.

In a paper by Church et al, It is pointed out that eye temperature, measured using infrared thermography (IRT), is a non-invasive tool for evaluating the stress response in cattle, bison and elk to surgical procedures. Moreover, infrared thermography has recently revealed that a rapid drop in eye temperature is likely a sympathetically mediated response via vasoconstriction that can be used to detect fear and/or pain related responses in animals to different handling procedures. Infrared thermography may be used in the future as an objective outcome based measure for the evaluation and assessment of animal welfare. It is mentioned that recent data has demonstrated that IRT scans of the orbital area in calves allows for the efficacious identification of animals at earlier stages of illness, often several days to over one week before clinical signs were manifest. What is in the most interest is that it compare IRT to other indicators and points out that the first has more advantages as long as it minimizes the confounding factors that are often associated with other techniques. In the conclusion, it is again pointed out that eye temperature measures using IRT are especially promising, and are fast becoming an essential component in the development of a complementary index used to measure pain and stress in animals, and could eventually replace invasive procedures, such as the measurement of plasma catecholamines to measure autonomic nervous system responses for assessing animal welfare. Moreover, as it is stated, IRT will have wider applications such as testing the efficacy of different analgesics and measuring animal emotions such as fear in the very near future.

Animal detection

For farmers it can be very useful to track animal locations and detect when an animal does a certain action. At the moment, ear tags are mostly used for identification of animals, but with modern technology animals can be more closely tracked. For example, identifying and tracking animals can show an animals history and detecting animals at the food station can give insights into its food intake.

Greene describes in a paper the effort of U.S. Department of Agriculture’s (USDA’s) to trace farm animals by identifying them and rapidly reacting to diseases as soon as they occur. The identification is done by using a national animal ID, which will allow the users to track the previous ownership, to prevent theft and check all detail about an animal. The U.S. National Identification System(NAIS) has the role to set standards and rules for animals’ health, living conditions, and trade of animals. NAIS and USDA work together to establish a new approach to animal disease traceability. The main objectives of this project are to identify animals, to track their food habits and their origins and owners. As advantages can be remembered the disease eradication, minimize economic impact, increase marketing opportunities, provide a tool for producers, address national safety regulations. But there are also some disadvantages like: invasion of privacy(collection of personal data), increase costs for the farmers(to install the systems), and market domination by large retailers.

RFID

Radio Frequency Identification (RFID) tags can store and transmit data through electromagnetic transmission. RFID readers can be used to detect RFID tags within certain ranges. Combination of RFID tags and readers can be used for detecting moving object such as animals.

In a paper by Seol et al, RFID tags are used for tracking large number of moving objects. The idea is as follows: each entity that is supposed to be tracked must be equipped with a basic RFID tag, that can receive queries and respond to so called readers. The readers are static and are supposed to be positioned all around the area. Every reader has a certain range it can operate in. So it can communicate/detect entities only within that range, for example 5 or 10 meters. The readers will pass the presence information of a certain tag to the central server that stores this information appropriately. The central server is responsible for gathering data and operating on it, for example by approximating a path that a tag took, keeping track of the number times that a certain tag appeared in a certain location.

In a US patent by Huisma, RFID tags are used for detecting animal visits to feeding stations. Animals are equiped with a RFID tag, that can be read in close proximity to the feeding stations. These detected visited are used together with weighing devices in the feeding troughs to measure the difference in weight before and after a consumption event, using a mathematically weighted filter technique. The reduction in food is divided between the RFID tag last seen and the next one. By detecting animals at the food stations with RFID tags, the food intake of each animal can be recorded. This information can be used for finding animals with abnormal feeding behavior. The patent mentions the obstacles with this method, namely inaccurate RFID readings (few seconds delay, readings by other stations) and inaccurate food reduction measurements (wind, rodents, inaccurate division).

Camera detection

Cameras and Computer Vision can be used to detect and monitor animals. Different cameras technologies, such as RGB, Infrared (IR) and Time-of-Flight (TOF) can all be used for animal identification, location tracking and status monitoring.

In a paper by Zhu et al, an 3D machine vision of livestock is described. In previous papers Internet Protocol(IP) cameras have been implemented to track weight of animals and to ensure they do not get unhealthy. IP cameras capture RGB images, which makes them dependent on room light, shadows and contacts between animals. In order to tackle those problems, the method used in the paper adds an IR camera to the RGB image. This gives information about the depth of every pixel thus giving a true 3D data for more accurate detection of the animals. The authors of the paper use Microsoft Kinect, which has RGB camera, IR projector and IR camera. By setting different thresholds and creating a software, it is possible to estimate the weight of a pig very accurately and find ones which are over- or under-weighted.

In a paper by Salau et al, a TOF camera is used for determining body traits of cows. They first introduce the manual method of body trait determination which relies two measures that is used to describe a cow’s body condition - Body Condition Score, which is gathered by visually and manually judging the fat layer upon specific bone structures and how sunken the animal’s rear area is, and the BackFat Thickness. However, this manual system for body trait determination has higher costs, more stressful for the animals, doesn’t avoid errors during manual data transcription, and can not provide large volumes of data for use in genetic evaluation, rather than an automated method would do. Then the paper fully focuses on introducing and further explaining of the automated system which relies on collecting data from the camera - Time Of Flight. Technical aspects of TOF method, which is based on using camera mounted into a cow barn, and its implementation with testing and numbers are present in this paper. It is clearly indicated that this automated system was able to carry out the tasks camera setup, calibration, animal identification, image acquisition, sorting, segmentation and the determination of the region of interest as well as the extraction of body traits automatically. At the end, it is summed up that the application of TOF in determination of body traits is promising, since traits could be gathered at comparable precision as BFT. However, animal effect is very large and thus further analyses to specify the cows’ properties leading to the differences in image quality, reliability in measurement and trait values need to be carried out.

A paper by Kumar et al focuses on tracking pet animals that are lost. They mention the fact that there is an increase in the number of pet animals that are abandoned, lost, swapped, etc., and that the current methods to identify and distinguish them are manual and not effective. For example, ear-tagging, ear-tipping or notching and embedding of microchips in the body of pet animals for their recognition purpose. The authors point out that these methods are not robust and do not help to solve the problem of identification of an animal. The idea is to use animal biometric characteristics to recognize an individual animal. To recognize and monitor pet animals (dogs) an automatic recognition system is proposed in the paper. Facial images are used for the recognition part and surveillance cameras are used for the tracking purposes. Results of the research are quite impressing, but it has yet to be tested in a real life environment.

Similarly, a paper by Yu et al describes an automated method of identifying wildlife species using pictures captured by remote camera traps. Researchers not only described the technical aspects of the method, but also tested the method on a dataset with over 7,000 camera trap images of 18 species from two different field sites. After all, they’ve achieved an average classification accuracy of 82%. Summing up, it was shown that object recognition techniques from computer vision science can be effectively used to recognize and identify wild mammals on sequences of photographs taken by camera traps in nature, which are notorious for high levels of noise and clutter. In the future work, the authors say, some biometric features that are important for species analysis will be included in the local features, such as color, spots, and size of the body, which are partly responsible for determining body traits.

Animal monitoring

Monitoring animals can be of great importance for farmers and the animals themselves. First of all, it is a common situation when farmers/feedlot operators miss the time when a certain animal is market ready. This increases the costs of running a farm. Also, detecting whether an animal is sick requires presence of a feedlot operator. This causes more stress for animals. What is more, even when an animal is detected to be sick if will be treated with a range of different antibiotics regardless if the animal needs treatment for all of these illnesses.

GPS

A method is proposed by Guichon et al, for gathering reference movement patterns of the animals, for example the ones that resemble behavior of a sick/overfed/underfed/market ready/etc. and then compare with the movement patterns of the current live stock. These patterns can be used as well to identify the exact illness an animal has. This is because the system can track all places that the animal visited and therefore can narrow the scope of possible illnesses. Animals are tracked with a GPS tracker that every 15 seconds polls the GPS satellites and collect raw GPS data representing the associated animals position. Then the data is loaded to server that stores it and runs several smoothing/filtering programs to “clean” the raw GPS data. Since the data is processed continuously, the SQL database with coordinates and timestamps gets updated. Afterwards, the collected data is compared with reference patterns to count, for example how many times a particular animal though different zones in a cowshed like to the water zone/feedlot/activity zone/etc.

Similarly, in a paper by Panckhurst et al, an ear tag for livestock is developed, which is lightweight (less than 40g) and solar powered. It has a GPS module, which is used for determining the location of the animals and this is then send to a base station, where the data is collected. In the paper a technical description about the ear tag and the base station are given

Sensors

To monitor an animal and possibly detect sickness, sensory data can be used. By placing sensors on/in an animal that measure information such as blood pressure, temperature, or activity, it is possible to gain knowledge on an animal's conditions.

In a paper by Umega and Raja, different sensors are placed on the animal to monitor its wellbeing. Those sensors include heart rate sensor, Accelerometer, humidity sensor and others. RFID tags are used to identify the different animals, and the data is transmitted by wifi. The authors of the paper realise the design using Arduino Uno Microcontroller. The data from the sensors is extracted by the controller and can be displayed for the end user.

Similarly, a paper by Zhou and Dick proposes an intelligent system for livestock disease surveillance. The main purpose is to detect if animals are sick/or about to be sick and treat it to avoid the diseases spreading among other animals and infect the whole livestock. In this method special sensors are used that are attached to every animal collecting both - animals (cows) temperature and ambient temperature every 5 minutes. Each sensor is uniquely identifiable. Then, the collected data is sent via network to a main server, where it is processed. The processed temperature readings can then be used to detect diseases.

In a paper by Maina, the use of IoT and Big Data is explored for determining an animals health status and increase productivity. Interconnected sensors can be used to sample and collect the vital signs of the animals and based on that determine their health status. They have developed a prototype sensor system which was attached to the necks of cows. This sensor can distinguish between several different activities as eating grass, standing and walking. This paper however does not explore the actual connection of many sensors in a single network.

Lastly, in a US patent by Yarden, a system of basic tags, smart tags, a mobile unit and a PC unit for monitoring cattle and detecting illness. The basic tag is put on the ear of an animal, and collects data from sensors on various locations on/in the cattle’s body. The smart tag is similar to the basic tag, but also collects information from various basic tags in proximity. Either the raw sensory data is send, or the basic tag will process this data and only send a health indicator. The smart tag periodically sends the information to the mobile unit, which relays it to the PC unit, where it is displayed to the end user and stored. When illness is detected, the basic tags can display alarm with a LED and buzzer, and a message is send to the PC unit.

Monitoring devices

Similarly to using various sensors, animals can also be monitored by attaching a single monitoring device to them that gathers sensory data such as location and vitals. This information can then be used for tracking the animals and determining their health status. These devices can be used to detect sickness in animals in an early stage.

A US patent by Ridenour describes a monitoring device that is placed on an animal’s appendage, that contains biosensors that can measure vitals. Additionally, it can be expended with a GPS tracker or a tag for monitoring visits to food and water stations. These measurements are used to determine when an animal is sick, by detecting when conditions fall outside normal ranges. When sickness is detected, it will send out an alarm. A transmitter can also be included, to send physiological information to a base station where all animals are monitored. Advantages over this device compared to other monitoring devices is that the system is non-intrusive, easy to use and can be adapted to a users need.

Similarly, a US patent by Ingley and Risco outlines a device for monitoring activity, ketone emission and vitals such as body temperature, nose wetness and humidity of cows. The device will be placed as either a nose ring or an ear tag. The device will take measurements and log this for at least 8 hours. When entering a milking facility, the data can be downloaded and used as an indicator of the cow’s health.

In a paper by Lopes and Carvalho, a low power monitoring device is attached to livestock for preventing theft. There will be a lower power module on the animal with a wake-up radio. A drone will be used to wake up the animal's module and collect the gathered information. Furthermore, the module should also be highly resistant to water, impacts and dust. With such a system, each animal can be identified with the drone and theft can be caught early.

Project

Approach

In order to complete the project within deadlines, we plan to start reading and summarizing the papers in the first week, being followed by establishing the USE aspect plus the impact of the technology on the animals. In the fifth week already, we plan to start working on the prototype which will be a simulation in Java that will show a location(has to be determined) and few types of animals(chosen according to the sick behavior study). Then, by allowing the user to select an animal, that animal becomes sick, the simulation detects that and finally reports it to the user. During last weeks we also have to specify why is it robotics and what advantages or disadvantages are present.

Planning

Milestones

Milestones are shown in the planning picture.

• In the first week, we already plan to have summarized the papers and use them to identify patterns in sick animals behavior and what technologies can be used to detect these changes.
• In the 3rd week, the milestone is to deliver a full analysis of the USE aspects.
• During the following two weeks, we expect the simulation to work.
• In the last week, a milestone will be preparing and holding the final presentation.
• The last milestone is to prepare all deliverables for handing in.

Deliverables

As deliverables we decided to prepare the following:

• A presentation, which will be held in the last week. During this presentation, all of our work will be presented and a simulation will run.

• A simulation of the subject treated. For example, a barn that has a lot of animals(about 200-300) of different species(cows, pigs, chickens). The user will be allowed to place the identification technologies around the barn and "make" some of the animals sick. The simulation shall be able to discover which animal is sick by analyzing its behavior. The user is notified which animal might be sick.

• Research paper of the technology described above, which will take into account the advantages, disadvantages, costs, and impact of such an implementation.

Coaching Questions

Coaching Questions Group 5