Ontology-Based IoT Middleware Approach for Smart Livestock Farming toward Agriculture 4.0: A Case Study for Controlling Thermal Environment in a Pig Facility
- Productivity increase. As reported in projections of the United Nations (UN) Population Division, the world’s population is expected to increase from 7.7 billion in 2019 to 8.5 billion by 2030 and up to 9.7 billion by 2050, reaching 10.9 billion by 2100 (42% increase) . Accordingly, agricultural productivity has to be increased by 60% during the 21st century so as to address the challenge of ensuring adequate food supply not only at present, but in the long term as well .
- Reasonable allocation of resources. Industrialization, urbanization, as well as the increase in demand resulting from higher living standards are globally exerting escalating pressures on natural resources. As a matter of fact, 25% of the farmlands and pastures are characterized by severe degradation due to deforestation, vegetation overcutting, and insufficient fallow periods . Additionally, water resources are being excessively consumed compared to water availability while transbasin diversions usually result in grave environmental issues . Last but not least, improper work distribution of agricultural machinery and infrastructure is resulting in excessive energy consumption .
- Climate change mitigation. Climate change has massive ramifications on ecosystems and biodiversity including frequent occurrences of droughts, floods, and extreme weather conditions . Moreover, the atmospheric temperature is expected to rise by 2 °C until 2100 due to the greenhouse effect aggravated by manmade greenhouse gas (GHG) emissions . On top of this, the agricultural sector in particular was responsible for 11% of global manmade GHG emissions in 2013 . Since farming production is exceptionally vulnerable and exposed to the impacts of climate change , this could present a considerable threat to global food safety.
- Food loss elimination. Food loss refers to all edible products that are wasted at any point along the supply chain, including not only products that remain unconsumed in stores and households or those that are blemished during transport , but also commodities that are damaged at the early stages of production. Concerning farming production in particular, food loss may occur as a result of agrochemical misuse, lack of pest management in farmlands and pastures, inappropriate disease control of crops and livestock [26,27], etc. Eliminating food loss is a critical issue from an environmental point of view, considering that processing and recycling of wasted food consumes even more resources than producing new commodities .
2. Smart Livestock Farming: State of the Art and Open Issues
3. Materials and Methods
3.1. IFM System Operational and Architectural Overview
3.1.1. Perception Layer
3.1.2. Middleware Layer
- Monitoring/control of (a) the animals’ physical and biological parameters such as heart rate, temperature, behavior as well as location; and (b) ambient conditions such as environmental temperature and humidity, gas emissions, etc.
- Forecast/control of imminent animal diseases such as tetanus, parturient paresis (milk fever), listeriosis, etc.
- Surveillance and security control of the animals and the livestock facilities in general.
3.1.3. Application Layer
3.2. IoT Middleware Design Analysis
3.2.1. Context Detection
3.2.2. Context Processing
- Context Retrieval Engine
- Context Inference Engine
- Service Mapping Engine
- Service Discovery Engine
3.2.4. Context Repository
3.3. IoT Middleware Context Modeling Structure
3.3.1. Thing Ontology
3.3.2. Location Ontology
3.3.3. Time Ontology
3.3.4. Event Ontology
3.3.5. Rule Ontology
3.3.6. Properties of Ontology Entities
4. Case Study: Smart Control of Thermal Environment in Pig Farming Facility
4.1. Context Modeling and Reasoning
- Thing (Device Subclass): It involves all types of devices. In particular: (a) environmental sensors are incorporated for monitoring ambient temperature and humidity, (b) RFID tags are used to monitor the pigs’ physiology features of interest (body temperature and heart rate) as well as to identify their exact ID and position, (c) actuators are employed for triggering the proper equipment (air-conditioning system) of the facility, and (d) smart devices such as smartphones and tablets are used for establishing the users interaction to the system.
- Thing (Animal Subclass): It involves the animal species of interest, in this case pigs.
- Thing (User Subclass): It involves two types of users, that is farmers and veterinarians, as notification recipients for undertaking further proactive actions related to the wellbeing of the pigs housed in the facility.
- Location (Indoor Space Subclass): It involves the building layout (area) as well as the points of interest such as the quarters, corridors, and pens that are involved in the process. It also involves a profile including the environmental requirements of the location of interest.
- Time: It involves various daily timeframes that may affect the ambient conditions. For instance, temperature tends to be higher during the noon hours (maximum effect). Furthermore, time refers to the occurrence of events that are triggered by internal or external rules.
- Event: It involves the entire IoT activity, such as the monitoring of parameters and the control of the corresponding devices as well as the user interactions to the system.
- Rule: It involves the rules applied during the entire IoT activity. The rules may be applied according to the reasoning process or be dictated into the middleware by the users.
4.2. Performance Evaluation and Results
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|||Identifying the ecosystem, architectures, and technologies of the IoT as well as the present status, opportunities, and expected future trends regarding its role in livestock management.|
|||Overviewing and interpreting the requirements for IoT sensors that are applicable for smart livestock monitoring.|
|||Designing, implementing, and evaluating the performance of an IoT hardware and software architecture that adopts the LoRa low-power wide-area network (LPWAN) technology for continuously monitoring livestock located in barns during grazing.|
|||Introducing the design of an integrated framework of smart livestock farming IoT system by means of RESTful (REST: representational state transfer) web services.|
|||Presenting the design of a geographical paddock to monitor spatiotemporal behaviors of livestock using the IoT and GPRS technologies.|
|||Establishing a smart piggery wastewater treatment system, which was upgraded from a self-developed fully automatic wastewater treatment system by using IoT applications.|
|||Introducing a platform which combines IoT, edge computing, artificial intelligence, and blockchain technologies based on the Global Edge Computing Architecture to monitor the state of dairy cattle and feed grain, as well as to ensure the traceability and sustainability of the different processes involved in the production.|
|||Presenting various aspects of smart dairy farming (SDF), as well as the state-of-the-art framework that can assist farmers in increasing their milk yields by using IoT and data-driven technologies.|
|||Introducing a scalable cloud-based architecture for a smart livestock monitoring system following Agile methodology and featuring environmental monitoring, health, growth, behavior, reproduction, emotional state, and stress levels of animals.|
|Entity Property||Domain Class/Subclass||Type of Context|
|UserID||Thing/User/subclass||String to identify the user ID|
|Name||Thing/User/subclass||Short text to name the user|
|Thing/User/subclass||String to identify email address|
|AnimalID||Thing/Animal/subclass||String to identify the animal ID|
|Breed||Thing/Animal/subclass||Short text to name the breed|
|Birthdate||Thing/Animal/subclass||String to identify date of birth|
|DeviceID||Thing/Device/subclass||String to identify the device ID|
|Node||Thing/Device/subclass||String to identify the node ID|
|Network||Thing/Device/subclass||String to identify the connection type/IP|
|LocName||Location/Outdoor Space/subclass||Short text name|
|LocDescription||Location/Outdoor Space/subclass||Short text description of the location|
|AreaDimensions||Location/Outdoor Space and Indoor Space||Number to measure the area in m2, km2|
|Coordinates||Location/Outdoor Space/subclass||Latitude and longitude to identify absolute position|
|PositionID||Location/Indoor Space/subclass||String to identify the relative position (Building, Quarter, Corridor, Pen)|
|Timestamp||Time/Instant and Interval||Exact timestamp|
|Pig Behavior||Assistive Actions|
|System||Simulated Model||Development Platform||Output|
|Sensory Devices||Python servlet||Python script in SQL server||Data files|
|Context Providers||Python script running in Azure||API using Azure||High level context|
|Context Aggregator||Python script running in Azure||API using Azure||Context model in XML|
|Context Modeling||OWL||Azure data source||Context model|
|Service Mapping||Python script running in Azure||API using Azure||List of actions in XML|
|Context Repository||Static database||Azure data source||Database records|
|Service Providers||Static XML in various web servers||Azure web server||Service rules with actions|
|Inference Cycle Time (ms)||Response Time |
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Symeonaki, E.; Arvanitis, K.G.; Piromalis, D.; Tseles, D.; Balafoutis, A.T. Ontology-Based IoT Middleware Approach for Smart Livestock Farming toward Agriculture 4.0: A Case Study for Controlling Thermal Environment in a Pig Facility. Agronomy 2022, 12, 750. https://doi.org/10.3390/agronomy12030750
Symeonaki E, Arvanitis KG, Piromalis D, Tseles D, Balafoutis AT. Ontology-Based IoT Middleware Approach for Smart Livestock Farming toward Agriculture 4.0: A Case Study for Controlling Thermal Environment in a Pig Facility. Agronomy. 2022; 12(3):750. https://doi.org/10.3390/agronomy12030750Chicago/Turabian Style
Symeonaki, Eleni, Konstantinos G. Arvanitis, Dimitrios Piromalis, Dimitrios Tseles, and Athanasios T. Balafoutis. 2022. "Ontology-Based IoT Middleware Approach for Smart Livestock Farming toward Agriculture 4.0: A Case Study for Controlling Thermal Environment in a Pig Facility" Agronomy 12, no. 3: 750. https://doi.org/10.3390/agronomy12030750