Survey of Intelligent Agricultural IoT Based on 5G
Abstract
:1. Introduction
1.1. Smart Agriculture
1.2. 5G-IoT Smart Agriculture
1.3. Summary
1.3.1. Contribution
1.3.2. Organization
2. Integration and Application of 5G and Smart Agricultural Internet of Things
2.1. 5G Characteristics
2.2. Typical Applications of Intelligent Agricultural Iot Based on 5G
2.2.1. Enhance Mobile Broadband Applications (eMBB) in Smart Agricultural IoT
2.2.2. Large-Scale Machine Type Communication (mMTC) in Smart Agricultural IoT
2.2.3. Ultra-Reliable Low Latency Communication (uRLLC) in Smart Agricultural IoT
2.3. Future Trends and Key Technologies of 5G-IoT Application in Smart Agriculture
- Cloud edge collaboration: agricultural monitoring terminal and cloud collaboration.
- Cloud computing/AI/big data/Internet of Things/digital twin and other agricultural integration.
- Virtualization and servitization of perception/access/communication layer, software defined network.
- Model-driven, cloud-native new application (APP) development environment for smart Internet of Things.
- The deep integration of people, information space, and physical space will form a deep intelligent smart agriculture and achieve a harmonious ecology of human–machine symbiosis.
3. Evolution of Intelligent Agricultural IoT 2.0 for 5G
3.1. 5G Smart Agricultural IoT 2.0 Architecture
3.1.1. Personalized Service Network Slice in 5G Intelligent Agricultural Internet of Things
3.1.2. The 5G Intelligent Agricultural Internet of Things (IoT) System Is Integrated with “Cloud, Network, Edge, and End”
3.2. Perception of Deep Fusion of 5G-Intelligent Agricultural Internet of Things
3.2.1. Intelligent Agricultural IoT Sensing Device Based on 5G
3.2.2. 5G Intelligent Agricultural IoT Operating System
3.2.3. Large-Scale Terminal Access of 5G Smart Agricultural Monitoring Terminals
3.3. Reliable Data-Driven Detection of 5G Smart Agricultural IoT
3.3.1. Complex Event Detection in Agricultural Production
3.3.2. Depth Detection of Pests and Diseases Based on Machine Learning
3.4. Cloud Edge Fog Computing Fusion in 5G Intelligent Agricultural Internet of Things
3.5. 5G Intelligent Agricultural IoT In-Depth Service
3.6. 5G Intelligent Agricultural IoT Production Intelligent Control
4. Revolution of Smart Agricultural IoT Application Paradigm under 5G
4.1. Typical Application Scenarios of 5G Smart Agricultural IoT
4.1.1. Smart Farm
4.1.2. Smart Forestry
4.1.3. Intelligent Animal Husbandry
4.1.4. Smart Fishing Ground
4.2. Deep Sense of 5G Smart Agriculture
4.2.1. Agricultural 5G Image Processing
4.2.2. Agricultural Intelligence Detection Based Machine Learning
4.3. 5G Intelligent Agricultural Machinery
4.3.1. Intelligent Agricultural Machinery
4.3.2. Automatic Driving of Agricultural Machinery
4.3.3. 5G Automatic Coordination of Multiple Agricultural Machines
4.4. 5G Agricultural UAV
4.5. 5G Intelligent Agricultural Supply Chain Management
5. Challenges of 5G Smart Agricultural IoT
5.1. Fusion and Optimization of Sparse 5G Base Station and Heterogeneous Sensing Network in Smart Agriculture
5.1.1. Optimization of Hybrid Deployment of 5G and Sensing Network
5.1.2. Optimization of 5G and Sensing Network Gateway Deployment
5.2. Optimization Control under Edge Computing in 5G Intelligent Agricultural Production
5.2.1. Automatic Phenotype Monitoring Based on 5G Internet of Things
5.2.2. Intelligent Sensing Real-Time Control for Intelligent Agricultural Machinery
5.3. Scheduling Optimization of Heterogeneous Nodes under 5G Smart Agriculture
5.3.1. Sense Scheduling for 5G Smart Agriculture
5.3.2. Optimization of 5G Network Signal Coverage Scheduling for Agricultural UAV
5.4. Fault Detection and Self-Healing for 5G Intelligent Agricultural Platform
5.4.1. Node Fault Identification and Early Warning Based on Heterogeneous Sensing Data Fusion
5.4.2. Research on Fault Tolerance Based on 5G Heterogeneous Fusion Sensing Network
5.4.3. Self-Healing Mechanism Based on 5G Heterogeneous Fusion Sensing Network
5.5. AI Application Optimization for 5G Intelligent Agricultural Internet of Things
5.5.1. Lightweight Deep Learning Algorithm Based on 5G-IoT Edge Computing
5.5.2. Multi-Source Data-Sensing Machine Learning Algorithm for Smart Agriculture
5.6. 5G-IoT System Service Model for Smart Agriculture
5.7. Security Issues of 5G Internet of Things for Smart Agriculture
5.7.1. Information Traceability of the Whole Process of Intelligent Agricultural Production Based on 5G Blockchain
5.7.2. Intrusion Detection for Intelligent Agricultural Production Based on 5G
6. Summary
Author Contributions
Funding
Conflicts of Interest
References
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Country | Agricultural Type | 5G Agricultural Internet of Things | Development Representative/Development Characteristics | 5G Policy |
---|---|---|---|---|
America | Large farms | 5G + UAV + GPS+ satellite remote sensing | FarmLogs, Cropx | FCC—$9 billion 5G subsidy program |
Europe | Precision agriculture | 5G + big data + smart agricultural machinery monitoring and control, smart agricultural machinery | Holland, Switzerland, Huawei, Sunrise | 5G commercial promotion |
Japan | Green agriculture and ecological agriculture | 5G + agricultural Internet of Things + smart monitoring | A new farming model with automation and intelligence | 5G falls behind, 6G works |
Israel | Facility agriculture | Intelligent depth sense + intelligent control | Innovative agriculture | 5G networking |
China | Mixed existence of various types of agriculture | Agricultural production is developing towards automation and smart agriculture by leaps and bounds | 5G, smart agriculture, smart agricultural machinery | 5G is being comprehensively promoted and demonstrated in agricultural application |
Time | Overview Journals | Main Focus | Scene |
---|---|---|---|
2016 | M. R. Palattella et al. [16] | From technology, standardization and market prospect | Market Paradigm |
2018 | Shancang Li et al. [17] | Key implementation technologies, main research trends and challenges | Key technologies and trends |
2018 | D. Wang et al. [18] | New paradigm of 5G intelligent Internet of Things (5G l-loT): big data mining, deep learning, and reinforcement learning | New paradigm |
2018 | G. A. Akpakwu et al. [19] | Application requirements of the Internet of Things and the development status of related communication technologies | Communications technology |
2019 | N. Wang et al. [20] | Physical layer security | Security |
2020 | K. Shafique et al. [21] | Prospects of 5G key technologies for the Internet of Things | 5G Key Technology |
2021 | Yu Tang et al. [22] | Opportunities, challenges, and key technologies | Smart agriculture |
2022 | Ogbodo E U et al. [23] | 5G and LPWAN-IoT for Improved Smart Cities and Remote Area Applications | 5G LPWAN-IoT |
2022 | Khanh Q V et al. [24] | Wireless communication technologies for IoT in 5G: vision, applications, and challenges | Wireless communication technologies |
Time | Overview Journals | Theme | Key Words |
---|---|---|---|
2017 | Mekala M S et al. [25] | Smart agriculture cloud computing | Smart agriculture, cloud computing |
2018 | Rahul Dagar et al. [26] | Intelligent Farm IoT | Smart Farm, IoT |
2019 | Farooq M S et al. [27] | Investigation on the Role of the Internet of Things in Smart Farms | Smart Farm, IoT |
2019 | Devare J et al. [28] | Crop generation detection and control | Detection, crops |
2019 | Fiona J R et al. [29] | Image processing and disease detection based on image detection in agriculture | Image processing, disease detection |
2019 | Bh Ag At M et al. [30] | Internet of Things in Smart Farm | Smart Farm, IoT |
2019 | Sarker V et al. [31] | Edge computing Lora in the Internet of Things | Edge computing, Lora |
2019 | Smart et al. [32] | Smart Farm | Smart Farm |
2020 | VIPPON et al. [33] | Progress of Internet of Things in Agriculture | IoT, Agriculture |
2020 | Friha O et al. [34] | New technologies of the Internet of Things for smart agriculture in the future | Future smart agriculture IoT |
2021 | Rayhana R et al. [35] | RFID sensing technology in smart agriculture | Smart agriculture, RFID, perception |
2021 | Xing Yang et al. [36] | Internet of Things for Smart Agriculture in the Future | Smart agriculture, IoT |
2021 | Ye Liu et al. [37] | Industry 4.0 to Agriculture 4.0 | Agriculture 4.0 |
2021 | Bhat S A et al. [38] | Big data and AI revolution for precision agriculture | Big data, AI, precision agriculture |
2021 | Godwin Idoje. et al. [39] | Progress and challenges of intelligent agriculture Internet of Things | Smart agriculture, IoT |
2021 | Wen Tao et al. [40] | Progress and challenge of intelligent agriculture Internet of Things communication technology | Smart agriculture, IoT, communication technology |
2021 | Godwin Idoje et al. [41] | Technological progress and challenges of smart farms | Smart Farm |
2022 | N. N. Misra et al. [42] | Internet of Things, Artificial Intelligence and Big Data in Agriculture and Food Industry | Food industry |
Parameter | Standard | Frequency Band | Time Delay | Data Rate | Transmission Distance | Energy Consumption | Cost | Network Size |
---|---|---|---|---|---|---|---|---|
5G | 3GPP Release-16 | 3–6 GHz | Low | 100 Mb/s–10 Gb/s | Base station signal coverage area | Medium | Medium | Infinite |
4G | LTE | 2.4 G/865 MHz | Medium | 10 Mb/s–1 Gb/s | Base station signal coverage area | Medium | Medium | Infinite |
Zigbee | IEEE 802.15.4 | 2.4 G | High | 20–250 Kb/s | Within 100 m | Low | Low | Below 500 |
Wifi | IEEE 802.11 | 5 GHz-60 GHz | Medium | 1 Mb/s–7 Gb/s | Less than 100 m | Medium | Medium | Below 100 |
NB-IoT | 3GPP Release 13 | 850–900 MHz | High | 160–250 kbps | Base station signal coverage area | Extremely low | Low | <50,000 |
SigFox | SigFox | 200 KHz | High | 100–600 bit/s | Base station signal coverage area | Low | Low | <50,000 |
RFID | ISO18000-6C | 860–960 Mhz | Low | 40–160 kbit/s | 1–5 m | Low | Low | <1000 |
Literature | Application Scenario | Data Type |
---|---|---|
Garcia [52] | Distributed precision agriculture | Video and Data |
He Liu [53] | Video segmentation | Video |
Sabzi S [54] | Monitoring of potato weeds in video | Video |
He Jiang [55] | Fruit disease surveillance based on deep learning | Image |
Type | Describe | Scene |
---|---|---|
Autonomous exception reporting service type | For example, the notification of smoke and fog alarm detector and smart electricity meter power failure, the minimum data demand for uplink data (in the order of cross knots), and the cycle is usually in years and months. | Fishery breeding, precision agriculture |
Business type of independent periodic report | For example, the measurement report of intelligent utilities (gas/water/electricity), intelligent agriculture, intelligent environment, etc., the uplink demand for small data volume (hundreds of bytes), and the cycle is mostly in days and hours. | Plant moisture, environmental monitoring, climate monitoring |
Network instruction service type | For example, when the device is turned on/off, it triggers sending an uplink report, requests meter reading and requires minimal downlink data (in the order of cross knots). The cycle is usually in days and hours. | Automatic irrigation, automatic oxygenation, etc. |
Software update business type | For example, software patches/updates require a large amount of data (kilobyte level) for uplink and downlink, and the cycle is usually in days and hours. | Remote System Update |
IoT OS | Description/Provider | Networked Operating System | Description/Provider |
---|---|---|---|
Brillo [80] | Google’s solution for building connected devices | LiteOS | Huawei |
mbedOS | ARM Internet of Things device platform | TinyOS | Tencent |
RIoT [83] | Internet of Things friendly operating system | AliOS Things | Alibaba |
Contiki [84] | Open source IoT operating system | RT-Thread | Real time operating system (open source) |
Zephyr | Scalable real-time operating system for resource constrained systems | Windows 10 IoT Core | Windows |
Nuttx | Standard compliant and small footprint real-time operating system | WatchOS | Apple |
Literature | Content | Field | Type |
---|---|---|---|
B. Bose et al. [115] | Diagnosis, detection, and classification of cannabis diseases | Plant protection | Classification algorithm |
D. Brunelli et al. [116] | Identify and kill apple pests | Plant protection | Neural network |
R. Medar et al. [117] | Crop yield prediction | Plant protection | Machine learning |
N. Gobalakrishnan [118] | Plant disease detection | Crop diseases and insect pests | Image processing |
M. Merchant [119] | Various nutritional deficiencies of mango leaves | Crop protection | Image processing |
Q. Feng [120] | Tomato harvesting machine | Harvest | Image segmentation processing |
No. | Application Area | Business Attribute | Cover Height/m | Coverage |
---|---|---|---|---|
1 | Agricultural and forestry plant protection | Spraying pesticide | 10 | countryside |
2 | Agricultural and forestry surveying and mapping | Agricultural land survey | 200 | countryside |
3 | Agricultural inspection | 1080p Video return | 100 | Patrol inspection covers field agriculture |
4 | Agricultural formation flight | UAV formation flight | 200 | countryside |
5 | Future cloud AI | UAV cloud-based autonomous flight | 300 | countryside |
6 | Agricultural and forestry monitoring | Crop growth monitoring | 100 | countryside |
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Liu, J.; Shu, L.; Lu, X.; Liu, Y. Survey of Intelligent Agricultural IoT Based on 5G. Electronics 2023, 12, 2336. https://doi.org/10.3390/electronics12102336
Liu J, Shu L, Lu X, Liu Y. Survey of Intelligent Agricultural IoT Based on 5G. Electronics. 2023; 12(10):2336. https://doi.org/10.3390/electronics12102336
Chicago/Turabian StyleLiu, Jun, Lei Shu, Xu Lu, and Ye Liu. 2023. "Survey of Intelligent Agricultural IoT Based on 5G" Electronics 12, no. 10: 2336. https://doi.org/10.3390/electronics12102336
APA StyleLiu, J., Shu, L., Lu, X., & Liu, Y. (2023). Survey of Intelligent Agricultural IoT Based on 5G. Electronics, 12(10), 2336. https://doi.org/10.3390/electronics12102336