A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming
Abstract
:1. Introduction
- Field Monitoring: Smart farming helps in reducing crop waste by adapting better monitoring, accurate data obtaining and data processing.
- Livestock Monitoring and Tracking: Smart farming helps to identify the location of animals grazing in open area within big stables. Technology also helps to measure the status of ventilation and air quality in farms and detect harmful gases from excrement.
- Application in Green Houses: Smart farming controls micro-climate conditions towards the aim of maximizing the production and quality of fruits and vegetables within green houses.
- Compost Management: As a measure of preventing fungus and other microbial contaminants, smart farming helps to control the level of humidity and temperature in crops such as straw, hay, etc.
- Offspring Care: Smart farming controls the growing conditions of the offspring in animal farms, hence ensuring their survival and health.
- Exploring two emerging technologies: IoT and UAV, which will be the pioneers of smart farming in the coming years.
- Outlining some major applications of IoT and UAV in smart farming as well as the agricultural industry.
- Exploring the communication technologies, network functionalities and connectivity requirements needed to ensure seamless connectivity for Smart farming.
- Identifying the connectivity limitations and challenges of smart agriculture in remote areas with two case studies. In case study-1, we propose and evaluate meshed Long Range Wide Area Network (LoRaWAN) gateways to address connectivity limitations of Smart Farming—while, in case study-2, we explore satellite communication systems to provide connectivity to smart farms in remote areas in Australia.
- Identifying future research challenges on this topic and outlining directions to address those challenges.
2. IoT and UAVs in Sustainable Smart Farming
2.1. IoT in Smart Farming
- Wireless connectivity technologies with short-distance communication range (at most 10 m): Examples include Bluetooth, RFID, UWB technologies [15].
- Wireless connectivity technologies with a long-distance range (100 m and above): Cellular networks, and Low Power Wide Area (LPWA) technologies which are classified to Non-3GPP (LoRa [18], Sigfox [19] and Weightless) and 3GPP (NB-IoT [20], LTE-M, EC-GSM) technologies that are considered in the long-distance communication range category.
2.2. UAVs in Smart Farming
3. Application Areas of IoT and UAV in Smart Farming
3.1. Monitoring
3.2. Mapping
3.3. Detecting Weed and Infestation
3.4. Planting Seeds and Seedlings
3.5. Spraying Pesticides and Fertilizers
3.6. Forecasting
3.7. Controlling
4. Communication Technologies for Seamless Connectivity in Smart Farming
4.1. Network Functionality and Connectivity Requirements
4.2. Availability and Challenges of Communication Technologies
4.2.1. Cellular Networks
4.2.2. LPWAN Technologies
- LoRaWAN (Long Range Wide Area Network): LoRaWAN was defined by Alliance in article [67]. The network and media access control protocols are both defined on top of the physical layer of LoRa by LoRaWAN, the required parameters at the physical layer are defined by LoRa [61,64,66,68]. The radio frequency bands (169, 443, 868, and 915 MHz) with 0.25 and 12.5 kbps data rates are used in LoRaWAN [9]. LoRaWAN communication connects the LoRa gateway to a number of sensors. Drones (UAVs) equipped LoRa gateways are able to fly over the agriculture regions and gather data from sensors placed on the bottom of the farm. By doing this, users will be able to access the remote and inaccessible areas. In addition, this provides a larger coverage using a single gateway. Some applications cannot take advantage of the LoRa technology because there are still some restrictions in using such a technology. One of the main drawbacks of IoT applications is that a device with LoRa technology can only transmit at most 36 seconds per hour. This is the main limitation that can affect not only the time among messages but also the payload. Therefore the IoT application equipped with LoRa should be programmed in such a way that can adapt itself with these limitations. Moreover, only the half-duplex communication can be supported by the latest LoRa modules, which means a device with LoRa cannot receive and send data at the same time [69].Some of the limitations of LoRaWAN are considered as follows: Firstly, LoRaWAN uses the ALOHA Protocol which is not slotted, in its MAC layer. This means Clear Channel Assessment (CCA) is not performed at all, and packets can be transmitted at any arbitrary time. The collision avoidance mechanism, which is employed in order to cost reduction and simplicity provision, is not used in this mechanism. Secondly, if the devices in LoRaWAN are mobile, there is no certain handover method, as these devices are not associated with a specific gateway. Finally, there are three classes in LoRaWAN including, Class All, Beacon, and Continuous which are called A, B, and C, respectively. Class A is intended to enhance the power efficiency of the sensors, which work with a battery. Actuator nodes use Class B that inherits and performs all the functionality of Class A. The periodic receiving windows are also being opened and are allowed to receive the downlink messages by Class B. Devices in Class C are listening without interruption to receive the messages, hence these devices should have a lot of power.
- Narrow-Band IoT (NB-IoT): NB-IoT is a protocol in mobile communications, especially standardized by the 3GPP standardization group with a 180 kHz bandwidth. Using this bandwidth, the down-link and up-link data rates are considerably reduced around 20 and 250 kbps, respectively. As a result of this, updating Firmware over the Air (FotA) will be hard to reach using NB-IoT. As NB-IoT does not support the handover, considering NB-IoT for mobile IoT applications will be difficult, as well. In addition, current LTE infrastructures need to be upgraded when it comes to using NB-IoT. Therefore, deploying NB-IoT is a difficult task [64,68].
- Sigfox: Sigfox [70] is a LPWAN technology that takes advantage of Ultra-Narrow-Band modulation (UNB) which decreases the levels of noise then the communication rate will increase. It is proper for the lightweight and low data rate IoT based devices. The typical structure of the modulation is Binary Phase Shift Keying (BPSK). In addition, the down-link and up-link communications of each device are limited by Sigfox. Moreover, many countries have used Sigfox and there is no roaming involved.
4.2.3. Comparison in Terms of IoT Factors
- Coverage and Range: The highest coverage range (at least 40 km) is related to Sigfox, and only one Base Station is enough to cover the entire area. The lowest range (at most 10 km) is obtained using NB-IoT which is not adapted for rural regions, and it is only used for LTE infrastructure. LoRaWAN has a coverage range of at most 20 km [72].
- Latency: NB-IoT offers a low IoT latency connectivity, while a low bidirectional latency is provided by Sigfox and LoRaWAN at the expense of increased energy consumption [73]. As a result, the best solution for IoT applications with low latency connectivity and latency insensitive applications can be NB-IoT, LoRaWAN-Class-C, and LoRaWANClass-A and Sigfox, respectively.
- Battery life: The lifetime of NB-IoT based devices is lower than LoRaWAN and Sigfox ones. This is because the energy consumption of NBIoT based devices is more than Sigfox and LoRaWAN devices, as they need to handle QoS and synchronous communication [40].
- Quality of service (QoS): Some applications need the Qos requirements, Sigfox and LoRaWAN are suitable for such applications, while for those applications that need QoS requirements NB-IoT is preferred [74].
- Scalability and Payload length: Sigfox, NB-IoT, and LoRaWAN provide high scalability. NB-IoT not only provides higher scalability than LoRaWAN and Sigfox but also maximum payload length. About 50K connected devices per Base Station is supported by RaWAN and Sigfox while NB-IoT supports twice as much as this number of users [70]. NB-IoT allows the highest payload length of 1600 bytes, while the Sigfox allows the lowest data transmission up to 12 bytes.
- Deployment model: Many countries and cities take advantage of completed LoRaWAN and Sigfoxs’ ecosystems. LoRaWAN is deployed in 42 countries while Sigfox is used in 31 ones [72]. However, NB-IoT was published under rollout to set up its network over the world. Sigfox, NB-IoT, and LoRaWAN technologies are still in the final phase. Public network operation and local network placement via Base Stations are provided by these three technologies.
5. Connectivity Limitations of Smart Farming in Remote Areas
- Longer range connectivity with redundant connections: In remote areas, the IoT devices need to be spread out over a larger area to cover an entire farm. These devices often fail to get connected to nearby internet sources due to their limited communication range. Therefore, existing connectivity range of IoT gateways need to be improved to reduce dependency on backhaul systems.
- Self sustained power source: current IoT system must reduce dependency on traditional main grid power source to make it usable in remote setting. A sensor system could use renewable energy with adaptive energy sharing and management.
- Low and remote maintenance requirement: Inaccessibility is one of the major limitations of remote areas. Hence the IoT devices set in remote locations should have higher durability, improved reliability and low maintenance requirement. They should be equipped with proactive and remote management and maintenance for remote areas.
- Minimal power usages using optimised techniques: Power is a scarce resource in remote area. Therefore, low power consuming techniques are desirable for battery operated nodes in remote areas.
- Privacy assurance: Many of the existing IoT systems ignored users and objects privacy requirement. For sustainable growth and trust, the IoT system must assure privacy of users and objects at minimum.
5.1. Case Study 1: Meshed LoRaWAN Gateways for Smart Farming in Remote Areas
5.2. Case Study 2: Satellite Communication Systems to Provide Connectivity to Smart Farms in Remote Areas of Australia
6. Open Research Issues
- Hardware maintenance and limited energy resources:The perception layer’s hardware are setup in harsh environment, like farms and mining field, which have extreme weather conditions like high temperature, rain, strong wind and extreme humidity, etc. As a result, the electronic circuits of these hardware devices get damaged. Therefore, stronger hardware devices need to be designed that will be less damaged by the harsh environment. Additionally, these devices operate on inadequate battery power consistently for a long period. Hence, alternative energy efficient solutions are required for the end devices because, in case of any program failure, instant battery replacement is complicated, especially in remote areas [35]. Authors in [90,91] suggested energy efficient approaches for the network side; however, energy efficient solutions for the end devices are also desirable.
- Security and privacy issues:The use of IoT contributes to widespread exposure to cyber security risks and vulnerabilities in smart farming. Gupta et al. in [92], introduced some key challenges for security and privacy in smart farming: access control and trust, data, network and compliance, and supply chain. The architecture of smart farming recognizes the high chance of cyber attacks, which needs to be addressed. A smart farm which is a highly connected system and generates a huge amount of data. Since most of the devices used in smart farming are unattended therefore, they can be easily targeted by the attacker. If the attacker is able to compromise a device, then, through that infected device, attacks can spread through the whole network and infected all other devices. In the literature, several machine learning assisted Intrusion Detection Systems (IDS) [93,94] have been proposed to identify the infected device by analysing the network traffic. However, none of them have considered the smart farming specific solution. Therefore, smart farm-specific sophisticated access control and machine learning-based IDS development can be an effective solution in this regard.
- Big data in smart farming:A massive volume of data with a wide variety are captured, stored and analysed for decision-making in IoT and UAV based smart farming. Big data are used to provide predictive insights in farming operation by providing real-time operational decisions. The scope of Big Data applications in Smart Farming goes beyond primary production; rather, it influences the entire food supply chain. The major issues with data analysis are data quality, intelligent processing and analysis, sustainable integration of Big Data sources, etc. [95]. The openness of the platform is also very important since it can empower farmers in their position in supply chains.
- Weed detection and management:Weed control is an important aspect of horticultural crop management. Failure to adequately control weeds leads to reduced yields and product quality. Use of chemical and cultural control strategies can lead to adverse environmental impacts when not managed carefully, and effective weed management strategies that minimise environmental risks through strategic application of control measures can be expensive to implement. Hence, low cost smart tools for identification and mapping of weeds at early growth stages will contribute to more effective, sustainable weed management approaches. Existing studies [26,96,97] have shown some approaches to detect weeds using UAV images; however, they only could achieve less than of accuracy, hence more accurate weed detection approaches are desirable. The authors in [4] presented a shielded band sprayer to spray herbicides in weed, avoiding to spray on crops, hence increase the food quality and reduce the use of plant protection products. However, further research should be carried out, which may indicate the occurrence of statistical differences in the production yield between various weed control methods in crops. It is recommended to continue crop tests based on the band spraying method in terms of the effectiveness of weed control and to extend the scope of tests with the quantitative analysis of the used herbicides and their possible residues in crop yield.
- Multi/hyper-spectral imagery for disease and pest control:Multi spectral and Hyper-spectral image based remote sensing techniques have demonstrated high potentiality in detecting pests and diseases in crops. A multi-disciplinary approach—including plant pathology, engineering, data analytic and informatics—is required [98,99] to utilise the full potential of these highly dimensional, sophisticated and innovative technologies. Besides precision crop protection, plant phenotyping for fungicide screening and resistance breeding can be optimized by these innovative technologies.
- Automated watering control and management in remote areas:the right amount of water usages is important to maintain aesthetic requirements of parks and sport groups. Farms need to water based on the crop’s need. Moreover, efficient water management needs to be implemented to reduce nutrient leaching in the stream by an excessive amount of water in the lower layer of the soil. Using live monitoring of moisture at various soil depths, watering based on Artificial Intelligent (AI), automating of watering and water management using IoT and UAV technology can address most of the above-mentioned objectives.
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
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Acronym | Definition |
---|---|
3GPP | 3rd Generation Partnership Project |
AI | Artificial Intelligence |
AIoT | Agricultural IoT |
BPSK | Binary Phase Shif Keying |
GCS | Ground control station |
IoT | Internet of Things |
LPWAN | Low Power Wide Area Network |
LOS | Line of Sight |
LoRa | Long Range |
LoRaWAN | Long Range Wide Area Network |
M2M | Machine to machine |
NFC | Near Field Communication |
NBIoT | Narrow Band IoT |
QoS | Quality of Service |
RFID | Radio Frequency IDentification |
UAV | Unmanned Aerial Vehicle |
UNB | Ultra Narrow Band |
UWB | Ultra WideBand |
WSN | Wireless Sensor Network |
NB-IoT | SigFox | LoRa | |
---|---|---|---|
Modulation | QPSK | BPSK | CSS |
Interference immunity | Low | Very High | Very High |
Localization | Yes (TDOA) | Yes (RSSI) | Not supported |
Standardization | 3GPP | SigFox company with ETSI | LoRa-Alliance |
Maximum data rate | 200 kbps | 100 kbps | 50 kbps |
Bidirectional | Yes/Half-duplex | Limited/Half-duplex | Yes/Half-duplex |
Maximum message/day | Unlimited | 140 (UL), 4 (DL) | Unlimited |
Maximum payload length | 1600 bytes | 12 bytes (UL) | 243 bytes |
8 bytes (DL) | |||
Coverage | 164 dB | 160 dB | 157 dB |
Power Consumption | Very low | Low | Low |
Security | Very High | Low | Low |
Bandwidth | 200 kHz | 100 kHz | 250 kHz and 125 kHz |
Frequency | Licensed LTE Frequency | ISM Band 433, 868, 915 MHz | ISM Band 433, 868, 915 MHz |
Technology | OpenLTE | Proprietary | Proprietary |
Spectrum | Licensed | Unlicensed | Unlicensed |
Topology | Star | Star | Star |
Downlink Data Rate | 0.5–200 kbps | 0.1 kbps | 0.3–50 kbps |
Uplink Data Rate | 0.2–180 kbps | 0.1 kbps | 0.3–50 kbps |
Range | 1 km (urban) | 10 km (urban) | 5 km (urban) |
10 km (rural) | 40 km (rural) | 20 km (rural) | |
Duty Cycle Restriction | No | Yes | Yes |
Output Power | 23 dBm | 14 dBm | 14 dBm |
Battery Lifespan | years | years | years |
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Islam, N.; Rashid, M.M.; Pasandideh, F.; Ray, B.; Moore, S.; Kadel, R. A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming. Sustainability 2021, 13, 1821. https://doi.org/10.3390/su13041821
Islam N, Rashid MM, Pasandideh F, Ray B, Moore S, Kadel R. A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming. Sustainability. 2021; 13(4):1821. https://doi.org/10.3390/su13041821
Chicago/Turabian StyleIslam, Nahina, Md Mamunur Rashid, Faezeh Pasandideh, Biplob Ray, Steven Moore, and Rajan Kadel. 2021. "A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming" Sustainability 13, no. 4: 1821. https://doi.org/10.3390/su13041821
APA StyleIslam, N., Rashid, M. M., Pasandideh, F., Ray, B., Moore, S., & Kadel, R. (2021). A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming. Sustainability, 13(4), 1821. https://doi.org/10.3390/su13041821