5G for Agriculture

Harvesting New Possibilities

5G for Energy
5G for Energy

Pain points in the Agriculture industry

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Pain Point 1

Shortage of labour

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Pain Point 2

Value leakage across the whole chain

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Pain Point 3

Struggle to support ESG requirements

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Pain Point 4

Inadequate storage and maintenance leading to 6% crop loss annually

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Pain Point 5

Non-optimal pesticide usage

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Pain Point 6

Inefficient water use

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Pain Point 7

Non-uniform growth of crops impacting product quality and volume

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Pain Point 8

Need for improvement of livestock monitoring

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How 5G-enabled digital solutions can help

5G can maximise automation looking at the total end workflows. The order of priority will be driven by the maximum labour savings impact.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Install the sensors as a result of the workflow (see below)
  • Be ready to install a broad spectrum of sensors: 5G (images) and others (temp / water / soil / etc.)  

Step 2: Connectivity

Step 2: Connectivity More Information
  • Equipment (time series) sensor data transmission
  • Information from storage facilities is also sent to Enterprise storage

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • 5G (real-time) collecting and actioning data to be exploited to see how it can reduce labour numbers

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • Equipment data stored to train AI algorithms
  • All data stored in Enterprise storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • AI (ML/MV) models to be developed to drive down labour needs (automation / robots) 

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Executing the agreed workflows should result in minimal need for human intervention

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • End of process

Application logic

Scenario 1

  • List all end to end workflows and order by labour usage: high to low.
  • Once we have that list, determine for each workflow how 5G-enabled Digital Services can make a difference and paint the end-to-end picture of what needs to happen at each step.
  • Also list how other digital services (LoRaWAN based) can help.Implement workflows described above.
  • Assume that combination of 5G and non-5G will make the difference. 

Scenario 2

  • 5G is important.
  • Developing AI – ML/MV models to predict will take a number of iterations where accuracy of these models (predictions) will improve over time.
  • SME involvement working with data scientists is required to develop the models.
  • Both MV and ML models to be used.
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Expected benefits

  • Increase productivity level of each labourer (harvestors)
  • Better oversight on harvesting productivity at bloc-level and harvester-level
  • Capturing of activities in real-time via 5G for proactive action

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

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Key value created

  • Increased production with less labour

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

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How 5G-enabled digital solutions can help

Monitor leakage using sensors (and AI-MV) across the whole chain and take actions where leakage is severe.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Install the sensors as a result of the value leakage results (see below) including delivery to mill. 
  • Be ready to install a broad spectrum of sensors 5G and non-5G as well. 

Step 2: Connectivity

Step 2: Connectivity More Information
  • Equipment (time series) sensor data transmission
  • Information from storage facilities is also sent to Enterprise storage

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • 5G (real-time) collecting and actioning data to be exploited to see how it can save extra labour

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • Equipment data stored to train AI algorithms
  • All data stored in Enterprise storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • AI (ML/MV) models to be developed to drive down value leakage
  • Focus on high to low value leakage priorities

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Executing the agreed workflows should result in minimal need for human intervention 

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • End of process

Application logic

Scenario 1

  • Have a clear description of the activities across the whole chain: Activities should be taken here in its broadest sense: Whilst harvesting; transport in Plantation; Transport from Plantation to Mill. 
  • Isolate the parts where value leakage seems to be considerable and focus on how this can be addressed using 5G-enabled Digital Services.
  • This will determine the sensors we need.

Scenario 2

  • Developing AI – ML/MV models to predict will take a number of iterations where accuracy of these models (predictions) will improve over time.
  • SME involvement working with data scientists is required to develop the models.
  • Both MV and ML models to be used.
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Expected benefits

  • Improved crop handling process
  • Reduced crop loss from pre-emptive action
  • Improved efficiency through real-time monitoring via 5G

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

icon

Key value created

  • Increased yield due to focus on value leakage

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

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How 5G-enabled digital solutions can help

Collect all data needed for ESG reporting: Define important data sources and do make sure that sensors, etc. + ML/MV are used when we collect that data.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Based on the sources of ESG information:
  • What are the data sources? Expect flexibility given changes. 
  • What sensors should be added?
  • Is networking in place?

Step 2: Connectivity

Step 2: Connectivity More Information
  • Time critical data goes to edge + enterprise storage, while other data goes to Enterprise storage

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • 5G (real-time) collecting and actioning data to be exploited to see how it can save extra labour

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • Equipment data stored to train AI algorithms
  • All data stored in Enterprise storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • AI (ML/MV) models to gather and predict ESG data

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Provide the measurement results

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • End of process

Application logic

Scenario 1

  • List the critical ESG Data sources.
  • Make sure that this list is complete.
  • For each data source, make clear how the data is collected and if/what sensors need to be added.
  • If/What are the ML/MV models to be developed to support/make the calculations.

Scenario 2

  • Developing AI – ML/MV models to predict will take a number of iterations where accuracy of these models (predictions) will improve over time.
  • SME involvement working with data scientists is required to develop the models.
  • Assume that data sources will change over time due to the fact that we get to better sources of ESG data.

Scenario 3

  • Be very flexible on how data will be collected since it will change over time due to new insights.
  • Also assume that models will change on a frequent basis.
  • Do ensure that all data gets into the enterprise data store.
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Expected benefits

  • Improve compliance level to mainstream ESG framework
  • Better oversight of operational processes
  • Real-time monitoring over 5G for improved efficiency

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

icon

Key value created

  • ESG data gets reported correctly and efficiently

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

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How 5G-enabled digital solutions can help

Optimise storage levels and improve equipment availability through predictive maintenance by collection of data on storage facilities and equipment.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • For maintenance: Sensors to collect timeseries data from critical equipment
  • For storage facility: Sensors providing information on storage availability (levels)

Step 2: Connectivity

Step 2: Connectivity More Information
  • Equipment (timeseries) data transmission
  • Information on storage facilities is sent to Enterprise storage

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Not time-critical at present, but this is expected to change to real-time in the future

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • Equipment data stored to train AI algorithms
  • All data stored in Enterprise storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • AI (ML/MV) models with inputs from all relevant (timeseries) data to:
  • (ML) predict failure of critical equipment
  • (MV) forecast storage availability

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Information and alerts of critical equipment predicted to fail so action can be taken to replace it in time

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • Overview of storage options

Application logic

Scenario 1

  • Sensors installed on selected critical equipment (e.g. rotating equipment and equipment that would impact crop growth and/or harvesting if not available).
  • As a result, timeseries data will be collected every X seconds and sent to Enterprise storage.
  • Cameras are installed at each storage facility to monitor stock levels, and images will be sent to Enterprise storage for MV monitoring.

Scenario 2

  • All data collected is non-critical since it contains predictions of days ahead.
  • Over time, this could change to real-time when fault rectification of certain equipment is time sensitive.
  • Developing AI – ML/MV models to predict will take a number of iterations where accuracy of these models (predictions) will improve over time.
  • SME involvement working with data scientists is required to develop the models.

Scenario 3

  • The models will be able to predict potential failure of critical equipment and send triggers and alerts to rectify the failure before it happens.
  • Identifying standard images and training of the MV model will be required and ultimately be able to trigger alerts such as low stock levels, predict storage capacity, etc.
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Expected benefits

  • Increase efficient use of storage facility and space
  • Reduce interruptions to farming activities with higher equipment availability
  • 5G will enable real-time activities where required

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

icon

Key value created

  • Reduced harvesting interruptions & better usage of storage to reduce crop losses

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

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How 5G-enabled digital solutions can help

Collect pesticide usage across the plantation and manage usage based on actual crop needs.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Sensors/drones to collect time series data on crop status to discover pesticide needs
  • Sensor for other data: growth levels, soil moisture, etc.

Step 2: Connectivity

Step 2: Connectivity More Information
  • Crop (timeseries) sensor data transmitted
  • Other relevant data

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Not time critical at present, however this is expected to change with availability of equipment with real-time pesticide control

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All edge data collected stored to train AI algorithm
  • All data stored in Enterprise storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • AI (ML/MV) models used to:
  • (MV) detect pests with drones
  • Trigger the right level of pesticides to be applied to by sprayer machines in identified areas

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • End-to-end visibility on crop status, and pesticide needs/usage for real-time preventive action

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • End of process

Application logic

Scenario 1

  • Sensors and drones are used to monitor crop statuses: 
  • Tracking growth levels
  • Crop changes, identifying pesticide needs
  • The use of agriculture sprayers for pesticide application can start offline but will move to real-time (5G) where the amount of pesticides sprayed can be controlled in different areas.

Scenario 2

  • 5G becomes relevant when we want to create a real time loop: Drones collect data about the field, pass the data to the AI (MV) application to scan the results and these will be used to control the provision of pesticides.
  • Without 5G, the required bandwidth will not be sufficient to get the data in real-time to the AI (MV) apps for verification.

Scenario 3

  • AI (MV) models about predicting pest levels (and crop growth levels) and subsequent pesticide levels (and type) to be applied.
  • Build up the MV model in steps using multiple SMEs and Data Scientists to get to end-to-end MV/ML models.
  • Over time, look at what other options are possible to further reduce the need for pesticides.
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Expected benefits

  • Better compliance with regulatory requirements (health & safety, legal) as a real-time experience with 5G
  • Improved farming practices to be more environmentally sustainable

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

icon

Key value created

  • Efficient usage of pesticide leading to better crop yield and value

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

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How 5G-enabled digital solutions can help

Collect data about water usage, type of crops, and other information across the plantation to provide the right level of irrigation to each crop area.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Sensors (fixed or on drones): measuring moisture levels of soil and humidity, rain levels, reservoir levels, etc.

Step 2: Connectivity

Step 2: Connectivity More Information
  • All sensors will transmit data to Enterprise storage

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Not time critical at present, however this is expected to change to real-time in the future

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All edge data collected stored to train AI algorithms
  • All data is stored at Enterprise storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • AI (ML) predicts future humidity levels and provides recommendation of irrigation required
  • Model requires refinement over time

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Reports to provide visibility as the model can operate as a closed loop

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • End of process

Application logic

Scenario 1

  • Information of current soil moisture levels known through sensors at all critical locations.
  • Visibility includes at irrigation storage reservoirs where rainwater can be accumulated with sensors in place to measure the levels in these reservoirs.
  • Information on type of crops that are currently in each crop area across the plantation.
  • Additional information could be obtained through drones for aerial surveys, e.g. monitoring drought or flood situations.

Scenario 2

  • For AI (ML) modeling, additional information could be required:
  • Weather forecast information e.g. temperature; rain levels; etc.
  • Other water storage levels
  • Crop status
  • Identify how the results of the models could be used for prediction of floods and other insights.
  • Joint effort between SME and data scientists is required for model development.

Scenario 3

  • Based on the results of the AI model (predicted soil moisture levels), water supply and irrigation needs can be predicted; combination of rain and other water supply (where needed).
  • Large amounts of data are important as inputs to these ML models.
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Expected benefits

  • Early insights into possible droughts and floods
  • Visibility of water needs and storage levels for planning
  • Improve crop growth due to optimal irrigation

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

icon

Key value created

  • Improved crop growth and efficient management of water usage

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

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How 5G-enabled digital solutions can help

Obtain crop growth rates through collection of data, monitor and identify areas where optimum growth is impacted, apply AI to identify possible reasons, and suggest actions to address problems.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Drones could monitor progress of crop growth
  • Sensors (fixed or on drones) to collect information, e.g. soil moisture levels, nutrients, pest, or any specific crop sensor

Step 2: Connectivity

Step 2: Connectivity More Information
  • All sensors will transmit data to Enterprise storage

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Not time critical at present, however this is expected to change to real-time in the future

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All edge data collected is loaded into Enterprise storage
  • All data is stored at Enterprise storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • AI (MV) models provide information of current growth rates vs expected levels (predicted)
  • (AI – ML) Provide potential reasons if rate is below expected growth levels, and suggest actions

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Reports providing visibility of crops, as model can operate as a closed loop

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • End of process

Application logic

Scenario 1

  • Drones would be a key enabler to capture data over large areas to detect abnormalities, e.g. growth, pests, soil conditions, etc.
  • Begin data collection offline, however prepare for evolution to real-time once 5G becomes available when developing MV models.

Scenario 2

  • Development of AI (MV) models could include various other information (e.g., expected crop growth rate, plantation area, etc.) and produce results such as predicting growth levels for the foreseeable future.
  • Development of the MV model is done through an iterative process where multiple models will be required to detect and predict growth.

Scenario 3

  • The model created could start with simple predictions such as rain vs irrigation needs, detection of colour changes to crops indicating pests and could increase in complexity over time.
  • Joint effort between SME and Data Scientists is required for model development.
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Expected benefits

  • Early detection to address potential factors hampering crop growth
  • Improve yield and quality of crops
  • Efficient management of resources (e.g., water, pesticides, fertilisers)

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

icon

Key value created

  • Increased crop yield and quality

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

icon

How 5G-enabled digital solutions can help

Collect data about location of livestock, detection of diseases, production (e.g. milk production) and other data to drive individual measures.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Personalised tags for identified livestock
  • Selection of types of tags will depend on data required
  • Tags will collect a broad spectrum of data

Step 2: Connectivity

Step 2: Connectivity More Information
  • Sensor data is transmitted

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • 5G-enabled real-time feedback would be useful to alert issues (e.g. illness) of animals

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All edge data collected from the tags stored to train AI algorithms
  • All data is stored at Enterprise storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • AI (ML) models to be developed to detect changes to predict illness, e.g. temperature
  • AI (MV) could be used to understand movement and behavioural change

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Alert if any critical data changes

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • End of process

Application logic

Scenario 1

  • Livestock tags will aim to collect a broad spectrum of data:
  • Location of livestock
  • Temperature
  • Any other information
  • Sensor data could be augmented to collect data about the status of the land (environment humidity, temperature, etc.)
  • Consider whether cameras/images (use of drones) could provide additional information.

Scenario 2

  • Data collected could be time critical to be processed (e.g. detecting illness), where edge computing is required. However, this can be adopted at a later stage.
  • Identify information that could be extrapolated based on the sensor data collected.

Scenario 3

  • Data needs to be studied to determine the ML models that could be developed. Assessments such as symptoms indicating illness that could be predicted by changes in data (e.g. temperature).
  • Joint effort between SME and data scientists is required for model development.
  • Consider how MV can help here as well and that will have an impact on sensors and data to be collected.
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Expected benefits

  • Prevention of disease outbreak through early detection and containment
  • Detection of abnormal behaviours to address potential issues hampering yield

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

icon

Key value created

  • Maintaining a healthy livestock for better production

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

Cross-industry solutions

Autonomous robots and aerial drones can traverse harsh and dangerous environments and reduce hazard risks for human workers. Such technology will provide enhanced monitoring and safety upkeep for facilities. They can be fitted with various sensors geared towards the purpose they are meant for and by using 5G, we can deploy them in real-time whereby the data collected can be sent directly to an AI application for investigation and immediate further action.

Wearable technology allows security and medical personnel to conduct background and health checks efficiently. Of course, they have a much broader focus and can be used in many different industries where clothing has been enriched with wearable technologies. Wearables are also able to collect extra information for a 5G-enabled digital service.

Sensors are crucial for capturing real-time data and insights and pave the way for more informed decision-making. Sensors will be the data collection point for every 5G-enabled digital service and therefore will have to cover a broad spectrum of types of information to be collected and the way they are connected to the DNB 5G network.

XR is set to take work and play to the next level with cutting edge technology never seen before. These devices have the potential to boost productivity and enhance entertainment, changing the way we experience our reality through digital transformation. There is an increasing broad spectrum of devices available for both consumer and enterprise markets, making it increasingly important for each user experience.

DNB Ecosystem

Disclaimer of Endorsement

All information provided on potential 5G-enabled solutions and potential vendors is provided for information purposes only and based on our awareness of the available 5G-enabled solutions and potential vendors in the market. It does not constitute endorsement, recommendation or favouring by DNB. It is your responsibility to verify and evaluate such 5G-enabled solutions and potential vendors. DNB is not an agent or legal representative of any of such 5G-enabled solutions and potential vendors, is not associated or endorsed by such potential vendors, has no authority to act on behalf of any such potential vendors, and will be an independent party if you enter a business relationship with such potential vendors. Third party links included are for convenience only and are not under DNB’s control. DNB does not assume any responsibility or liability for your use of such third-party links.