Author | Jayachandran RameshBabu and Chidrupaya Samantaray |
---|---|
Title | IIoT Engineer and Director – Industrial Automation |
Organization | Accessible Engineering Innovation |
Date | September 20, 2020 |
Time | 33 Minutes |
Presentation | https://www.brighttalk.com/webcast/12231/436130/dds-use-case-smart-agriculture |
Document | http://public2.brighttalk.com/resource/core/299426/dds-agriculture-full-deck_666817.pdf |
This document provides an overview of a digital agriculture use case including the wide range of challenges and its solution architecture using the Data Distribution Service™ (DDS™) technology. The goal of digital agriculture and precision agriculture is to improve production while minimizing the cost and resource utilization.
During the industrialization of agriculture, the mechanical machineries helped farmers reduce effort and time while increasing yield per acre. But the risks of rapid climate change plus growing demand for food products in market (such as food supply chain, food processing industries) for an increasing population, have increased dramatically. This demand has created pressure on the existing agriculture methods with a wide range of challenges. At the same time, it has created areas of opportunity for improvement.
Immediate Challenges in Industrial Agriculture
Digital Agriculture solutions
The integration of newer IoT technologies and devices into the agricultural practices is now pushing farmers to migrate to digital farming and precision agriculture. This advancement allows them to benefit from the current demand for agricultural products while resolving related challenges and risks. Digital farming and precision agriculture is reliant on a system of sensors and controllers along with robust communication technologies on every piece of equipment, throughout every stage of the farming process.
Growth Prospects of Digital Agriculture
These are the list of use cases within digital agriculture includes…
In this webinar we will focus mainly on the crop management segment.
Crop Management
Digital farming and precision agriculture is reliant on a system of sensors, controllers along with robust communication technologies throughout every stage of the farming process, and on every piece of equipment to perform the work more efficiently…
The various types of sensors, drives and distributed control systems drive the need for a connected network which can facilitate the exchange of data in real-time/non real-time. This Data-Centric distributed system needs to be precise, highly complex, dynamic, secure and robust.
This is a small representation of what a digital farm may look like…
Hyperspectral Sensor | That helps in detect, mapping minerals, and classifying agricultural crops. |
---|---|
Multispectral Sensor | capturing images, evaluate soil productivity, and analyzing plant health. |
Fluorescence Sensor | For measuring macro nutrients in plants. |
Thermal Sensor | For measuring the temperature of plants and soil. |
Moisture Sensor | To evaluate the crop conditions. |
Airborne Sensors | Airborne sensors in drones (UAV), are used for measuring the crop ??????( [nick]Very hard to understand what he said right here; Time-Stamp: 11:22) |
Motion Detection Sensor | Helps to avoid collision between the vehicles. |
Now you can imagine the number of sensors used for crop management and its not limited to this list. So here comes the complexity of configuring, controlling, and the maintenance of this bigger population of sensors in Digital Agriculture.
Interoperability |
| A typical digital platform can have 'n' number of sensors for drones, and autonomous machinery that also includes 'n' number of sensors within them. All of these working independently using a custom application platform and communication methods. So integration and commissioning of such a system becomes highly complex, time consuming, and costly. |
---|---|---|
Maintenance |
| The maintenance of such a complex system introduces high level of render dependency. This causes production delays, man-power loss, and possibly bottlenecks in achieving digitalization code. This also creates a challenge in any kind of customization, reconfiguration, and scaling or replacement of existing systems. |
Security |
| |
Financial Resources |
| Some of the financial challenges are linked to the high cost of hardware and maintenance. |
These, listed above, are the major production challenges in Digital Agriculture.
Data Distribution Service (DDS) simplifies the complex requirements listed above in a scalable way, making the digital agriculture process more effective.
The DDS data-oriented design and decoupling feature provides flexibility and modular structure in the system. For example, in the irrigation stage, the digital agricultural appliances such as moisture sensors, irrigation motors and soil health sensors need to be associated in one-to-one, one-to-many and many-to-one secenarios for exchanging information, in order to decide the size of the watering distribution areas and their quantity/duration. Using DDS, the equipment can be grouped as participants of different functional domains. Only the equipment belonging to the same domain can communicate with each other. DDS also enables a dynamic re-configuration of this association mapping during runtime.
Data Distribution Service (DDS) listeners in the entity provide a mechanism for the middleware to asynchronously alert the application of the occurrence of relevant status changes.
For example:
Using DDS, event handling of asynchronous events and conditional variables are shared on equipment including self-driving vehicles which alert among themselves to prevent damage and risks.
Web-enabled DDS Services authenticate and control the DDS global data space (i.e. read/write topics) using standard web protocols such as RESTful, SOAP, HTTP etc, from the standard web client such as web browsers. For example, in farming, there are a number of digital agricultural appliances involved in the agriculture field where the farmers need access to each resources to use the digital agriculture methods and processes effectively. This needs a simplified UI dashboard for data visualization and control. By leveraging DDS web services, the parameters (i.e. topic of the agricultural appliances) can be read/write remotely.
The farmers can use the UI dashboard via the browsers in the handheld devices from any location to control the digital agricultural appliances.
Data Distribution Service (DDS) security plugins and secure Real-time Publish-Subscribe (RTPS) messages protect against the unauthorized access and subscription of data throughout the digital agricultural ecosystem. For example, there are different vendor-devices involved in the digital agriculture appliances, operating in an open area along with equipment in nearby farms. In this situation, the appliances must have high Data Security in order to avoid threats and vulnerability and at the same time prevent unintentional information sharing and cross-talk. Using DDS-Security configurations and security policies defined for data distribution, the explicit policies created for protecting DDS domains based on the Data Models can ensure that the equipment information is not visible or discovered between different configured domains.
So here is the solution architecture, as we can see here an on boarding of sensors to machineries, to integration into the cloud is seamless using unified DDS platform. Finally the concept of connected digital agriculture and sensors integrated onto the equipment makes the farming process more data driven and data enabled. Leveraging the intensive DDS integration capabilities and features that addresses the vertical and horizontal needs such as security, scalability, and robustness of data distribution. With DDS, real-time goals of digital agriculture can be realized cost effectively and without fragmentation to the system.