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Digital Twin in Smart Farming

Digital Twin in IoT_071922A
[Digital Twin in IoT]

- Overview

A digital twin is basically the digital equivalent of a real-life object that mirrors its behavior and state during its lifetime in a virtual space. Using a digital twin as a central means of farm management can decouple physical flow from its planning and control. Digital twins remove fundamental constraints about place, time, and human observation.

Agriculture no longer requires physical proximity, allowing remote and automated execution, monitoring, control and coordination of farm operations. This allows to separate the physical flow from the informational aspects of the farm process.

Digital twins can also be enriched with information that cannot be observed (or inaccurate) by the human senses, such as sensor and satellite data, or data provided by other information owners. Additionally, a key aspect of a digital twin is that it can use advanced analytics to add intelligence. Therefore, the digital twin not only represents the actual state, but also analyzes the historical state and simulates future behavior. Thus, if properly synchronized, digital twin-based applications can enable farmers and other stakeholders to take immediate action in the event of (expected) deviations.

Digital twins hold great promise to take smart farming to new levels of agricultural productivity and sustainability. Despite the recent interest in digital twins, a good foundation for development and implementation is still underway. In particular, the application in the field of smart agriculture is still in its infancy. There are some exploratory studies and case studies on the use of digital twins in farm management, but further research is needed especially in the management of using digital twins to plan, monitor, control and optimize farm processes.


- Digital Twin and Smart Farming

Modern agricultural production is impossible without reliable and up-to-date information on farm operations. Farms are increasingly relying on digital technologies such as sensing and monitoring equipment, advanced analytics and smart devices. Driven by the rapid development of technologies such as cloud computing, Internet of Things, big data, machine learning, augmented reality and robotics, agricultural production is rapidly shifting to smart agricultural systems. 

Smart farming can be seen as the next stage of precision farming, where management tasks are based not only on precise location data, but also on contextual data, situational awareness, and event triggering. 

A smart farming system can be viewed as a cyber-physical control loop that seamlessly integrates sensing and monitoring of all relevant farm processes, intelligent analysis and planning, and intelligent control of farm operations (“whole farm management perspective”). 

In a smart farming system, farmers can remotely monitor and control operations based on (near) real-time digital information, rather than direct observation and manual tasks in the field. 

Therefore, farmers are automatically notified if there is a problem or any problems are expected to arise. From behind a desk or smartphone, they can check on the scene or the stable by viewing rich digital images of plants, animals or machines. At the same time, machine learning algorithms enhance the digital view with object-specific analysis and recommendations. 

Farmers can simulate corrective and preventive actions and assess their impact on the digital representation. Finally, selected interventions can be performed remotely, and farmers can again use the digital view to verify that (expected) problems are resolved. It can also be expected that this smart farm management cycle will become increasingly autonomous, eliminating the need for human intervention from farmers. 

In conclusion, you can say that every object in the farm (eg crops, fields, cows, equipment) is virtualized and can be increasingly controlled remotely. The digital twin is an appealing metaphor to describe this development.



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