Digital Twins: what are they and how can they shape agriculture?

We live and breathe digital; this digital revolution is changing the way we interact with the world, helping us optimize processes that we never thought could be optimized. Digital twins, e.g. virtual replicas of physical systems, are changing crop management and spraying techniques. In this blog post, we’ll explain how digital twins can be utilized and how they will be used within the Smart Droplets project framework.

What are Digital Twins?

Digital twins are virtual models that represent real-world entities, processes, or systems. They are created using data from sensors, advanced algorithms, and machine learning to mimic the behavior of their physical counterparts. Digital twins are widely used in various industries, such as manufacturing, aerospace, and energy, to optimize processes, reduce costs, and enhance performance. In agriculture, digital twins enable farmers to simulate and analyze crop management practices, leading to improved decision-making and optimized farming operations.

How can they be utilized in crop spraying?

Crop spraying is a critical part of modern agriculture, as it helps farmers manage pests, diseases, and nutrient deficiencies effectively. Traditional spraying methods can take up valuable resources, such as time and labor, while at the same time, they are not as precise and can lead to wastage of resources and environmental harms. Using digital twins in agriculture can help in the following aspects of crop spraying:

1. Precision Spraying: With the creation of accurate models of agricultural fields, digital twins allow farmers to apply the right amount of pesticides and fertilizers, thus reducing waste and minimizing the environmental impact.
   
2. Predictive Analysis: Digital twins can simulate and predict the spread of pests and diseases, along with the help of advanced algorithms and machine learning techniques. These predictions allow farmers to intervene early on, reducing the need for usage of pesticides.
3. Resource Optimization: Farmers have the ability to monitor and analyze the efficiency of their spraying equipment; this allows for data-informed decision-making, which in turn, can lead to better performance and reduction of operational costs.
4. Remote Monitoring: Digital twins allow farmers to easily monitor their fields no matter where they are, thus it saves time and labor, while minimizing human error in the crop spraying process Farmers can monitor their fields remotely with digital twins, saving time and labor while minimizing human error in the spraying process.
   
   

Digital Twins and Smart Droplets

Smart Droplets solution adopts a hybrid approach for spraying recommendations, combining predictions on regional, farm and plant level. It will also be able to flag anomalous situations in which human intervention is required.

An integral part of Smart Droplets is the AI-based Digital Twin sub-system that combines data and experts’ agronomic knowledge. This is crucial for achieving the Green Deal targets without disrupting current production systems. Data-driven approaches will be deployed to overcome discrepancies in current farm management systems, where ever-increasing data are collected in farms, but are buried in data silos and their value is never realized.

Expert agronomic knowledge will be embedded into these data-driven approaches to avoid yield losses and to facilitate interpretability of the results. Towards this data-driven decision making, digital twin technology will be used to encompass these approaches and replicate the behavior of the actual physical system.

How will Digital Twins advance current technologies?

The developed Digital Twin solution will advance the current state-of-the-art in three dimensions:

1. Combination of information from real-time and historical data streams, from time-series and spatial explicit data streams, considering the temporal and spatial dimensions of the physical system
   
2. Ability to multitask, e.g. giving spray dosage recommendations for several tanks, yield protection, decision making simulations and alerts
   
3. Enhancing the capacity of the digital twins model, to generalize through AI tools, whenever limited data are available, allowing further exploitation of the model in a broad range of applications.
   

Combining the aforementioned advancements, the aim is to create a complete Digital Twin solution to be deployed in open field applications, with a consistent operational readiness. The ultimate goal is to optimize farming operations, enhance performance and reduce costs, while promoting sustainable agricultural practices.