Digital twin in agriculture: everything you need to know about this technology

Imagine having a tool that lets you view your farm’s progress like a time-lapse video, where you can pause, rewind, and even fast-forward. With digital twins in agriculture, this idea isn’t far-fetched. They offer a detailed, real-time snapshot of your farm’s past performance, current operations, and future potential. These virtual replicas empower you to make data-driven decisions, optimize processes, and foresee challenges before they arise.

Digital twins in agriculture become more widespread and we wonder whether such a trend is sustainability -driven or are there other factors? In this piece, we want to look into the concept of digital twins and its relevance to agriculture. Is it another posh term for selling technology or actually a practical tool for farmers around the globe? 

How does digital twin in agriculture businesses work?

So, let’s explore what digital twins in agriculture are, and what practical recommendations you can use if you are considering it. 

Digital twins in agriculture refer to virtual representations of physical farming systems, processes, or assets that use real-time data to model, simulate, and analyze agricultural activities. These digital replicas allow farmers, agronomists, and agribusinesses to monitor and optimize operations remotely by comparing the virtual twin’s behavior with the real-world farm conditions. 

While the concept has existed for decades, particularly in the use of 3D CAD models, asset modeling, and process simulations for manufacturing validation, recent advances in AI and machine learning have thrust digital twins into the spotlight. Notably, organizations like NASA have long utilized such simulations for complex projects, but now, these innovations are driving digital twins as a transformative trend poised to disrupt industries on a larger scale.

 Here’s how they are used:

1. Crop Management. Digital twins model specific fields, including soil health, crop growth, and environmental factors like weather. This helps farmers forecast yield, optimize irrigation, fertilization, and pest control, and make decisions on planting or harvesting times.

2. Livestock Monitoring. For animal farms, digital twins create a virtual model of livestock, tracking vital signs, nutrition, and overall health. This enables early detection of disease, optimization of feeding schedules, and prediction of reproductive cycles.

3. Equipment Efficiency. Digital twins replicate farm machinery and equipment performance. They allow farmers to remotely monitor machines, predict maintenance needs, and optimize operational efficiency by reducing downtime and increasing productivity.

4. Sustainability and Environmental Impact. They help simulate the environmental impact of farming practices, such as carbon emissions, water usage, and soil depletion, allowing farmers to adjust practices to improve sustainability.

By using IoT sensors, drones, and AI-powered analytics, the digital twin concept in agriculture enables more precise and data-driven decisions, resulting in higher yields, reduced waste, and optimized resource usage​.

Digital twins in agriculture: use cases

Developing digital twins in farming is revolutionizing the industry by addressing challenges in monitoring, productivity, and sustainability. Digital twin technology enables farmers to create virtual models of physical assets—such as crops, livestock, soil, and equipment—allowing them to simulate, analyze, and optimize agricultural processes in real time. These digital twin use cases are enhancing overall efficiency and sustainability across various aspects of agriculture, from crop health monitoring to equipment maintenance, paving the way for smarter, data-driven decision-making and more resilient farming practices.

Livestock Management

CATTLEytics. Specializes in creating digital twins for cattle farming, enabling farmers to monitor individual animals’ health, behavior, and productivity. This technology assists in early disease detection, optimizing feeding strategies, and improving overall herd management.

Connecterra. Utilizes digital twins to monitor dairy cows, providing farmers with insights into animal health, behavior, and productivity. This approach enhances herd management and operational efficiency.

Greenhouse horticulture

Fraunhofer IESE. Explores digital twins in greenhouse environments to optimize conditions for plant growth. By simulating various scenarios, they assist in achieving optimal yields and resource utilization.

Farm Equipment Optimization

John Deere. Implements digital twins to analyze real-time data from their machinery. This enables predictive maintenance and optimization of equipment performance, reducing downtime and operational costs.

Soil Carbon Sequestration

Downforce Technologies. Creates virtual models, or digital twins, to monitor and predict soil carbon changes over time. This aids in understanding the impact of regenerative practices on soil health and carbon sequestration.

How easy is it to implement into your business?

Implementing digital twins into a business can vary in complexity depending on the industry, the technology infrastructure, and the specific use case. However, several factors can make the process smoother:

Data Readiness. One of the first steps is ensuring you have the necessary data infrastructure in place. Digital twins rely heavily on real-time data from IoT sensors, machine learning models, and historical data. If your business already has IoT systems or data collection processes, implementation will be more straightforward​.

Technology and Expertise. The next key factor is having the right technical expertise. Businesses with access to strong IT teams or external partners with IoT, AI, and cloud computing experience will find it easier to implement digital twins. Companies like John Deere and BASF have pioneered this space with significant R&D support, but smaller companies can rely on third-party solutions that package these technologies into easier-to-deploy systems.​

Costs and Resources. Financially, implementing digital twins can be costly, especially in terms of sensors, data integration, and ongoing maintenance. However, the return on investment is typically high, given the potential to optimize operations, improve maintenance schedules, and reduce downtime​

Scalability. The ease of integration also depends on the scale of the system. Startups or smaller businesses can start with pilot projects, using a digital twin to model specific parts of their operations before scaling up. Platforms like Xarvio provide industry-specific digital twin solutions, which simplify the process for businesses without needing to build everything from scratch​.

How Can Qaltivate Help You with Digital Twins in Agriculture?

At Qaltivate, we take a holistic approach to implementing digital twins in agriculture. We begin with a thorough assessment of your current business operations, helping you set a transformation pace that suits your needs. To design a digital twin system tailored specifically to your business, we evaluate your level of digitalization, including data collection methods and tools that can enhance this process.

For deeper insights on innovations like digital twins, tune into our Digital Ag Global podcast! In one of our recent episodes, we explored animal tech and discussed how digital twins are transforming livestock management. Check it out to discover how these tools are shaping the future of agriculture.