Digital Twin Concept and Its Applicability in Agriculture

It enables real-time monitoring and optimization of soil quality, crop, and equipment for enhancing productivity, decision-making, and sustainability.

 

Digital Twin Concept and Its Applicability in Agriculture

Authors: 

✏️ The Agrineer 2023 Publication team

Introduction

Dr. Michael Grieves from the University of Michigan first used the phrase "digital twin" in 2002. He described it as a virtual representation or simulation of a real-world thing, activity, or system. The idea of establishing a virtual version of an actual object or system that allows for study, prediction, and optimization served as inspiration for the concept. A digital twin is essentially a virtual equivalent or digital copy of a real-world system or thing. Data from numerous sources, including sensors, Internet of Things (IoT) devices, and other data collection techniques, is used to build it. The creation of precise insights and predictions is made possible by the continual feeding of the digital twin with real-time data from the physical system.  

The digital twin acts as a representation of the real object, recording all of its traits, actions, and interactions with its surroundings. It allows for concurrent physical counterpart monitoring, analysis, and simulation. Digital twins can offer insightful information, speed up decision-making, and enhance the functionality of the physical system by combining data analytics, modeling, and simulation techniques. Digital twins can be used in any company or field. They can be used for a variety of things and systems, such as construction projects, production lines, transportation networks, and, naturally, agricultural practices. Digital twins can be used in agriculture to mimic and simulate a variety of agricultural ecosystem components, including soil quality, crop growth, equipment performance, and supply chain processes.  

The way that digital twin technology functions is by simulating an actual process, system, or object in a virtual environment. It entails gathering and integrating instantaneous data from sensors to produce an extensive representation. Based on the combined data, a mathematical or computational model is constructed and used to produce the digital twin. The digital twin receives continuous, real-time data feeds from the physical system, enabling analysis, visualization, and controlling. To improve the functionality of the physical system, insights, forecasts, and recommendations are provided by the digital twin. The physical system can be influenced by the digital twin through feedback and control mechanisms, and continual development makes sure the digital twin is updated with fresh information. Overall, digital twins facilitate immediate monitoring, analysis, and optimization of complex systems by creating a virtual counterpart that mirrors the physical system.

Digital twin application in different areas of Agriculture:  

Soil quality

The measurement of soil quality using digital twin technology has changed agricultural methods. Digital twins build a virtual representation of the soil environment using information from weather stations, soil sensors, and historical records. This makes it possible to regulate and analyze soil characteristics, moisture levels, nutrient levels, and pH balance in real time. Farmers may choose crops wisely and implement effective irrigation measures and nutrient management strategies using this knowledge. With the help of digital twins, irrigation timing and volume can be improved, resulting in less water stress on plants and more effective water utilization. By spotting nutrient shortages or surpluses, they also support precision agriculture by enabling farmers to alter fertilizer application procedures for optimum plant development and minimal environmental effect. Farmers may control soil pH with the help of digital twins, ensuring that nutrients are available to plants. Digital twins aid in preventing soil deterioration, erosion, and nutrient depletion by simulating situations and foreseeing the effects of administration decisions.  

Soil quality evaluation in agriculture has been revolutionized by digital twin technology. Digital twins enable farmers to optimize irrigation, fertilizer regulation, and crop choice by offering realtime insights about soil conditions. This results in enhanced soil health, increased output, and environmentally friendly farming methods. Precision farming is made possible by digital twins, which also enable proactive decision-making to keep soil quality and nutrient balance intact. Farmers are able to improve soil quality and promote more effective, ecologically friendly agricultural methods by using the power of digital twins.

 

Food Production  

Digital twin technology is transforming food production by providing concurrent inspecting, analysis, and decision support throughout the production process. Digital twins create virtual replicas that simulate and optimize crop growth, livestock management, and processing operations. By integrating data from various sources such as sensors, weather stations, and historical records, digital twins offer valuable insights for informed decision-making. Farmers can optimize irrigation, fertilization, and pest regulation based on the simulated crop growth parameters, leading to increased yields and reduced resource waste. Livestock management benefits from simultaneous monitoring of animal health and behavior, enabling optimal feed formulation and disease prevention. Decision support tools based on real-time data and predictive analytics empower stakeholders to make informed decisions for improved efficiency and profitability. Digital twin technology is driving more efficient, transparent, and sustainable food production systems.

 

Harvesting

Digital twin technology offers significant benefits in the context of harvesting within the agricultural industry. By integrating data from sensors, machinery, and environmental conditions, digital twins provide synchronic insights and opportunities for optimization. These digital replicas enable farmers to closely observe crop maturity, moisture levels, and other relevant parameters, empowering them to make informed decisions for efficient harvesting operations. With the help of real-time data, digital twins can offer recommendations for adjusting harvesting techniques, machinery settings, and logistics planning. This ensures minimal losses, improved crop quality, and reduced resource waste. Furthermore, digital twins enable predictive maintenance of harvesting equipment by continuously monitoring machine performance and detecting potential issues in advance. By facilitating proactive maintenance interventions, digital twins minimize downtime and enhance the overall efficiency of harvesting processes. In summary, digital twin technology plays a crucial role in optimizing the harvesting process by enabling precise governing, informed decision-making, and predictive maintenance, leading to increased productivity and resource efficiency in agriculture.

 

Post harvest operation:

Digital twin technology plays a crucial role in post-harvest operations, encompassing various aspects such as food processing, storage, supply chain management, and customer consumption.  

Let's explore each of these areas:  

Food Processing:  

Digital twins optimize food processing operations by simulating production lines, monitoring real time data from sensors, and identifying inefficiencies. They enable process optimization, predictive maintenance, and synchronic adjustments to ensure consistent product quality, reduce waste, and improve overall production efficiency. Additionally, digital twins help optimize food processing by identifying bottlenecks, improving quality control, and enhancing traceability. Resource handling is optimized through data analysis, reducing environmental impact and operational costs. By analyzing data on equipment performance, ingredient ratios, and processing parameters, digital twins help streamline food processing operations and enhance productivity.

Storage:  

Digital twins revolutionize the way agricultural products are stored and preserved. By continuously monitoring storage conditions such as temperature, humidity, and air quality, digital twins ensure optimal environments for maintaining product freshness and standard. They provide real-time alerts and recommendations, allowing for proactive interventions to prevent spoilage and extend shelf life. Digital twins facilitate precise inventory administration, reducing losses, and enabling efficient product rotation and allocation.  

Supply Chain Management:  

Digital twins optimize supply chain management by integrating data from various stakeholders, including farmers, distributors, and retailers. They provide real-time visibility into inventory levels, demand forecasts, transportation routes, and logistics operations. Digital twins enable efficient product tracking, traceability, and compliance with food safety standards. By analyzing data on demand patterns, delivery schedules, and market trends, digital twins enhance supply chain responsiveness, reduce waste, and ensure timely delivery of agricultural products.  

Customer Consumption:  

Digital twins also impact customer consumption by providing insights into consumer preferences, trends, and behavior. By analyzing data from customer interactions, purchase histories, and feedback, digital twins enable personalized recommendations and targeted marketing strategies. They help improve customer satisfaction and loyalty by ensuring the availability of desired products, maintaining consistent standards, and facilitating seamless ordering and delivery experiences.  

Conclusion:

In conclusion, the advent of digital twin technology has brought about a paradigm shift in agriculture, offering unprecedented opportunities for monitoring, analyzing, and optimizing various aspects of the industry. From soil quality evaluation to food production, harvesting operations, and post-harvest processes, digital twins have demonstrated their immense value in improving efficiency, sustainability, and decision-making. By leveraging real-time data and creating virtual replicas of agricultural systems, farmers can make informed choices regarding crop selection, irrigation strategies, nutrient management, and equipment maintenance. The integration of digital twins in agriculture has the potential to revolutionize the industry, paving the way for enhanced productivity, reduced resource waste, and more.

This is the web copy of an article that was originally published in the print version of 'The agrineer 2023' - Annual Magazine.