# Application Scenarios of Digital Twins for Smart Crop Farming through Cloud-Fog-Edge Infrastructure
## 5 Possible Application Scenarios with Digital Twin
### Overall Scenario of Digital Twin in Agriculture  
The proposed DT model of agriculture includes a digital representation of farms, fields and other related factors. This complex model dynamically integrates various data sources to replicate the current state of the farm. This model is also continuously updated with real-time data, allowing it to reflect current conditions and predict future states accurately. Figure 2: Cloud–Fog–Edge and MAMS Overview. For instance, if we consider a farm with \(n\) fields, the DT model would have the exact representation with details such as:  
* Farm: Farm ID, Farm Name, Number of Fields, Field IDs. * Field: Field ID, Farm ID, Field Name, Crop Info, Soil Info, Historical Information such as Last Cropping Date. * Zone: Zone ID, Zone Name, Field ID, Farm ID, Weather Data. As illustrated in Figure 3, digital twins assist in decision-making and predictions based on historical and real-time data analysis and provide recommendations and suggestions to farmers and other stakeholders. Digital twins can be applied to various aspects of agriculture in order to help farmers, including:  
* Real-time Monitoring and Prediction [5]: DTs can monitor crop health and growth in real-time and predict future outcomes based on data such as weather patterns, soil moisture, and nutrient levels. This information can help farmers make informed decisions about irrigation, fertilisation, and other factors that impact crop growth. * Enhanced Productivity and Efficiency: By simulating various scenarios, farmers can identify the most efficient use of resources, such as water and fertilisers, and optimise crop yields [5; 12]. As new data are collected and analysed continuously, farmers can continuously refine their practices, adapting to changing conditions and incorporating the latest insights for ongoing improvements in productivity and efficiency. * Resource optimisation and management: DTs can optimise farmers' use of resources like water, fertilisers, and pesticides by providing real-time data on their application and effectiveness. It also helps in task scheduling and management of farm equipment, including tractors, harvesters, and other agricultural machinery. This can help farmers reduce waste and improve efficiency. * Quality control: It can monitor crop quality and identify potential issues, such as pests or diseases, before they become significant problems. This can help farmers take proactive measures to protect their crops and maintain high-quality yields. * Collaboration: DTs can facilitate collaboration among farmers, researchers, agronomists, and other stakeholders, allowing them to share real-time data and insights. This can accelerate the development of new techniques and technologies in smart crop farming. * Data-Driven Decision-Making: DTs offer farmers a comprehensive perspective of their agricultural operations. This enables data-driven decision-making, as farmers can analyse soil conditions, weather patterns, and crop health data to make informed choices toward improving yields. Continuous data collection and analysis enable better management of agricultural processes, leading to more informed decisions [5]. Figure 3: Digital Twin for Agriculture. The implementation of a DT in agriculture is a powerful step towards more efficient and sustainable practices. This approach offers a holistic overview of the farm, enabling farmers to optimise processes throughout the entire agricultural cycle, from planting to harvesting. The DT also has features for predicting the future and a comprehensive understanding of farm processes. Farmers can more efficiently use resources like water, fertilisers, and pesticides. As a result, this reduces waste and operational costs and addresses the specific needs of crops at different growth stage levels at the right time. These all help in maximise yields. Integrating data from diverse sources and analysing them are significant features of DT.