# Application Scenarios of Digital Twins for Smart Crop Farming through Cloud-Fog-Edge Infrastructure
## 5 Possible Application Scenarios with Digital Twin
### Scenario 2: Early Alerts on Fields  
Vegetation indexes used in earth observations are related to crop stress, such as drought or disease [44; 45]. The field management system in the outlined architecture would prioriti an inspection made in a specific area in response to earth observations processed through the cloud. This guidance-based approach would rapidly reduce the time spent inspecting the field for stress symptoms, increasing efficiency and helping maintain crop health. This is illustrated in Figure 6, where the traditional approach of crop inspection requires walking through each field as highlighted on the left image with red lines; the guidance-based system reduces this need and can direct a farmer straight to the areas of risk. Whilst in the field, any map-based information could be updated through the static fog, and the farmer could record a diagnostic for the stress (e.g., the presence of a disease). In addition, whilst in areas of poor connectivity, sensors would benefit from the presence of a passing smartphone to create an ad-hoc connection to upload data. At the point of updating the system to diagnose a crop disease, the architecture will be able to respond to this new information, check the weather forecast, and, if weather conditions are favourable could prioritise new actions of fungicide applications (part of the recommendation system of the architecture).  
Additionally, the system could automate the shutoff of irrigation of the field/neighbouring fields of the same crop. This automation will (1) help reduce the spread of disease from water droplets and (2) provide the most time for the area to dry, which is necessary for fungicide applications. The dynamic nature of the architecture will provide the best outcome for a rapid response. In this scenario, the system minimises the time spent by the farmer inspecting fields, collecting relevant information (i.e., weather forecast), deciding on the best course of action and allowing more time for the farmer to respond to the crop disease threat, preventing potential yield reduction from crop disease spread. Without the system, most of the day could have been spent inspecting each field for symptoms of stress.  
When there is a deviation from the farm management plan, the DT field feature will be able to simulate the impacts of the change and provide advice on future management decisions. If an event occurs in a field or management deviates from the management plan, the digital twin field will be able to simulate the impacts of the changes and advise alternative management decisions. For example, if the drilling of a field is delayed due to adverse weather events, the DT will recommend increasing the seed rate, helping to negate  
Figure 6: Crop Inspection Traditional Approach (**left**) vs. Modern Approach (**right**).  
any reduced tillering. Furthermore, the DT model will be able to apply these variations, along with growth modelling and external data such as weather forecasting, to adapt the expected timing of future management actions, e.g., nitrogen applications. The DT will be able to suggest amendments to agrochemical applications in response to events occurring in the field/farm while also helping to automate certain processes. In addition to this, if yield reduction is predicted for a field in the DT, the system may suggest reducing future nitrogen applications in that field, which will help improve nitrogen use efficiency, reduce environmental damage, and maximise profits.