# Application Scenarios of Digital Twins for Smart Crop Farming through Cloud-Fog-Edge Infrastructure
## 5 Possible Application Scenarios with Digital Twin
### Scenario 3: Nutrient Management Recommendations  
Nutrient management is crucial in crop farming for several reasons. It ensures the optimal growth of crops by providing them with the right balance of nutrients [46]. Effective nutrient management not only boosts crop yield [47] and quality but also minimises the environmental impact by reducing fertiliser overuse and runoff, which can lead to soil degradation and water pollution [46].  
Various factors are considered in nutrient management for crops, including soil conditions, crop nutrient requirements, deficiency symptoms, previous crop information, and environmental conditions [48]. Conventional smart agriculture [49] employs technologies such as IoT sensors, drones, and satellite imagery to gather data for informed fertiliser application, improving crop growth, and minimising waste. However, these systems face challenges in terms of connectivity and internet access in remote agricultural areas, impacting the real-time monitoring capabilities of these technologies. Another problem is scalability when new services need to be introduced to the systems.  
Generally, the conventional smart agriculture method of nutrient application involves uniform practices across fields. In contrast, the proposed architecture customises nutrient guidance based on the unique needs of each field or zone level, as identified through real-time data processing. This is because the DT supports farm level, field level, and zone level granularity. The adaptability of the DT is evident in its responsiveness to dynamic environmental factors. In instances of adverse weather conditions or changes in soil composition, the system can promptly adjust nutrient recommendations to accommodate the evolving needs of the crops. This agility ensures that nutrient applications are modified with the current state of the field, maximising the effectiveness of the entire nutrient management process.  
Additionally, the system facilitates seamless communication between various stakeholders involved in nutrient management. Farmers receive clear and actionable recommendations through user-friendly interfaces, improving their ability to implement precise nutrient applications. The recommendation system also integrates with other components of the agricultural architecture, such as weather forecasting, to provide comprehensive insights for decision-making. Another key feature of the nutrient management recommendation system using DT architecture is its continuous learning capability. Over time, the system refines its recommendations based on historical data, crop responses, and evolving soil conditions. This iterative process ensures that nutrient management strategies are continually optimised, leading to improved crop health and overall agricultural productivity.  
Furthermore, integrating cloud, fog, edge computing, agents, and microservices transforms nutrient management to the next level. The fog layer instantly analyses real-time data collected by edge devices, overcoming connectivity limitations. Microservices provide flexible and scalable solutions, while the cloud layer enhances computational capabilities with machine learning. Autonomous agents streamline decision-making, making nutrient management more efficient and reducing the need for continuous human intervention. The whole approach of the DT architecture ensures a seamless and optimised nutrient management process in agriculture.