Precision Agriculture Revolution: Integrating Digital Twins and Advanced Crop Recommendation for Optimal Yield
This addresses the need for more efficient and sustainable farming practices for farmers and agricultural stakeholders, though it appears incremental by combining existing technologies.
The paper tackles the problem of optimizing agricultural production by integrating digital twins with real-time data from weather APIs, GPS, and soil sensors to provide precise crop growth forecasts and recommendations, aiming to improve water and pesticide management.
With the help of a digital twin structure, Agriculture 4.0 technologies like weather APIs (Application programming interface), GPS (Global Positioning System) modules, and NPK (Nitrogen, Phosphorus and Potassium) soil sensors and machine learning recommendation models, we seek to revolutionize agricultural production through this concept. In addition to providing precise crop growth forecasts, the combination of real-time data on soil composition, meteorological dynamics, and geographic coordinates aims to support crop recommendation models and simulate predictive scenarios for improved water and pesticide management.