.png){kind=icon}
.png){kind=icon}
Return
Digital twin-based winter wheat growth simulation and optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.fcr.2025.109953.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Context: Wheat is an essential food crop, and precise simulations and feedback mechanisms during its growth crucial for advancing the intelligence of wheat production systems. Objective: (1) Building Virtual Real Interaction of Data. (2) Constructing twin simulation of winter wheat process. (3) Constructing a feedback control technology for winter wheat growth process based on digital Methods: This study utilized digital twin technology integrated with crop growth models to optimize monitoring and management processes through sequential experiments simulating wheat growth. By utilizing Internet Things (IoT) devices and drones, the integration of wheat growth data and the creation of a digital twin ronment were achieved. The integration of digital twin technology with crop growth models allowed simulation and intelligent management of wheat growth processes. Results: The wheat growth digital twin model, developed based on the DSSAT framework, can effectively simulate wheat growth. Model calibration and dynamic parameter adjustments resulted in an R2 of 0.98 simulation accuracy of LAI (leaf area index) and AGB (above-ground biomass). Simulation errors for flowering and maturity stages were 0.6 days and 1.1 days, respectively, while yield simulation errors remained below hm2. Additionally, optimal management strategies were proposed for various winter wheat varieties. Conclusions: Digital twin technology enables precise simulation of wheat growth, supports effective feedback regulation, and significantly enhances the intelligence of wheat production.
Keywords:
Digital twin
Crop growth model
Process simulation
Winter wheat
UAV remote sensing
Journal
IF:
6.4
Papers:
6.4K
Citations:
2.8W