From sensors to smart yield forecasting
Digital Twin technology creates a digital replica of the greenhouse environment, integrating data from sensors, climate control systems, and robots. By combining this data, crop growth and harvest timings can be accurately predicted and optimized. This leads to more efficient use of resources and higher yields.
Background Use Case 11
A digital greenhouse environment for better insights
The Digital Twin enables the integration of various data streams — such as temperature, humidity, crop health, and soil quality — into a single platform. By combining this data with advanced physiological models and decision-making algorithms, the system predicts how crops will develop under specific conditions. This provides growers with real-time insights into the growth process and allows for targeted adjustments.
Since the launch of NXTGEN, Plantfellow has developed an MVP (Minimum Viable Product) of a Digital Twin, integrating unique solutions from the collaborating partners. Wageningen University & Research provides physiological models that offer insights into optimal growing conditions, while Hoogendoorn | LetsGrow contributes climate control algorithms to minimize energy and water usage. By leveraging sensors and AI models, the system can automatically determine irrigation strategies and adjust greenhouse climate conditions for maximum efficiency.
"With the Digital Twin, we can accurately predict crop growth and use resources more efficiently."
Public summaries Use Case 11
Data to Robocrops datahub
Contribution to the ecosystem and the sector
Improved yields through data-driven management
The Digital Twin strengthens the greenhouse horticulture sector by providing growers with deeper insights into the crop growth process. As a result, resources such as water, energy, and crop protection agents are used more efficiently. The technology enables quicker responses to changing conditions in the greenhouse, reducing harvest losses and lowering production costs. By combining climate data with plant models, collaboration between different systems is improved, leading to a more efficient production process.
Deliverables Use Case 11
Accurate growth forecasts and more efficient use of resources
Development of a digital greenhouse environment for real-time monitoring
Integration of sensors and climate data into a single platform
Improved predictions of crop growth and harvest timing
Optimization of resources such as water, fertilizer, and energy
Automatic adjustment of climate conditions based on AI-driven models
Added value for Human Capital
New skills in data processing and AI applications
The implementation of Digital Twin technology requires new skills in data processing, systems management, and AI modeling. Growers and technology companies will need to upskill in interpreting complex data and configuring automated processes. Additional expertise in machine learning and sensor management is essential to ensure the Digital Twin operates effectively and to empower growers to make independent adjustments.
Furthermore, the use of physiological models and climate algorithms demands new insights into plant growth and energy management. The system requires training in working with data-driven models and calibrating sensors to achieve accurate results. This project promotes the development of technical knowledge and enhances the sector's capacity for innovation.
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