Student project: AI-Driven Digital Twins for Orchard Management: Balancing Apple Tree Treatments

An AI-driven framework for precision orchard management and yield optimization.

What you will do

This project aims to utilize Azure Digital Twins to develop a comprehensive digital representation of an orchard, enabling precise estimation of flower cluster development. By integrating structured agronomic data with advanced probabilistic models, the project seeks to enhance decision-making in orchard management through real-time insights and forecasts.

• Literature Review: Conduct a thorough review of existing research on digital twin technology, probabilistic models, and function-structure plant models in agriculture.
• Timeline with Deliverables: Establish a clear timeline with specific deliverables for each phase of the project, including the design, implementation, integration, and evaluation stages.
• Data Collection: Gather and organize structured agronomic data necessary for the project, ensuring data quality and consistency.
• Model Development: Develop and test the selected model (probabilistic) to simulate flower cluster development accurately.
• Digital Twin Design: Define and implement a digital twin model (DTDL) for the entire orchard.
• Model Implementation: Choose either a probabilistic model  to simulate flower cluster development and justify the choice based on data availability and modelling goals.
• Data Integration: Demonstrate how structured agronomic data (e.g., pruning logs, treatment records) would be ingested and linked to the digital twin graph.
• Scenario Simulation: Simulate different pruning or treatment strategies along with weather scenarios and compare their predicted impact on flower cluster development.
• Evaluation and Insights: Evaluate how different modelling strategies influence decision-making in orchard management and provide actionable insights.

What we do for you

• We have a challenging problem where you have a lot of freedom to come up with solutions.
• We have a diverse team of experts both from the biological and the technical sides to supervise and support you.
• You will join the Data Science team of OnePlanet, which employs state of the art knowledge on machine learning for precision agriculture and the frameworks necessary to perform these big data tasks at huge scale.
• You will be able to exchange views and knowledge with the OnePlanet and Imec community of experts and scientists, widening your professional network.
• We can help you to improve your coding skills up to industry standards.
• You have access to our cloud solutions to solve this problem allowing you to process large amount of data within reasonable time.  

Who you are

• Proficiency in Python for data processing and model implementation.
• Knowledge of plant biology and agronomic practices is advantageous.
• Experience with modelling approaches such as probabilistic inference (e.g., Bayesian networks).
• Knowledge on digital twin frameworks, particularly Azure Digital Twins will be a plus.
• Experience with cloud services, especially Azure services like IoT Hub, Azure Functions, and Blob Storage is beneficial.
• Understanding of data integration and ingestion techniques.
• Proficiency in using Git for version control.
• Basic understanding of Agile-Scrum methodologies.
• A passion for contributing positively to environmental and societal challenges.

Interested

Does this position sound like an interesting next step in your career at imec? Don’t hesitate to submit your application by clicking on ‘APPLY NOW’.
Should you have more questions about the job, you can contact jobs@imec.nl.