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Machine Learning Crop Breeding in Precision-Controlled Vertical Farming for a Sustainable Future

Dr Aiden Durrant, University of Aberdeen; Dr Mamatha Thota, University of Lincoln; Professor Derek Stewart, Advanced Plant Growth Centre, The James Hutton Institute; Dr Tanveer Khan, Intelligent Growth Solutions (Industry Advisor)

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Research Aims

In response to the critical challenge of climate change's impacts on food security, this PhD opportunity addresses the imperative for innovative crops and production systems in vertical farming environments. The goal is to develop advanced Artificial Intelligence (AI) methods for Computer Vision (CV) to address plant production and breeding, assessing the impact of AI-driven approaches in controlled vertical farming settings. 

  

The research focuses on creating bespoke datasets through rapid horticultural crop experimentation, utilizing various sensing modalities. Machine learning models will employ multi-modal learning, emphasizing transformer-based computer vision models for feature extraction from diverse input modalities. 

  

Primary objectives include the design and development of novel datasets for plant phenotyping, production, and breeding, optimizing environmental settings for sustainability and yield through machine learning, predicting genomic information for crop selection, and assessing machine learning feasibility for rapid plant phenotyping and selective breeding in anticipated climates. Additionally, you will engage with both technical and non-technical stakeholders, ensuring practical implementation to meet business needs and societal challenges. 

  

Collaboration is key, with partnerships established with academic researchers at the University of Aberdeen, University of Lincoln, and industry leaders at the James Hutton Institute, the Advanced Plant Growth Centre (APGC), and Intelligent Growth Systems (IGS), providing access to state-of-the-art facilities and specialized expertise. 

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