Research Aims

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Remote sensing data analysis plays a crucial role in advancing sustainable agriculture, offering valuable insights and aiding informed decision-making. In the intersection of artificial intelligence (AI) and machine learning, remote sensing introduces distinctive challenges. While the predominant approach relies on data-driven methods, employing powerful deep neural networks and ever-evolving models, these techniques excel in self-learning and adaptation within data-rich environments. However, the complexity of remote sensing, with intricate environmental interactions and the inherent variability of natural landscapes, poses challenges for these data-driven approaches. 
 
In the realm of remote sensing, precision and context-awareness are crucial, requirements that data-driven methods alone may struggle to fulfil. A key challenge arises from the "black box" nature of AI, which may not align with end-user expectations for specific geographic features. Additionally, remote sensing data is susceptible to various factors like atmospheric conditions, sensor errors, and changes in land use over time. 

To address these challenges, incorporating knowledge-driven methods becomes essential. These methods can significantly enhance the quality and reliability of remote sensing data, bridging the gap between remote sensing and end-user expert knowledge. The key aim of this project will be to explore innovative AI technology that integrates both data-driven and knowledge-driven methods, aiming for more accurate and trustworthy remote sensing results. The research will focus on specific application areas, including smart agriculture and urban agri-food development and planning. The key expertise and skills gained during this project will include AI and machine learning, image processing and spatial analysis, engagement in interdisciplinary collaboration with experts in environmental science and agriculture technology, problem-solving, and understanding ethical considerations related to AI in agriculture. 

This PhD project offers a unique opportunity to contribute to the forefront of AI research while directly impacting sustainable agriculture practices. The student will develop a versatile skill set, combining technical expertise with a holistic understanding of agricultural systems, positioning them as a leader in the intersection of AI and sustainable agriculture. 
 
The requirement for this PhD project is a degree in computer science, mathematics or relevant disciplines. 

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