Research

• Smart Horticulture: Developing and deploying innovative sensing tools, computer vision techniques, robust data mining algorithms, high precision smart control systems, robotics and automation methods to minimize the labor cost, management implications and enhance production resilience. Possible applications include fruit, vegetable, nursery, landscape and greenhouse production systems.

 

• Controlled Environment Agriculture: Selection, development and integration of multimodal sensing suite to collect the data related to spatial, temporal and compositional heterogeneity of microclimate and plant stress signals. The extracted data can be used to study the complex interaction of microclimate, growth media, and outdoor environmental conditions and their impact on plant health for early detection and intervention to achieve better outcomes in crop production.

 

• AI-enabled decision Analysis: Robust, data-centric machine learning/deep learning pipelines to systematically analyze the heterogeneous multi-modal, multi-temporal, multi-resolution and multi-scale big data for unleashing the horticultural/agricultural production potentials.