We plan to investigate a wide range of AI research topics benefiting agricultural operations. The tentative topics to be studied (collaborating with our collaborators in AgAID) include:
* AI method for GNC; * AI method for scheduling; * AI method for manipulation,
* AI method for remote sensing in precision farming; * AI method for pesticide residual analysis.
1. AI method for GNC
We aim to investigate a new AI method to enhance the optimality of the path/trajectory of field robots, which is also adaptive to field environments and variations.
2. AI method for scheduling
We aim to investigate a new AI method to enable efficient scheduling of a team of agricultural robots in field operations.
3. AI method for manipulation
We aim to develop robot manipulation techniques for evaluating, picking and pruning fruits and vegetables. The techniques will use the latest results in visual foundation models, diffusion-based policies and imitation learning.
4. AI method for remote sensing in precision farming
We aim to build an AI foundation model for precision agriculture using multi-modal data. The foundation model can be adapted to various precision agriculture tasks.
5. AI method for pesticide residual analysis
We aim to investigate a new AI enabled method to detect pesticide residue of interest on plant leaf surface and predict pesticide loss due to rainfall.