- Developing hybrid modeling, RS and machine learning on agroforestry, food security and nutrition
- Developing agroforestry hybrid modeling (Hi-sAFe_ML)
- Hybridizing CMs, RS, and ML based cloud computing for yield gap analysis
- Developing hybrid process-based machine learning agroforestry decision support tool (DST)
- AI agent and LLM in digital agriculture (text analysis, agricultural reports, farmer interviews)
- Running spatial hybrid models on HPC cluster
- Cloud computing using GCP, Azure and Amazon
- Developping microclimate model and weather forecasting by integrating LSTM-AI Agent approach with proximal sensing (IOT, sensors...)
- Integrating proximal sensing (IOT, thermal cameras, EC weather stations, soil sensors, ..) with ML, AI Agent, PBM, RS for high quality and finer resolution decisions
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