A5064695038- Smart Agriculture and AI
- Child Nutrition and Water Access
- Geochemistry and Geologic Mapping
- Remote Sensing and Land Use
- Remote Sensing in Agriculture
- Traffic Prediction and Management Techniques
- Soil Geostatistics and Mapping
- Aquaculture Nutrition and Growth
- Advanced Computational Techniques and Applications
- Data Stream Mining Techniques
- Remote Sensing and LiDAR Applications
- Fish biology, ecology, and behavior
Cornell University
2022
Climate change is posing new challenges to crop-related concerns, including food insecurity, supply stability, and economic planning. Accurately predicting crop yields crucial for addressing these challenges. However, this prediction task exceptionally complicated since depend on numerous factors such as weather, land surface, soil quality, well their interactions. In recent years, machine learning models have been successfully applied in domain. either restrict tasks a relatively small...
Big data and the rapid development of artificial intelligence (AI) provide unprecedented opportunities to enhance our understanding global carbon cycle other biogeochemical processes. However, retrieving mechanistic knowledge from big remains a challenge. Here, we develop Biogeochemistry-Informed Neural Network (BINN) that seamlessly integrates vectorized process-based soil model (i.e., Community Land Model version 5, CLM5) into neural network (NN) structure examine mechanisms governing...
Climate change is posing new challenges to crop-related concerns including food insecurity, supply stability and economic planning. As one of the central challenges, crop yield prediction has become a pressing task in machine learning field. Despite its importance, exceptionally complicated since yields depend on various factors such as weather, land surface, soil quality well their interactions. In recent years, models have been successfully applied this domain. However, these either...
Monitoring vegetation productivity at extremely fine resolutions is valuable for real-world agricultural applications, such as detecting crop stress and providing early warning of food insecurity. Solar-Induced Chlorophyll Fluorescence (SIF) provides a promising way to directly measure plant from space. However, satellite SIF observations are only available coarse spatial resolution, making it impossible monitor how individual types or farms doing. This poses challenging coarsely-supervised...