Crop Yield Prediction
Deep learning models for crop yield prediction using remote sensing data, with CNN/LSTM and Gaussian Process approaches.
What is Crop Yield Prediction?
Crop Yield Prediction with Deep Learning is a research implementation for predicting agricultural yields using satellite imagery and machine learning. It provides tools for downloading remote sensing data from Google Earth Engine, preprocessing agricultural datasets, and training deep learning models including CNN/LSTM architectures and Gaussian Process regression. The project addresses food security challenges by enabling data-driven yield forecasting.
Researchers and data scientists working in agricultural technology, computational sustainability, and remote sensing applications. It's particularly relevant for those developing predictive models for crop monitoring and yield forecasting.
This implementation offers a complete pipeline from data acquisition to model training specifically designed for agricultural applications, with proven performance demonstrated through research awards. It provides efficient methods for handling large-scale satellite imagery and implements both deep learning and probabilistic modeling approaches.
Overview
Crop Yield Prediction with Deep Learning
Use Cases
Best For
- Research projects in agricultural yield prediction using satellite data
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
