This project implements a deep learning pipeline for predicting crop yields using satellite remote sensing data. It integrates convolutional and recurrent neural networks (CNN + LSTM) with Gaussian process modeling to generate spatial yield estimates based on temporal patterns in multispectral imagery. The repository includes data preprocessing scripts, model training routines, and visualization tools, with workflows built around data from Google Earth Engine and other open remote sensing sources.
Crop yield Prediction
Crop yield Prediction
Crop Yield Prediction with Deep Learning uses advanced algorithms to predict crop yields based on various data inputs.
Organization
GitHub user
Deployment Model
Cloud
Agricultural Production Step
Farm management
End User
Farmers, Advisors, Developers
Used for
Crops, Plants
TRL
5
Community Engagement
Very high
Number of Forks
208
Country
Universal
Disclamer
While every effort has been made to accurately record the licensing status and all other information presented, this catalogue is provided for informational purposes only. Users are strongly encouraged to verify these details—particularly the license terms and conditions—before adopting or integrating any solution, and to take special care when no explicit license is listed in the repository. The partners of the OpenAgri project make no representations or warranties of any kind, express or implied about the completeness, accuracy, reliability or suitability with respect to the interactive catalogue or the information, products, services or related graphics contained in the catalogue for any purpose. Any reliance you place on such material is therefore strictly at your own risk.