The notebooks available on Eden library show our modest contribution to the Deep Learning community interested in Precision Agriculture problems. They synthesize some of the most common agricultural problems and solutions we have addressed in the past years. It is important to note that the notebooks have not been finely tuned for the best possible results; they simply present ways to use the datasets for basic tasks. If you want to know more about the use of notebooks as a development tool, please check out this link: https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html
- Keras
- Tensorflow
- OpenCV
- Scikit-learn
- Auto-sklearn
- AutoKeras
- Pytorch
Definitely yes! We will be updating the notebook section with new notebooks implementing novel (and traditional) techniques related to Deep Learning and Image Processing that can be directly applied to datasets extracted from the Eden library (and similar ones). Click on the next image for an overview of the available notebooks:
Certainly. The notebooks published on the Eden platform are open for extension with additional information, bibliography, and new pieces of source code which could improve the readability of the explained techniques. If you have any suggestions for improving them, or any doubts to be clarified, do not hesitate to open an issue in the Github repo. We will reply to you as soon as possible.
All notebooks have a quite similar structure:
- Instructions for running - Take care of the dependencies!
- Theoretical background and agricultural context
- Code ready for execution
- Possible extensions and modifications of the provided notebooks
- Recommended Bibliography
