LeafBox: A Leaf Disease Toolbox
A toolbox for training researchers and students in image and data analysis techniques in predicting plant diseases.
LeafBox is an open-source toolbox targeted for plant disease diagnosis and prediction. The LeafBox project was started at the University of Cagliari by Dr. Francesca Maridina Malloci and prof. Gianni Fenu, and is under active development—new functionality and tutorials are added regularly.
Our mission is to provide a flexible, modular and ready-to-use toolbox for developing artificial intelligence models, providing field-collected data and reusable models in order to (i) facilitate reproducibility results with the research community (ii) share representative datasets collected directly in the field (iii) train more robust AI models to real-world conditions.
If you have questions, bug reports, suggestions for new features, etc. please post to our Github issue page.
We encourage community contribution. Please contact us by writing to: francescam [dot] malloci [at] unica [dot] it
What is included:
Our datasets are collected directly in the field, in order to collect sufficiently representative data for the development of AI models.
An open source python toolbox aimed at developing AI models for the prediction of leaf diseases. An easy-to-use, modular and reusable tool, which allows you to combine different analysis techniques, in order to simplify the analysis processes, and facilitate the reproducibility and sharing of experimental results with the scientific community.
Download and run several notebooks useful for learning the services displayed by LeafBox.
Publications
If you use LeafBox, please cite the appropriate LeafBox publication below.
Fenu, G.; Malloci, F.M. DiaMOS Plant: A Dataset for Diagnosis and Monitoring Plant Disease. Agronomy 2021, 11, 2107. https://doi.org/10.3390/agronomy11112107
Fenu, G.; Malloci, F.M. Using Multi-Output Learning to Diagnose Plant Disease and Stress Severity. Complexity 2021,Article ID 6663442. https://doi.org/10.1155/2021/6663442