Project-AgML/AgML
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
This tool helps agricultural scientists and researchers efficiently develop machine learning models for tasks like disease detection, weed identification, and fruit counting. It provides easy access to a wide range of public agricultural image datasets, which can then be prepared and used to train and evaluate deep learning models. This is ideal for those working on computer vision applications in agriculture.
265 stars.
Use this if you need pre-curated agricultural image datasets and a streamlined way to prepare them for training and evaluating machine learning models specific to crop health, pest detection, or yield estimation.
Not ideal if your agricultural data is primarily non-visual (e.g., sensor data, soil analysis) or if you are not working with machine learning models.
Stars
265
Forks
41
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
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