🌿 AgriVision

The AgriVision is a dedicated group for Research in Agriculture, We utilizes Computer Vision and Deep Learning for solving problems in agricultural field, such as leaf annotation, labelling, detection, segmentation, classification, etc. We provide resources and tools for researchers to explore and apply Computer Vision and Image processing techniques for advancing plant science.

Common classical problems we handle

Segmentation Masks – Generate binary or multi-class masks for plant structures
Annotations – Add and manage precise annotation/bounding box
Image Processing – Perform preprocessing tasks such as cropping, resizing, and filtering
Plant-Type Labeling – Classify and organize images by species (e.g., basil, tomato, etc.)

We utilizes pretrained models, tools like CVAT, and platforms like Roboflow for ease in solving problems.

Mission

To empower the plant pathology community with accessible, reliable, and standardized image processing techniques, enabling faster research, improved dataset quality, and better insights into plant health.

Vision

A collaborative platform that bridges the gap between plant health and computer vision, fostering innovation in agriculture, sustainability, and plant sciences.

Get Involved & Collaborate

We are an open group and welcome collaboration from anyone passionate about AI in agriculture.

Contribute to Datasets: Help us annotate and expand our public datasets.
Develop Models: Share your notebooks and train new models on our data.
Provide Feedback: Use our models and tools, and provide feedback to help us improve.

Feel free to open an issue in our project repositories or start a discussion to share your ideas!

Citing Our Work

If you use any of our datasets, models, or code in your research, please consider citing us:

@misc{agrivision_2025,
  author    = {AgriVision Community},
  title     = {AgriVision: An Open-Source Initiative for AI in Agriculture},
  year      = {2025},
  publisher = {Hugging Face},
  journal   = {Hugging Face repository}
}