Infected Tomato Leaf - Vein Segmentation

The Infected Tomato Leaf - Vein Segmentation challenge was featured in the 2024 Machine Learning Marathon (MLM24). This UW-Madison-originated Kaggle competition focuses on segmenting primary and secondary vein structures from tomato leaf images at varying stages of disease progression.

Challenge design

• Task: Segment leaf vein structures in images of infected tomato leaves, distinguishing between primary and secondary veins across different disease stages.
• Domain: Agriculture and plant pathology – automated vein segmentation can help researchers study how diseases affect leaf vascular structure.
• Methods: Image segmentation approaches, commonly using architectures like U-Net.

Links

• Kaggle challenge: UW-Madison Infected Tomato Leaf - Vein Segmentation

Related resources

• Data: LVD2021 Leaf Vein Dataset: A related dataset for leaf vein analysis that can complement this challenge.
• Model: U-Net: Learn about U-Net, a popular architecture for biomedical and agricultural image segmentation tasks.

Questions

If you have any lingering questions about this project, please feel free to post to the Nexus Q&A on GitHub. We will improve materials on this website as additional questions come in.