| --- |
| language: en |
| license: mit |
| tags: |
| - computer-vision |
| - image-segmentation |
| - plant-disease |
| - agricultural-ai |
| - foundation-model |
| - sam |
| - yolo |
| - coffee |
| - rust-disease |
| datasets: |
| - coffee-leaf-rust-severity |
| metrics: |
| - iou |
| - dice |
| - precision |
| - recall |
| - lin-concordance-correlation-coefficient |
| --- |
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| Foundation Model–Assisted Coffee Leaf Rust Severity Estimation |
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| This repository accompanies the manuscript: |
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| Foundation model–assisted segmentation enables robust field-based severity estimation of coffee leaf rust |
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| This project presents a fully reproducible computer vision pipeline for quantitative estimation of coffee leaf rust (Hemileia vastatrix) severity under heterogeneous field conditions. The framework integrates object detection, lesion segmentation, pixel-based severity quantification, and concordance analysis grounded in phytopathometry principles. |
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| The study compares classical image processing, supervised deep learning, and foundation segmentation models for lesion detection, and evaluates agreement with gold-standard pixel-level annotations using Lin’s Concordance Correlation Coefficient (LCCC). |
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| Data Card Author(s) |
| Mary Paz Romero Benavides, Universidade Federal de Viçosa: Owner / Manager |
| Emerson M. Del Ponte, Universidade Federal de Viçosa: Contributor |
| Waldênia de Melo Moura, EPAMIG: Contributor |
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| 🌱 Project Overview |
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| The methodological workflow consists of: |
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| Leaf Detection – YOLOv8 trained using model-assisted annotations |
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| Leaf Extraction – Detection-guided segmentation |
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| Lesion Segmentation – Comparison of five approaches: |
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| ImageJ thresholding |
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| pliman (R package) |
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| DeepLabV3+ |
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| Fine-tuned SAM2 (SAM_CLR) |
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| Zero-shot SAM3 |
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| Severity Estimation – Pixel-based calculation: |
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| S (%) = Diseased Area / Leaf Area × 100 |
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| Agreement Analysis – Lin’s Concordance Correlation Coefficient between predicted and reference severity |
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| 📊 Dataset Summary |
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| The full dataset comprises: |
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| 1,285 field-acquired coffee leaf images |
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| 606 curated pixel-level rust lesion masks |
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| 100 independent evaluation masks |
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| Roboflow dataset links: |
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| CLR_SAM_dataset: |
| https://universe.roboflow.com/clr-zky50/sam_clr/dataset/1 |
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| DL506: |
| https://universe.roboflow.com/clr-zky50/dl506/dataset/1 |
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| GoldenStandard: |
| https://universe.roboflow.com/clr-zky50/imgtest-fvn9j/dataset/1 |
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| 📂 Repository Structure |
| 📁 01_models |
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| Contains documentation describing the trained models used in this study. |
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| ⚠️ Due to GitHub file size limitations, model weights are hosted on Hugging Face. |
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| Models include: |
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| YOLOv8 leaf detector |
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| Fine-tuned SAM2 (SAM_CLR) |
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| DeepLabV3+ |
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| Configuration used for zero-shot SAM3 inference |
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| 📁 02_binary_images |
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| Contains validation binary masks (PNG format) corresponding to segmentation outputs from each evaluated method. |
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| These masks were used to compute: |
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| Intersection over Union (IoU) |
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| Dice coefficient |
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| Pixel accuracy |
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| Precision |
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| Recall |
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| Disease severity (%) |
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| Lin’s Concordance Correlation Coefficient (LCCC) |
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| Binary mask format: |
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| 0 → background |
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| 255 → rust lesion |
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| This folder enables independent verification of segmentation performance and severity calculations. |
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| 📁 03_analysis |
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| Contains R scripts used to: |
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| Compute severity metrics |
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| Perform agreement and concordance analysis |
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| Generate all figures included in the manuscript |
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| Main R dependencies: |
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| tidyverse |
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| epiR |
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| lme4 |
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| ggplot2 |
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| This folder reproduces the statistical analysis pipeline described in the paper. |
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| 🔬 Reproducibility |
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| This repository provides: |
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| Validation segmentation outputs |
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| Statistical analysis scripts |
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| Model documentation |
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| External links to trained weights |
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| Together, these components allow full reproducibility of segmentation metrics and severity agreement results reported in the manuscript. |
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| 🤖 Model Hosting |
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| All trained model weights are hosted on Hugging Face: |
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| 👉 https://huggingface.co/MaryPazRB/Paper_CLR_CV |
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| This ensures accessibility without exceeding GitHub file size limitations. |
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| 📜 License |
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| Code: MIT License |
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| Binary masks and annotations: CC-BY 4.0 |
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| For questions or collaboration inquiries, please open an issue or contact the corresponding author. |
