Update README.md

README.md

@@ -19,4 +19,159 @@ metrics:
   - precision
   - recall
   - lin-concordance-correlation-coefficient
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+---
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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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+🌱 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
+
+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
+
+
+For questions or collaboration inquiries, please open an issue or contact the corresponding author.