README.md

🌿 Sick-greens

A deep learning system for plant disease detection and progression tracking built on the PlantVillage dataset. Given a leaf image, the model identifies the disease, classifies its stage (healthy → early → mid → late), estimates days since infection, and computes a treatment urgency score.

What It Does

• Disease Classification — identifies plant disease across 38 classes (e.g. Apple Scab, Tomato Late Blight, Potato Early Blight)
• Stage Classification — maps each disease to one of 4 progression stages:
  • Stage 0 — Healthy
  • Stage 1 — Early
  • Stage 2 — Mid-stage
  • Stage 3 — Late-stage
• Days Estimation — regression head estimates days since infection onset
• Urgency Scoring — outputs a 0–10 urgency score with action recommendations (Low / Moderate / High / Critical)
• Temporal Tracking — tracks disease progression across multiple images over time and plots stage & urgency curves

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Dataset

PlantVillage via TensorFlow Datasets (tfds.load('plant_village'))

• 38 disease/healthy classes across crops including Tomato, Potato, Apple, Grape, Corn, Peach, Pepper, Strawberry, and more
• Capped at ~100–102 images per class for balanced training
• Split: 70% train / 15% validation / 15% test

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Model Architecture

• Backbone: MobileNetV2 (pretrained on ImageNet, fine-tuned)
• Heads:
  • Disease classification head (38-class softmax)
  • Stage classification head (4-class softmax)
  • Days regression head (single neuron)
• Class weights applied to handle imbalance

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Evaluation Metrics

Task	Metric
Disease Classification	Accuracy, Weighted F1
Stage Classification	Accuracy, Weighted F1
Days Estimation	MAE, RMSE

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Tech Stack

• Python, TensorFlow / Keras
• TensorFlow Datasets
• MobileNetV2
• Pandas, NumPy
• Matplotlib, Seaborn, Plotly
• scikit-learn
• OpenCV, Pillow

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Requirements

Install all dependencies via:

pip install -r requirements.txt

Note: If running on Google Colab, the notebook auto-installs all dependencies in its first cell — no manual setup needed.

Key requirements at a glance:

Package	Version
tensorflow	≥ 2.12.0
tensorflow-datasets	≥ 4.9.0
keras	≥ 2.12.0
scikit-learn	≥ 1.2.0
opencv-python-headless	≥ 4.7.0
Pillow	≥ 9.4.0
numpy	≥ 1.23.0
pandas	≥ 1.5.0
matplotlib	≥ 3.6.0
seaborn	≥ 0.12.0
plotly	≥ 5.14.0

See requirements.txt for the full pinned list.

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Getting Started

1. Clone the repo:

   git clone https://github.com/sharr-catalyst/Sick-greens.git
   cd Sick-greens

2. (Optional — local run only) Install dependencies:

   pip install -r requirements.txt

3. Open YourGreensAreSick_v2.ipynb in Google Colab

4. Run all cells: Runtime → Run all

   The notebook will auto-install all dependencies in the first cell.

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📂 Project Structure

Sick-greens/
│
├── models/                         
│   ├── metadata.json                 # Contains training metrics, dataset sizes, and architecture data
│      
│
├── outputs/                          # 📊 Training visualization assets
│   ├── confusion_matrices.png
│   ├── limited_class_distribution.png
│   ├── progression_curve.png
│   ├── sample_images.png
│   ├── sample_predictions.png
│   └── training_history.png
│
├── README.md                         
├── app.py                           
├── requirements.txt                  
└── YourGreensAreSick_v2.ipynb       

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Model

The trained model is hosted on Hugging Face:

To download programmatically:

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="Sharmistha-catalyst/sick-greens-plant-disease",
    filename="final_progression_model.h5"
)

Web App

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Project Report

View Report

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Author

sharr-catalyst — GitHub