Sharmistha-catalyst/sick-greens-plant-disease

🌿 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

---

Evaluation Results

Task	Metric	Score
Disease Classification	Accuracy	82.82%
Disease Classification	Weighted F1	0.8242
Stage Classification	Accuracy	80.93%
Stage Classification	Weighted F1	0.7998
Days Estimation	MAE	2.86 days
Days Estimation	RMSE	4.16 days

---

Usage

from huggingface_hub import hf_hub_download
from tensorflow import keras
import numpy as np
import json

# Download model and metadata
model_path = hf_hub_download(
    repo_id="Sharmistha-catalyst/sick-greens-plant-disease",
    filename="final_progression_model.h5"
)
metadata_path = hf_hub_download(
    repo_id="Sharmistha-catalyst/sick-greens-plant-disease",
    filename="metadata.json"
)

# Load model and metadata
model = keras.models.load_model(model_path)
with open(metadata_path) as f:
    metadata = json.load(f)

# Preprocess and predict
image = np.expand_dims(your_image_array, axis=0)  # (1, 224, 224, 3)
image = keras.applications.mobilenet_v2.preprocess_input(image)

disease_pred, stage_pred, days_pred = model.predict(image)

---

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

---

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

---

Tech Stack

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

---

Links

• 📓 Colab Notebook
• 💻 GitHub Repository
• 📄 Project Report

---

Author

Sharmistha-catalyst — Hugging Face · GitHub