This repository provides a lightweight, production-ready MobileNetV2 model trained on the PlantVillage augmented dataset for multi-class plant disease classification (38 classes).

The model is optimized for:

✔ Real-time inference ✔ Low-compute environments (mobile/edge devices) ✔ Web apps (Streamlit) ✔ Integration with farm-tech solutions

🧠 Model Summary

Architecture: MobileNetV2
Dataset: New Plant Diseases Dataset (Augmented) – PlantVillage
Total Classes: 38
Training Images: ~87,000 images (balanced across 38 classes)
Validation Accuracy: 95%
Loss: Cross Entropy
Optimizer: Adam (LR = 0.001)
Input Size: 224 × 224
Format: PyTorch .pth

🌐 Live Demo

Try the fully deployed, interactive plant-disease detection app:

👉 Streamlit App: https://agriguard271005.streamlit.app/

Upload a leaf image → get: ✔ Disease name ✔ Confidence score ✔ Health status ✔ Severity level ✔ Recommended action

🤗 HuggingFace Model

The pretrained .pth model and configuration files are hosted here:

👉 HuggingFace Model Repo: https://huggingface.co/Daksh159/plant-disease-mobilenetv2

Available assets:

mobilenetv2_plant.pth
class_names.json
README.md (this file)

🏗 Model Architecture (Training Setup)

Backbone: MobileNetV2

python

model = models.mobilenet_v2(pretrained=True)

for p in model.features.parameters():
    p.requires_grad = False  # freeze feature extractor

model.classifier[1] = nn.Sequential(
    nn.Dropout(0.2),
    nn.Linear(model.classifier[1].in_features, 38)
)

Training Loop

10 epochs
Augmented inputs
Adam optimizer
Early stopping possible

📊 Supported Classes

Includes diseases & healthy leaves of:

Apple
Blueberry
Cherry
Corn
Grape
Peach
Pepper
Potato
Raspberry
Soybean
Squash
Strawberry
Tomato
Orange

(Full list of 38 classes included in class_names.json)

🖼 Inference Example

python

import torch
from torchvision import models, transforms
from PIL import Image

model = ...   # load your mobilenetv2_plant.pth
model.eval()

tf = transforms.Compose([
    transforms.Resize((224,224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485,0.456,0.406],
                         [0.229,0.224,0.225])
])

img = Image.open("leaf.jpg").convert("RGB")
x = tf(img).unsqueeze(0)

with torch.no_grad():
    preds = model(x)
    probs = torch.softmax(preds, dim=1)[0]
    index = probs.argmax().item()

print("Predicted Class:", CLASS_NAMES[index])
print("Confidence:", float(probs[index]))

🔌 API / Integration Use Cases

This model can be integrated into:

🌱 Farming mobile apps 🌾 Agri-tech dashboards 💡 Real-time disease monitoring systems 📸 Drone-based crop scanning pipelines 🌍 IoT/edge deployment for field devices

🖥 Deployment

Available Deployments

Platform	Status	Link
HuggingFace	✔ Model Hosted	https://huggingface.co/Daksh159/plant-disease-mobilenetv2
Streamlit Cloud	✔ Web App Live	https://agriguard271005.streamlit.app/

🏁 Future Improvements

Planned enhancements:

Grad-CAM heatmap explainability
Faster ONNX / TensorRT export
Support for multiple leaves in a single image
Region-based crop disease localization
Larger dataset fine-tuning

🙌 Acknowledgements

PlantVillage Dataset
PyTorch
Kaggle
Streamlit Cloud
HuggingFace 🤗

itzsherlockz/plant-disease-mobilenetv2

🌿 Plant Disease Classification – MobileNetV2

High-Accuracy Deep Learning Model for Crop Disease Detection

🚀 Overview