Plant Disease Detection - ResNet50
A pre-trained ResNet50 model for detecting plant diseases from leaf images, optimized for FastAPI deployment.
Model Description
- Architecture: ResNet50
- Parameters: 23.6 million
- Model Size: 91 MB
- Accuracy: 95%+ on PlantVillage test set
- Classes: 38 plant disease categories
- License: Apache 2.0
Supported Crops & Diseases (38 Classes)
Maize (Corn) - 4 diseases ✅
- Cercospora leaf spot (Gray leaf spot)
- Common rust
- Northern Leaf Blight
- Healthy
Tomato - 10 diseases ✅
- Bacterial spot
- Early blight
- Late blight
- Leaf Mold
- Septoria leaf spot
- Spider mites
- Target Spot
- Yellow Leaf Curl Virus
- Mosaic virus
- Healthy
Other Supported Crops
- Apple: 4 diseases (scab, black rot, cedar rust, healthy)
- Grape: 4 diseases (black rot, esca, leaf blight, healthy)
- Potato: 3 diseases (early blight, late blight, healthy)
- Pepper: 2 classes (bacterial spot, healthy)
- Cherry, Peach, Strawberry, Orange, etc.
Quick Start
from transformers import AutoModelForImageClassification, AutoImageProcessor
from PIL import Image
import torch
# Load model
model = AutoModelForImageClassification.from_pretrained("mesabo/agri-plant-disease-resnet50")
processor = AutoImageProcessor.from_pretrained("mesabo/agri-plant-disease-resnet50")
# Inference
image = Image.open("plant_leaf.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
predicted_idx = probs.argmax(-1).item()
confidence = probs[0][predicted_idx].item()
print(f"Disease: {model.config.id2label[predicted_idx]}")
print(f"Confidence: {confidence * 100:.2f}%")
FastAPI Integration
from fastapi import FastAPI, File, UploadFile
from transformers import AutoModelForImageClassification, AutoImageProcessor
from PIL import Image
import torch
import io
app = FastAPI()
# Load model at startup
model = AutoModelForImageClassification.from_pretrained("mesabo/agri-plant-disease-resnet50")
processor = AutoImageProcessor.from_pretrained("mesabo/agri-plant-disease-resnet50")
model.eval()
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
image = Image.open(io.BytesIO(await file.read())).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
predicted_idx = probs.argmax(-1).item()
confidence = probs[0][predicted_idx].item()
return {
"disease": model.config.id2label[predicted_idx],
"confidence": round(confidence * 100, 2),
"status": "success"
}
Performance
| Metric | Value |
|---|---|
| Accuracy | 95%+ |
| Inference Time | < 100ms (CPU) |
| Memory Usage | ~400 MB |
| Input Size | 224x224 RGB |
West African Agriculture Note
This model currently supports Maize and Tomato which are important crops in West Africa.
Not yet supported:
- Cassava (most important staple - coming soon)
- Cashew (major cash crop)
- Cocoa (critical for Ghana, Ivory Coast)
For cassava/cashew support, we recommend fine-tuning on the CCMT Ghana dataset.
Training Data
- Dataset: PlantVillage
- Images: 54,305
- Augmentation: Yes
- Resolution: 224x224
Requirements
pip install transformers torch pillow
Citation
@misc{agri-plant-disease-resnet50,
author = {mesabo},
title = {Plant Disease Detection - ResNet50},
year = {2024},
publisher = {Hugging Face},
url = {https://huggingface.co/mesabo/agri-plant-disease-resnet50}
}
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- mesabo/agri-chat-multilingual - Multilingual agricultural chatbot (7 languages)
License
Apache 2.0 - Commercial use allowed