This talk covers how we built a predictive model for plant disease in Cannabis. We cover methodology, model training, model evaluation, deployment and ideas for improving the model. Check out the deployed model at https://chronicsickness.com

1. Identifying sick Cannabis
with AI
DefCon 2018

2. Who am I?
Software Engineer
Organizer at Kaggle NYC

3. Background
Artificial intelligence is becoming
more pervasive and accessible.
AI in Radiology
Skin Cancer predictive model

4. The problem
Cannabis can become sick
1. Hobbyists
2. Industrial players
3. Requires expert

5. Goal:
Software that tells you if cannabis
is sick
By using your phone

6. How do we make a model?

7. Machine
learning
process
● Gather data
● Train on examples
● Deploy
● Iterate

8. Step 1: Gather data
Sick images
● r/cannabis
● Google Image search
○ Cannabis + sick
○ Cannabis + diagnosis
○ Cannabis + healthy
● growweedeasy.com
● google-images-download

9. Step 1: Gather data
Healthy images
● easier

10. Step 1: Gather data
Other images
● Macro text
● Lookalikes
● flowers-recognition
● reddit-memes
● Caltech object dataset

11. Step 1: Gather data
Dataset
● 3000 images
○ 1000 sick
○ 1000 healthy
○ 1000 other
● Small dataset for ML

12. Step 2: Train model
Deep Neural Network
● DLwP Section 5.3
● Data augmentation
● Transfer learning

13. Step 2: Train model
Amazon Web Services
Sagemaker
● One-click python notebooks
● 1 GPU $1.50 / hour
● 4 GPU $10 / hour
● Upload dataset zip

14. Step 2: Train model
Transfer Learning
● ImageNet winner
● Trains faster

15. Step 2: Train model
ResNet50
● 50 layers
● Residual architecture
● 60% validation accuracy

16. Step 2: Train model
VGG16
● 16 layers
● Visual Geometry Group, Oxford
● 80% validation accuracy

17. Step 3: Deploy to cloud
● Amazon EC2
● No GPU
● Flask
● Keras
● simple-keras-rest-api

18. Step 4: User Interface
chronicsickness.com

19. Step 5: Iterate
● Crowdsource
● 95%+ Accuracy
● 50,000 images

20. Future work
● 40 plant diseases
● 800 strains

21. Thanks!
chronicsickness.com
harrymoreno.com