1. Automated Recognition of Corn Embryos for Selective Breeding
Tanner Holte
Michael Davies
Dr. Baskar Ganapathysubramanian

2. Corn Embryos

3. Selective Breeding Project

4. Machine Learning
Supervised Approach
Data
Model
Prediction

5. Creating Training Set
Point in Polygon
Manual
1,529 sets created

6. Point-in-Polygon Algorithm
Recreate the polygon using data on vertices
Iterate through each pixel in the image
If the pixel falls on the inside of the lines, color the pixel white, else color it black

7. Training Convolutional Autoencoder Iowa State’s CyEnce Cluster
Compares colors of pixels
Generates model based on patterns
Once the model is trained, it can be applied to unlabeled images to generate the label

8. Testing
-3.66%
-4.03%
+49.34%
-8.86%
-4.51%
-8.44%

9. Consequences of the Project
Faster labeling
Labels are generally more consistent and accurate

10. Personal Experience Difficulty of understanding machine learning
Use of a new language, Python
Improving my own adaptability