Asparagus Computer Vision Aided Detection

1. Asparagus Computer Vision Aided Detection By Ryan Moore & GertiTuzi Advisor: Dr. Miller

2. Background Overview Asparagus picking is done manually. Workers sit on a moving vehicle and pick asparagus from the ground. Slow and tiring process A guided machine already in operation but asparagus detection not automated. Opportunity for automation. Computer vision would be very helpful.

3. Main Objectives 1) Develop and integrate a computer vision with the existing machine. 2) Detect if there is an asparagus within the image 3) Determine if the asparagus is greater than or equal to 6 inches. 4) Find the coordinates of the asparagus. 5) Send the coordinates to the machine.

4. Geometrical Setup and Data Extraction Geometrical Setup Data Extraction – Object Labels

5. Location Determination Marker Height Equation (Best Fit Line) World Distance Equation (Best Fit Line) • Data extracted from digital images were plotted against respective world measurements. • Strong relationship between digital and world measurements • Prediction test were run successfully. Average prediction errors were : 3% and 3.5% for base distance and marker height respectively

6. Morphology • Currently used to fatten up the blobs • Major shifts in the laser maker at the final output of the image. • Future implementations of morphology will consist of shape detection. • Thresholding of the image before morphology is very crucial to the success of the output.

8. Online Detector Setup

9. Methods and Characteristics • Light Intensity Average and Std. Deviation • Normalized Energy • Thresholding: C – Determined by Norm. Energy

10. Experiment Setup

11. Video