Seeing the Future - AI Vision in the Food Industry
Artificial Intelligence has become a buzzword — but what does it really mean for food manufacturing?
In this episode, Winston talks with Kirt Lillywhite about how AI-driven vision inspection is transforming the food industry. From smarter factories to faster quality control, they explore where automation meets innovation, and what the future of food production might look like.
🔍 Topics include:
- Why the term “AI” has lost its meaning, and what really matters.
- How vision systems are helping processors ensure quality and safety.
- What food factories might look like in 2035.
- How trust, accuracy, and human insight still play key roles in AI.
👤 About Our Guest
Kirt co-founded Smart Vision Works, well known for its AI and edge computing technologies used to sort products and detect physical contaminants in food production. He is a leader in AI-enabled Vision Inspection and is now KPM's Vice President of Software Engineering. He holds a Bachelor of Science in Computer Engineering and a PhD in Electrical Engineering from Brigham Young University in Utah. His research interests include real-time image processing, pattern recognition, parallel processing, and robotic vision.
Kirt’s Research and Publications
A Feature Construction Method for General Object Recognition:
https://www.sciencedirect.com/science/article/abs/pii/S0031320313002549
Self-tuned Evolution-COnstructed features for general object recognition:
https://www.sciencedirect.com/science/article/abs/pii/S003132031100238X
Implementation of an Award-Winning Invasive Fish Recognitionand Separation System:
https://www.mdpi.com/2079-9292/10/17/2182
Automatic Quality and Moisture Evaluations Using Evolution-Constructed Features:
https://www.sciencedirect.com/science/article/abs/pii/S0168169916307979
Object Recognition Algorithm For The AutomaticIdentification And Removal Of Invasive Fish:
Dense Disparity Real-Time Stereo Vision Algorithm forResource-Limited Systems:
https://ieeexplore.ieee.org/document/5970102
Real-time human detection usinghistograms of oriented gradients on a GPU:
https://ieeexplore.ieee.org/document/5403100/
Lessons learned in developing a low-cost high high-performancemedical imaging cluster:
https://ieeexplore.ieee.org/document/5255369
Vision Aided Stabilization and the Development of a Quad-Rotor Micro UAV:
