Earlier this month, we shared our research findings at the Society for Machinery Failure Prevention Technology Conference in Virginia Beach, VA. Our research was specific to Grain Elevator Quality Assurance using Deep Convolutional Neural Network.
The accidental mixing of different grain types at an elevator is a common and costly mistake resulting in either a total loss or costly blending processes to correct. The team presented an automated and intelligent quality assurance system to prevent these incidents by using a deep convolutional neural for crop type classification deployed to an edge device.
According to the team’s research, human crop type classification accuracy across eight different crop types is 99.7% for the best expert and as low as 83.9% for the worst novice with an overall accuracy of 96.2% for the entire group in lab experiments. It is anticipated that in real-world conditions when faced with fatigue, distraction, varied skill level and negligence this number would reduce further.
“It is clear that a highly accurate classification system which is able to continuously monitor the process and warn the operator would help reduce incidents.”
To read more about the findings from the team’s research, click here.