Wood Mulch Inspection
Classify varieties of wood chips and mulch automatically
Graphical programming environment for deep learning-based industrial image analysis
Wood chips are a common byproduct of wood harvesting, and have a variety of uses, including mulch, animal bedding, pulp, and power generation. Different varieties of wood chip are more suitable for one purpose than another, but, being a low-value product, wood chips are not typically tracked accurately, meaning they can easily be misidentified.
But some grades and types of wood chips do have a significantly higher value, particularly as mulch in home gardens, where gardeners have a variety of specific preferences, depending on local climate and tastes. The same is true of animal bedding where animals prefer one bedding type over another, and the wrong type of bedding can even be toxic. Directing wood chip varieties to where they can sell for the highest price can increase their value to the producer.
As a natural material, wood chips vary widely in color, texture, and shape, and must be identified against an extremely complex background of other chips of similar texture and color. Due to these factors, conventional machine vision is not a reliable solution for automating wood chip inspection.
AI-based solutions are trained on a small set of sample images of various grades and types of wood chips. The classification tool then quickly identifies the type of chip in a complex, random sample from a larger lot. Quality, price, and uniformity can be confirmed by wood mulch inspection before shipping to the customer, and the customer can receive exactly what they ordered.
If desired, the defect detection tool can be trained on a set of images of wood chips free of foreign particles, and then detect any undesired contaminants during wood chip or mulch inspection.