On-line measurement of the physical properties of each cane consignment at the factory

This project is investigating the use of computer vision and machine learning to measure various parameters in harvested cane brought from farms to the sugar mill, to improve the supply of cane for processing into sugar. This usually comprises billets of the cane stalk, leaves, tops and roots, with everything else classified as extraneous matter (EM) that doesn't contribute to the quantity of sugar that can be produced, and may be more costly for the mill to process and reduce factory throughput.

By having an on-line measurement of the physical properties of the cane supply, the data can be used with harvesting information (field loss estimates etc.) and transport information (bin weights etc.) to determine optimum harvesting conditions and maximise cane value in each district.