A harvester's speed and the speed of the extractor fan influence how much extraneous matter is retained as well as how much sugarcane juice is lost during harvesting.

The objective of this project is to develop a proof-of-concept Machine Vision System that evaluates cane loss during harvest by imaging to detect billboard particles and juice that fall onto the trash blanket. Data collected via machine vision sensors is then compared with sugar extracted from harvesters and commercial cane sugar (CCS) from mills under a range of field conditions. This enables the harvester operator to potentially adjust harvester operational parameters and amount of loss in response to real-time conditions.