Measurement of Trash Percentage for Mechanical Harvested Sugarcane Using Multispectral Image

This research aimed to develop a new technique for estimating the percentage of trash in mechanically harvested sugarcane samples using multispectral imaging and machine learning. The research found that the Random Forest model with a Classification Tree of 150 was the most effective classification model, with a Quality Percentage of 96.05%. In addition, the best prediction models for estimating the weight of sugarcane billet using Multiple Linear Regression had an R2 value of 0.77 and a Root Mean Square Error of calibration (RMSEC) value of 21.03 g. The results of the research also showed that the new technique was capable of accurately estimating the percentage of trash in sugarcane samples, with a Root Mean Square Error of prediction (RMSEP) of 2.09% when compared to traditional methods. This suggests that the new technique may be a useful, rapid method for assessing the percent of sugarcane billet and trash in production processes.