Sugarcane Yield Grade Prediction Using Random Forest with Forward Feature Selection and Hyper-parameter Tuning

This paper presents a Random Forest (RF) based method for predicting the sugarcane yield grade of a farmer plot. The dataset used in this work is obtained from a set of sugarcane plots around a sugar mill in Thailand. The number of records in the train dataset and the test dataset are 8,765 records and 3,756 records, respectively. We propose a forward feature selection in conjunction with hyper-parameter tuning for training the random forest classifier. The accuracy of our method is 71.88%. We compare the accuracy of our method with two non-machine-learning baselines. The first baseline is to use the actual yield of the last year as the prediction. The second baseline is that the target yield of each plot is manually predicted by human expert. The accuracies of these baselines are 51.52% and 65.50%, respectively. The results on accuracy indicate that our proposed method can be used for aiding the decision making of sugar mill operation planning.