Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Crop and weeds identification is of important steps towards the development of efficient automotive weed control systems. The higher the accuracy of plant detection and classification, the higher the performance of the weeding machine. In this study, the capability of two popular boosting methods including Adaboost.M2 and Logit Boost algorithms was evaluated to enhance the plant classification performance of Random Forest (RF) feature filtering techniques including Information Gain (IG were applied to the image-extracted features and fed into single and boosted classifiers. The RF model trained by IG selected features (IG-RF) was the most appropriate classifier among the evaluated models whether in single or boosted modes. It was also found that boosting of IG-RF by using Adaboost.M2 and LogitBoost algorithms improved the classification accuracy. The accuracy, k, and RMSE were calculated to analyze the performance of algorithms. It was concluded that combination of boosting algorithms and feature selection methods can promote plant type discrimination accuracy, which is a crucial factor in the development of precision weed control systems.

Keywords: Random Forest, Adaboost.M2, Logit Boost, RMSE, Kappa.