Affiliations: [a] Geomatics Engineering Group, Indian Institute of Technology (IIT), Uttarakhand, India | [b] Department of Civil Engineering, Roorkee College of Engineering (RCE), Uttarakhand, India
Correspondence:
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Corresponding author: Sandeep Kumar Singla, Geomatics Engineering Group, Indian Institute of Technology (IIT), Roorkee-247667, Uttarakhand, India. E-mail: ssingla1@ce.iitr.ac.in.
Abstract: Recent technological enhancements in the field of information technology and statistical techniques allowed the sophisticated and reliable analysis based on machine learning methods. A number of machine learning data analytical tools may be exploited for the classification and regression problems. These tools and techniques can be effectively used for the highly data-intensive operations such as agricultural and meteorological applications, bioinformatics and stock market analysis based on the daily prices of the market. Machine learning ensemble methods such as Decision Tree (C5.0), Classification and Regression (CART), Gradient Boosting Machine (GBM) and Random Forest (RF) has been investigated in the proposed work. The proposed work demonstrates that temporal variations in the spectral data and computational efficiency of machine learning methods may be effectively used for the discrimination of types of sugarcane. The discrimination has been considered as a binary classification problem to segregate ratoon from plantation sugarcane. Variable importance selection based on Mean Decrease in Accuracy (MDA) and Mean Decrease in Gini (MDG) have been used to create the appropriate dataset for the classification. The performance of the binary classification model based on RF is the best in all the possible combination of input images. Feature selection based on MDA and MDG measures of RF is also important for the dimensionality reduction. It has been observed that RF model performed best with 97% accuracy, whereas the performance of GBM method is the lowest. Binary classification based on the remotely sensed data can be effectively handled using random forest method.
Keywords: Random forest, CART, GBM, KNN, NDVI, Landsat-8
