Author(s)

Idris Idris Sunusi, Zhou Jun, Zhenzhen Wang, Chenyang Sun, Nura Alhaji Sale, Nuhu Sulaiman Adam

Corresponding Author

Zhou Jun

ABSTRACT

An experiment was conducted to develop algorithms for online terrains classification by robots used in agricultural environments. An instrumented agricultural tractor robotic platform was used to collect data from four different terrains; concrete, dense grass, sparse grass, and firm soil. The data collected were hand-labeled, segmented, and transformed from a time domain to frequency domain using fast furrier transform (FFT). The feature dimension of the transformed data was reduced using principal component analysis (PCA), and the principal components that account for 95% of the total variation in the feature data were selected. The selected features were used to train decision tree and linear discriminant analysis classifiers. From the result, the linear discriminant analysis performed better than the decision tree, and PCA improved the speed and accuracy of the online classification and offline training.

KEYWORDS

Agricultural robot; terrains classification; fast furrier transform; linear discriminant analysis; decision tree