A computer-based vision systems for automatic identification of plant species using kNN and genetic PCA

Authors/Creators

Oluleye Babatunde, Edith Cowan UniversityFollow
Leisa Armstrong, Edith Cowan UniversityFollow
Jinsong Leng, Edith Cowan University
Dean Diepeveen

Abstract

Precision farming involves integration of different areas of disciplines to lower production costs and improve productivity. One major arm of precision farming or agriculture is the development of computer-based vision systems for automatic identification of plant species. This work involves application of k Nearest Neighbour (kNN) and genetic principal component analysis (GA-PCA) for the development of computer-based vision systems for automatic identification of plant species. As the first contribution, several image descriptors were extracted from the images of plants found in the Flavia dataset. Lots of these image features are affine maps and amalgamation of such massive features in one study is a novel idea. These descriptors are Zernike Moments (ZM), Fourier Descriptors (FDs), Lengendre Moments (LM) Hu 7 Moments, Texture, Geometrical properties and colour features. The GA-PCA (1907 x 41) feature space improved the classification accuracy of kNN from 84.98% to 88.75%.

RAS ID

20806

Document Type

Journal Article

Date of Publication

2015

School

School of Computer and Security Science

Copyright

free_to_read

Publisher

Hungarian Association of Agricultural Informatics

Comments

Babatunde, O.H., Armstrong, L., Leng, J., Diepeveen, D. (2015). A computer-based vision systems for automatic identification of plant species using kNN and genetic PCA. Journal of Agricultural Informatics. 6(2). 32 - 44. Available here

Recommended Citation

Babatunde, O., Armstrong, L., Leng, J., & Diepeveen, D. (2015). A computer-based vision systems for automatic identification of plant species using kNN and genetic PCA. DOI: https://doi.org/10.17700/jai.2015.6.2.164