Effect of Fusion of Statistical and Texture Features on HSI based Leaf Images with Both Dorsal and Ventral Sides

The present work involves statistically analyzing and studying the overall classification accuracy results using Hue channel images of different plant species using their dorsal and ventral sides, and then subjecting them to the process of feature extraction using first order statistical features and texture based features. These extracted features have been subjected to the classification process using KNN and Random Forest algorithms. Further, this work studies the fusion of two different kinds of features extracted for dorsal and ventral plant leaf images and studying the effect of fusion on the overall classification accuracy results. This work also delves into the feature selection task using random forest algorithm and studies the effect of reduced dataset with unique features on the overall classification accuracy results. The most important outcome of this investigation is that the ventral leaf images can be a suitable alternative for plant species classification using digital images and further, the fusion of features does improve the classification accuracy results.

@article{Saini2018,
  title     = {Effect of Fusion of Statistical and Texture Features on HSI based Leaf Images with Both Dorsal and Ventral Sides},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {9},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Poonam Saini and Arun Kumar},
  doi       = {10.14569/IJACSA.2018.090940},
  url       = {https://doi.org/10.14569/IJACSA.2018.090940}
}