A computer aided plant leaf classification based on optimal feature selection and enhanced recurrent neural network

This paper develops a new plant leaf classification model based on enhanced segmentation and optimal feature selection. The first process is the pre-processing, in which RGB to grey-scale conversion, histogram equalisation, and median filtering are adopted. Further, the optimised U-Net model is used for the leaf segmentation. Once the segmentation of the leaf is done, a set of features are extracted related to shape, colour, and texture. Since the length of the feature vector seems to be high that in turn affects the network training, optimal feature selection is adopted in order to reduce data dimensionality and to build robust classification models. Here, the optimal feature selection is performed by the new hybrid algorithm, namely Crow-Electric Fish Optimization (C-EFO), which is the hybridisation of Electric Fish Optimization (EFO) and Crow Search Algorithm (CSA). Finally, the deep learning model termed as Enhanced Recurrent Neural Network (E-RNN) is used for performing the classification with the improvement based on C-EFO. From the analysis, the accuracy of the proposed C-EFO+Opt-U-Net+E-RNN is 4.7% better than k-NN, 3.5% better than VGG16, 3.5% better than LSTM, and 2.75% better than RNN, respectively. Finally, the experimental results on two plant leaf databases show that the proposed method is quite effective and feasible when compared to conventional models.