Research on plant leaf recognition method based on multi-feature fusion in different partition blocks

As an indispensable organism in nature, plants play an essential role in the ecological equilibrium. Ecological plant protection is also receiving increasing attention. In today's digital era, one of the important research topics is recognizing plants by using imaging equipment. In this study, we propose a multi-feature fusion approach based on the Local Binary Pattern (LBP) feature for plant leaf recognition by using the partition block strategy. By comparing the original LBP and Multiscale Block Local Binary Pattern (MB-LBP), we present an improved LBP feature descriptor. It extends the range of feature extraction, considering the effect that the multi-neighbourhood pixels have on the central pixels during the LBP encoding process and the central pixels are represented by double coding values. Therefore, it can extract more detailed information about plant leaves. Moreover, considering the leaf boundary and shape information extraction and the illumination variations, the Histogram of Oriented Gradient (HOG) feature and the colour feature are fused with the improved LBP feature descriptor. After dimensionality reduction by Principal Component Analysis (PCA), a mixed feature vector of plant leaves is used as input to an Extreme Learning Machine (ELM) for identifying plant leaves in two publicly available datasets, the Flavia dataset and the Swedish dataset. Experimental results show that our algorithm outperforms the conventional algorithms, with the recognition accuracy of 99.30% on the Flavia dataset where 4 x 4 partition blocks are used for the improved LBP feature and 2 x 2 partition blocks for the colour feature, 99.52% on the Swedish dataset where 4 x 4 partition blocks are used for the improved LBP feature and 2 x 2 partition blocks for the colour feature, respectively.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).