Plant Species Recognition Case Study

Plant species recognition is a prominent research area similar to Face recognition problem. Plant species recognition done through image processing has common obstacles in images such as illumination, viewpoints, orientation, background colour variations. Apart from this plant species recognition is hard since it has many species with inter-class and intra-class variations. To solve these problems, a backpropagation neural network (BPNN) has been created. This BPNN model contains an input layer, two hidden layers and an output layer. The input layer contains 3072 neurons and the neurons in output layer depend on the number of classes to be classified in the dataset. We have collected and created our own Indian plant dataset named as Leaf12.
Plant species recognition is complicated because of the common challenges that appear in images such as illumination, viewpoints, orientation, colour variation, background changes, intra-class and inter-class variations between species. There are several applications such as pl@ntnet, leafsnap, etc. for plant image retrieval, displaying the plant information. These applications also tend to collect plant images from the users. Since, leaf is the common part available in plants, most of the datasets contain leaf images. Either of the features like shape, texture or colour are used in plant species recognition.
Camilla et al. used backpropagation neural network (BPNN) for intra-class classification of tea plants (17 tea plant varieties) from Vietnam. Fourteen morphological parameters were used as inputs. Fifty hidden neurons were activated by the logistic sigmoid activation function. BPNN outputs were further investigated by cluster analysis using Unweighted Pair Group Method analysis (UPGMA) and formed a
proposed a model for classification of plant leaves using flavia dataset. Principal Component Analysis was used for feature extraction and Probabilistic Neural Network (PNN) for classification of 12 morphological features, which were derived from 5 geometric features. Five features were identified using PCA as important and was taken as the inputs for PNN.
Naresh et al. proposed modified LBP (Local Binary Pattern) for feature extraction and nearest neighbor classifier for medicinal plant classification. In general, hard thresholding was used for LBP. In modified LBP, mean and standard deviation were taken into consideration instead of threshold values. LBP method was chosen for feature extraction as this was good for texture analysis. This method was tested over UoM medicinal plant dataset, Flavia, foliage Swedish, and Outex datasets. UoM medicinal plant dataset were collected from Mysore, India and contained 33 medicinal plants with 1320 images in