Plant Keywords Plant species recognition, back propagation neural
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. This Leaf12 dataset contains inter-class species with 3840 images. BPNN model is tested using our dataset and compared with other benchmark datasets such as Folio, Swedish, 17Flowers, and Flavia. Our Indian plant dataset worked well in variations such as illumination, viewpoints, orientation, background colour variations and for inter-class species.
Plant species recognition, back propagation neural network, Indian plant dataset, inter-class species, classification
Plant species recognition is one of the interesting research areas in image processing since it has its wide application in agriculture, medicine, etc. 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 [email protected], 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 dendogram.
Hongfei et al. classified five species from Camelia genus (93 plant species sample) based on clustering approach, Learning Vector Quantization Neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Network (DAN2), C-Support Vector Machine. For classification, architectural and morphological characteristics of the leaf were considered. Camelia species were identified the best using DAN2 and SVM methods.
Stephen et al. 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 total.
Yu Sun et al. proposed a 26-layer ResNet (Residual Network) model for plant identification and it was said to be suitable for smart forestry. A large dataset was created through images taken using mobile phone and was known as BJFU100 dataset. This dataset contained 10000 images of 100 ornamental plant species found in Beijing Forestry University campus. For experimental analysis, BJFU100 and Flavia datasets were taken. In deep residual networks 18, 26, 34, and 50 layers were considered. Amongst the 4 layers taken, ResNet 26 outperformed the other three models. For experimental training, learning rate was set to 0.001. Flavia dataset accuracy result was compared with other approaches like PBPNN, SVM, DBN and ResNet26 shows 99.65% recognition rate.
Deep neural networks being the hot topic in image recognition, it was observed that works on simple neural networks over Indian plant species was sparse in recent years. Even, if works related to neural networks were present, morphological or physiological features were considered. Hence, instead of using geometrical or morphological features of leaf, the images were normalized and those values were given as inputs for BPNN. Our paper has been organized into four sections such as, neural network, datasets, results and discussion, conclusion and future work.