Microscopic Image Based Classification Algorithms for few Herbal Plants Identification using Machine Learning Approaches by Bhupendra Damjibhai Fataniya

Guided by: Dr. Taniz Zaveri and Dr. Sanjeev Acharya With Synopsis and CD 11EXTPHDE76

ABSTRACT:
Identification of herbal plant is of great interest in image processing and computer vision. In
literature, many methods are presented to identify the herbal plant like leaf-based
identification, chemical-based evaluation, physical evaluation and biological evaluation.
Identification of herbal plant is more difficult and challenging when it is presented in powder
form.
This thesis presents microscopic image-based classification of a few herbal plants from its
powder using various machine learning approaches. In this work, the cell characteristics of
the herbal plants are studied. The dataset of the powder microscopic images of the three
herbal plant is created in our laboratory using a Lawrence & Mayo microscope.
It is found from the literature survey that various object can be uniquely represented by shape
and texture based features which are further used for the object classification. In this thesis,
shape and texture feature based novel methods for classification of herbal plants (Liquorice,
Rhubarb and Dhatura) are proposed. The effectiveness of shape and texture feature methods
are evaluated using different classifiers for classification. Three shape and five texture
features are computed from the microscopic image dataset of the herbal plants. The
effectiveness of the shape and texture based feature set and their combinations are
investigated using Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and
Ensemble classifier. The effect of Speeded-Up Robust Features (SURF) is also analyzed
using a different kernel of the SVM classifier. From the experiments highest classification
accuracy of 99.9% is achieved when all the shape features with a combination of Gabor
wavelet features are applied to quadratic SVM classifier. Finally, from the proposed
algorithm, it is observed that the combination of selected shape and texture features work
better for the classification of powder microscopic image of the herbal plant of Liquorice,
Rhubarb and Dhatura. Convolutional Neural Networks (CNN) based classification is also carried out using pretrained
CNN models like AlexNet, GoogLeNet and VGG for the herbal plants. Transfer
learning approach is applied to train the pre-trained CNN models for our dataset. From the
experiments, it is found that AlexNet-TL performs better and the results are compared with
other pre-trained model GoogLeNet and VGG. It gives the highest accuracy of 99.49% with
40x dataset when the single resolution dataset is considered for training CNN. In the case of
the combination of the two datasets of different resolutions, VGG16-TL achieves the highest
accuracy of 99.96% with 4x and 40x dataset. The combination of two or more datasets of
different resolution increases the training complexity in the CNN model. GoogLeNet-TL
performs better when all three resolution of the dataset are combined. It achieves the highest
accuracy of 99.03%. The fourth category of the class where the background of the image has
been included with the dataset to train the pre-trained CNN model. In this category of dataset,
AlexNet-TL gives 98.65% accuracy which is better than other pre-trained CNN model. It is
observed from the simulation results, transfer learning from the pre-trained CNN model
works better for automatic classification and highest classification accuracy of 99.96% is
achieved in VGG16-TL for the combination of 4x and 40x dataset. When dataset of 4x, 10x
and 40x is applied to the CNN for training, transfer learning work well with 40x dataset. It is
due to the clear visibility of cell characteristics at 40x resolution.
The features extracted from the microscopic image of the herbal plant using shape and texture
are hand crafted features. It is very difficult to tune hand crafted features with a classifier for
the herbal plant classification. In the CNN based classification of herbal plants, features are
self-extracted from the input image dataset. CNN based classification is the good choice
when input image dataset is complex and difficult to tune the hand crafted features. It takes
the more training time to train CNN model. This problem can be overcome by using the
Graphic Processor Unit (GPU) during the training of CNN.