Leaf Diseases Detection of Medicinal Plants based on Image Processing and Machine Learning Processes

On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.

diseases,the growth and productivity are decreased from time to time. In the early stage of diagnosis of these diseases can help to prevent the spreading and helps to better productivity.
The authors survey various kinds of classification models based on image processing and machine learning techniques to detectand recognized the diseases of agricultural fields.
They also show the proposed method and its accuracy for detailed analysis [3].
In another research paper, the authors also survey a detailed analysis of different classification techniques to detect the diseases of various agricultural plants [4].
To detect the apple leaf disease authors proposed a deep neural base improved convolution neural network model. The proposed implementation predicts the 98% accuracy for disease detection [6].
To identify automatic crop diseases the authors survey 19 studies based on Convolution Neural Network (CNN). In this study, the authors describe diseases profile, implementation techniques of CNN, and analysis of the performance of the techniques. Finally, they provide the guidelines for an improved CNN for future research [7]. In another paper, the authors' overview different plant leaf disease detection based on different machine learning classification techniques. They describe different algorithms and their performance [8].

III. PROPOSED METHODOLOGY
In this experiment to detect the leaf diseases from an input image, the following steps are involved. Figure-1 shows the overview structure of the proposed leaf diseases system.
For segmentation, a global optimum threshold value is selected. In the leaf image database, the background of the input images is much darker therefore the segmentation effect is more effective. This procedure is faster and the calculation is much simple. Figure 5 shows the segmentation result of the input image.   are the two efficient gradient-based feature selection techniques [14]. Their performance is much better than any other feature selection technique.

a. Histogram Oriented Gradient (HOG)
It is a gradient-based feature descriptor [15]. It calculates the existing gradient and orientation of the input image.
Basically, this method broke the input image into a small piece of regions, then calculate the vertical and horizontal gradients of those blocks locally. Finally calculate the magnitude and orientations of those gradients.

b. Linear Binary Pattern (LBP)
It is a gradient-based effective texture descriptor [15].
This method divided the input image pixels into matrices. Then it considers the central value of matrices as a threshold value and set a binary value based on the threshold value. This process is continued for all pixel matrix and finally, it converts all the binary numbers into decimal and represents the input image in a better way.

CLASSIFICATION
In machine learning classification [16][17] is a process, used to understanding, recognizing, and grouping the same type of data based on their categories. The classification algorithms are used a pre-trained training dataset to predict the category of an unknown sample whose data fall into the predetermined categories.
For classification of an image support vector machine is one of the most popular and best classification method. It is a supervised learning method. This algorithm is divided the whole data space with a maximum margin to predict the class of the unknown data sample. Figure 6 shows the classification model of this experiment.

IV. EXPERIMENTAL RESULT AND DISCUSSIONS
The experimental database has 10 leaf classes and two main classes 'Healthy' and 'Diseased'. Table 1 shows the details about the experimental database.

V. CONCLUSION
In this paper, an automated leaf detection system is proposed.
To detect the healthy and diseased leaf image region-based thresholding technique is used. Again to detect the particular diseased area of a diseased leaf color-based region thresholding method was used. For feature selection from the input images both HOG and LBP feature selection technique is used. To classify the category healthy and diseased leaf with subclasses, leaf name, and disease name the two-class, and multi-class SVM classifier is used. A detailed performance analysis was done between main classes and subclasses by using different classifiers. Finally, a graphical user interface is created for all users.