AN EFFICIENT HAND-CRAFTED FEATURES WITH MACHINE ...

Turkish Journal of Physiotherapy and Rehabilitation; 32(2)

ISSN 2651-4451 | e-ISSN 2651-446X

www.turkjphysiotherrehabil.org 1683

AN EFFICIENT HAND-CRAFTED FEATURES WITH MACHINE

LEARNINGBASED PLANT LEAF DISEASE DIAGNOSIS AND

CLASSIFICATION MODEL

1K. JAYAPRAKASH,

2DR. S.P. BALAMURUGAN

1Assistant Professor/Programmer, Department of Education, Annamalai University. 2Assistant Professor / Programmer, Division of Computer and Information Science, Annamalai

University.

Email: [email protected], [email protected]

ABSTRACT

India loses 35% of the yearly crop productivity owing to plant diseases. Earlier plant disease detection using

traditional methods or human experts is a complex and time-consuming process. Therefore, rapid and

automated plant disease detection models are essential to meet the increasing demand for food productivity

and quality. Presently, computer vision and image processing techniques find useful for plant disease

detection and increase crop yield sustainably. Therefore, this paper attempts to propose an efficient hand-

crafted feature with machine learning based plant leaf disease diagnosis and classification model. The

proposed model uses a Gaussian filtering (GF) technique to preprocess the input image and boosts its quality.

Besides, Grabcut based segmentation technique is utilized to identify the diseased portions in the plant

leaves. Moreover, two feature extractors namely local binary patterns (LBP) and Scale Invariant Feature

Transform (SIFT) models are applied as feature extractors. At last, multilayer perceptron (MLP) and random

forest (RF) models are employed as the classifier models to allocate the proper class labels to the test plant

leaf images. The performance of proposed method is assessed against a benchmark plant leaf disease dataset

and the experimental outcomes show the promising efficiency of the proposed model over the recent methods

interms of different measures.

Keywords: Agriculture, Plant disease detection, Machine learning, Intelligent models, Image processing,

Tomato leaf disease

I. INTRODUCTION

Agriculture is the major contributor to national income in some ofthe countries. Though farmers make significant

efforts in choosing healthier seed of plants and create appropriate environment for developing plants, it is several

diseases which affect plant resulting to distinct plant diseases. The plant pathogens like (Virus, fungi, and

Bacteria diseases) are the major cause of plant diseases. Similarly, a few insects that fed on the portions of plants

like (sucking insect pest), and plant nutrition’s like (absence of micro components) also, contain crucial impact

on developing plants [1]. The major problem in the area of agriculture is that it should determine the early

detection of plant diseases batches in earlier phase which makes for suitable time control to decrease the loss,

minimalize production cost, and raise the income. A common method for detecting and recognize plant diseases

is naked eye observation of specialists. As the timely and correct detection of diseases is highly significant,

automated methods are required to seek accurate, fast, less expensive disease detection. Image processing

techniques could satisfy the requirements. The image processing is utilized from agricultural applications to

succeeded determinations: (1) for identifying diseased fruit, leaf, stem, (2) for measuring infected region, (3) for

detecting shape of infected region, (4) for defining color of infected region, and (5) for defining shape and size of

fruits.

Currently, automated identification of plant diseases fascinates various scientists in distinct fields due to their

major advantages in observing huge fields of crops. Therefore, automated recognition of the disease’s symptoms

is attained after they arise on plant leaves. The automated recognition method is commonly comprising of 2

major steps. Initially, the plant leaf image is taken by digital camera. Next, the classification and detection of leaf

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diseases are attained by distinct phases: extraction of the affected area, calculationof several features

demonstrating every disease, and categorize the features for identifying the diseases. A significance of automated

detection and diagnosing of plant diseases appears as it can help in observing huge fields of crops, therefore it

gives automated recognition of diseases depending upon the symptoms that occur on plant leaves [2].

Recently, machine learning (ML) [3] based automated recognition of plant diseases fascinated several

investigators in distinct areas. [4] presented a novel accurate and fast method to grade plant diseases by computer

image processing system. Initially, they utilized Otsu technique for extracting the leaf area and later utilized

Sobel operator for detecting edges of diseased spot. Besides, the feature selection (FS) procedure is an essential

task for pattern recognition and classification system, for example, plant disease recognition method. It enhances

the prediction accuracy of methods by decreasing the amount of features, removes unrelated, noisy, and

redundant features. On the other hand, tomatoes are extensively cultivated food crops across the world. It

conquers 4th level among vegetable world. India is the most popular country concerned with tomato cultivation. It

is graded 5th between leading countries in the world. The tomato is a member of Solanaceae family that involves

Irish potatoes, eggplant, peppers, and tobacco. The leaf is a major source to all tomato diseases. The leaves of

healthier tomato plants are green in color.

This paper proposes a new hand-crafted feature with machine learning (ML) based plant leaf disease diagnosis

and classification model. The proposed model uses a Gaussian filtering (GF) technique to preprocess the input

image and boosts its quality. Then, Grabcut based segmentation technique is utilized to discriminate the infected

portions in the plant leaves. Furthermore, two feature extractors namely local binary patterns (LBP) and Scale

Invariant Feature Transform (SIFT) models are applied as feature extractors. Lastly, multilayer perceptron (MLP)

and random forest (RF) models are applied as the classification models. For examining the disease detection

efficiency of the proposed model, a set of simulations were performed on benchmark plant leaf disease dataset.

II. RELATED WORKS

[5] proposed an automatic method for detecting plant disease utilizing Gray Level Co-occurrence Matrix

(GLCM) and Wavelet based features. These features have been trained with distinct ML methods [6-8]. An

automatic method for tomato grading scheme was proposed by [9]. This technique employed texture and color

features and was categorized by SVM. [10] introduced an automatic method for diagnosing leaf disease by

GLCM and Gabor Wavelet Features (GWF) features. These multi-resolution features have been trained by

utilizing weighted KNN. [11] proposed a method for detecting leaf disease by exploiting hyperspectral

measurement. A method for detecting the seriousness of the disease in leaves are presented in [12]. Statistical

features fromHSV and RGB color space have been used to determine seriousness level.

[13] proposed a method for detecting leaf disease in tomatoes with integrating Otsu’s segmentation with DTs to

classify. This method considers texture, color, and shape features for learning the features of leaf disease. [14]

introduced a method for detecting leaf diseases by utilizing texture and color features. The affected area was

primarily segmented by K-means clustering. Later, features have been extracted from needed interest area and

trained by SVM for classification. The alternative method utilizing K-means approach is presented to detected

leaf disease and classification [15]. [16] utilized K-means clustering for detecting the existence of fungal

infection on leaves. A significant problem in employing aforementioned clustering method is establishment of

accurate number of clusters and setting of variables to distinguish every cluster.

In recent years, SIFTs have been examined for several image processing challenges. A method utilizes SIFT for

detecting leaf disease that was proposed by [17]. In this study, the SIFT features have been trained by utilizing

SVM to detect existence of disease. The SIFT based features have been integrated by Johnson SB distribution for

efficient classification of diseases from tomatoes [18]. The aforementioned approach is to detect disease which is

depending upon hand engineered features to extract in leaf parts of an image. The accurateness of this method is

individually based on the behavior of handcrafted features elected. Similarly, it is noticeable the efficiency of this

method must be authenticated towards an extensive dataset.

III. THE PROPOSED MODEL

The working procedure of the proposed plant leaf disease diagnosis model is displayed in Fig. 1. Firstly, BF

technique is employed to preprocess the image and thereby filtered the noise that exists in it. The subsequent



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stage involves the Grabcut technique for segmenting the diseased and non-diseased portions in the plant leaf

image.

Fig. 1. Working process of proposed model

Followed by, LBP and SIFT models are used for the extraction of meaningful features which are essential for

further examination. Finally, the MLP and RF models are utilized to classify the plant leaf images into normal

and diseased ones.

3.1. Image Preprocessing using GF Technique

Generally, image preprocessing is essential for any image processing related tasks, particularly plant disease

detection models [28-35]. Therefore, GF technique is employed to preprocess the images and discard the noise

that exists in them. The GF is mainly employed to smoothen the image and remove the noise that exists in it. The

convolutional operator is the Gaussian operator and the concept of Gaussian smoothening is accomplish utilizing

convolutions [19]. The Gaussian operator from 1-D can be defined by:

GlD(x) =1

√2𝜋oe− (

x2

2o2) . (1)

The optimum smoothening filter for images are identified in the spatial and frequency domains, thus filling the

uncertainty relationship as given below:

𝛥x𝛥𝝎 ≥1

2. (2)



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The Gaussian operator in 2D can be represented by:

G2D(x, y) =1

2𝜋𝜎2e− (

x2 + y2

2𝜎2) , (3)

where 𝜎 (Sigma) denotes the standard deviation of the Gaussian function. When the value of 𝜎 is high, then the

image smoothening result becomes high. Besides, (x, y)stands for the Cartesian coordinate points of an image

representing the window dimensions. It comprises additive and multiplication procedures amongst the kernel and

images, where the image can be denoted using a matrix ranging between 0-255.The kernel is a normalized square

matrix, which can be defined using several bits. In case of convolutional task, the multiplication of every bit of

kernel and every component of the image is divided through power of 2.

3.2. Image Segmentation using GrabCut Method

At the time of image segmentation, the preprocessed image is feed as input to Grabcut technique to distinguish

the normal and affected portions in the image. The Graph Cut method is most traditional approaches of

combinatorial graph concept. Recently, several researchers have employed this technique for segmenting video

and images that have attained better outcomes. The Graph Cut method is sort of image segmentation method

depending upon graph cutting process. It needs human communication markers with background and foreground

pixels as input. It is established by allowing the graph depending upon several degrees of related foreground and

background pixels. It also resolves the least cutting to differentiate the background and foreground. The energy

function is determined by:

E(𝛼, k, 𝜃, z) = U(𝛼, k, 𝜃, z) + V(𝛼, z) (4)

U(𝛼, k, 𝜃, z) = ∑ 𝐷

𝑛

(𝛼𝑛, 𝑘𝑛, 𝜃, 𝑧𝑛) (5)

V(𝛼, z) = 𝛾 ∑ [𝛼𝑛 ≠ 𝛼𝑚]

(𝑚,𝑛)𝜀C

exp − 𝛽‖𝑧𝑚 − 𝑧𝑛‖2 (6)

𝐷(𝛼𝑛, 𝑘𝑛, 𝜃, 𝑧𝑛) = − log 𝑝(𝑧𝑛 |𝛼𝑛, 𝑘𝑛, 𝜃) − log 𝜋(𝛼𝑛, 𝑘𝑛) (7)

The U function denotes area data item of energy function. The background and foreground are the combinations

of Gaussian method that is utilized for indicating the likelihood whether the pixel is background/foreground [20].

The V function denotes energy function boundary and the irregular penalty of adjacent pixels among m as well

asn. When the variance among 2 adjacent pixels is smaller, the likelihood that they belong to a similar foreground

and background is bigger. On the other hand, the 2 pixels are possible that exists edge and disjointed for

background and foreground. The varied Gaussian method is utilized for calculating a likelihood of every pixel

belong to the foreground/background and the image segmentation outcome are attained by enhancing the energy

function.

3.3. Feature Extraction

During feature extraction, the LBP and SIFT models are utilized and derived an appropriate set of feature vectors.

The LBP operator is a powerful and simple method for texture analysis. It is viewed as the integration among

structural and statistical methods of texture analyses. An essential feature of LBP operator is it consists of

rotation invariance, monotonic gray scale transformation, and illumination invariance,and ease computation. This

creates it probable for analyzing images in a shorter time. This featureis more fascinating for several types of

applications like iris recognition, biomedical, video retrieval, image and face recognition.The actual form of LBP

function describes the texture by 2 measures: local spatial and contrast patterns [21]. They extract the features by

relating every pixel with their circular 8 neighboring in 8 3x3 windows. This measure is compute utilizing the

value of the center pixel as threshold on the 8neighboring’s around all pixels. The binary numbers (pattern) are

attained and transformed to decimal number (LBP code) for labeling the pixel of the image. The contrast (Con) is

the variance among the mean of high neighboring values (that is higher compared to the value of central pixels)

and the mean of low neighboring values (that is lesser compared to the value of central pixels). The contrast is

estimated by Eq.(8).



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𝐶𝑜𝑛 = 𝛴𝐻𝑖

𝑁𝑖− 𝛴

𝐿𝑗

𝑁𝑙 (8)

Where 𝐻𝑖 , 𝐿𝑗 represent value of 𝑖𝑡ℎ high and 𝑗𝑡ℎ low neighborings correspondingly. 𝑁ℎ and 𝑁𝑙 denotes amount of

neighboring with high and low values correspondingly.The local spatial pattern is attained as follows: every pixel

is determined by its 8 neighborings. Initially, every neighboring is labeled by zero and one values. When the

value of neighboring exceeds the value of central pixel then it became zero. Therefore, the threshold neighboring

denotes a binary code that is allocated to central pixels. These binary codesare transformed to decimal (LBP)

number; by multiplying the threshold neighboring with provided weights matrix to the equivalent pixel. The

outcomes are attained from every multiplication that is added to attain LBP code for central pixel.

Next, SIFT technique is mainly employed to identify and extract local feature descriptor that is non-variant to

scaling, image illumination, and rotation. SIFT feature has several advantages as follows:

They are non-variant to alignment, uniform scaling, and moderately non-variant to illumination

modifications;

Improved error tolerance with few matches;

with good efficiency and speed;

convenient to combine and generate useful information.

The detection stage of SIFT feature is divided into 4 steps:

Scale space extrema identification [22]: In this stage, at first, the image 𝐼(𝑥, 𝑦) undergo convolution with GD at

varying scales as given in Eq. (9):

𝐿(𝑥, 𝑦, 𝜎) = 𝐺(𝑥, 𝑦, 𝜎) ∗ 𝐼(𝑥, 𝑦) (9)

where, 𝐿(𝑥, 𝑦, 𝜎) is convolutional of image 𝐼(𝑥, 𝑦) with the GF 𝐺(𝑥, 𝑦, 𝜎) at scaling of 𝜎. Differences between

two Gaussian images at scale 𝑘𝜎 and 𝜎 are taken as Eq. (10) shown:

𝐷(𝑥, 𝑦, 𝜎) = 𝐿(𝑥, 𝑦, 𝑘𝜎) − 𝐿(𝑥, 𝑦, 𝜎) (10)

The difference at these two scales is called a DoG (Differences of Gaussians) image:

Keypoint localization: After the first stage, keypoints, also can be called Interest points are identified as local

maximal or minimal of the DoG images over scales. All the pixels from the images undergo comparison with the

8 neighbors at an identical scale. It also needs to accurately perform the keypoints’ localizations by removing

points by a fixed value.

𝐷(𝑥) = 𝐷 +1

2

𝜕𝐷𝑇

𝜕𝑥𝑥 (11)

where 𝑥 is determined by keeping 𝐷(𝑥, 𝑦, 𝜎) to 0.

Orientation assignment: In order to accomplish non-variance to orientation, the gradient magnitude 𝑚(𝑥, 𝑦) and

orientation θ (𝑥, 𝑦) are predetermined using Eq. (12-13):

𝑚(𝑥, 𝑦) = √(𝐿(𝑥 + 1, 𝑦) − 𝐿(𝑥 − 1, 𝑦))2

+ (𝐿(𝑥, 𝑦 + 1) − 𝐿(𝑥, 𝑦 − 1))2

(12)

𝜃(𝑥, 𝑦) = 𝑎𝑟𝑐𝑡𝑎𝑛 (𝐿(𝑥, 𝑦 + 1) − 𝐿(𝑥, 𝑦 − 1)

𝐿(𝑥 + 1, 𝑦) − 𝐿(𝑥 − 1, 𝑦)) (13)

Keypoint descriptor generation: If a keypoint orientation is chosen, the feature descriptor is determined by a

collection of orientation histograms on 4 × 4 pixel neighborhoods

3.4. Image Classification



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At the final stage, the MLP and RF models are employed to allocate the proper class labels to the test plant leaf

images using the extracted feature vectors.The MLP method is one of the commonly utilized kinds of ANN

methods. It belongs to a common class structure of ANN named FFNN. AnFFNN framework of MLP comprises

of neuron that is gathered in layers. In MLP method, the entire input nodes in input and hidden layers are

dispersed to several hidden layers [23]. Fig.2 displays a common architecture of simple FFFN. Assume that there

are 𝑁 layers in MLP: initial layer is named as input, 𝑁th layer represents output, and two to 𝑁 − 1 layers denote

hidden layer. Consider that there are 𝐿𝑙 neurons, in which, 𝐿𝑙 = 1, 2, 3, … , 𝑁.

Fig. 2. Structure of FFNN

Where 𝑤ij𝐿 and x𝑖𝑗 denotes weight and 𝑖th indicates neuron, correspondingly, thus 1 ≤ j ≤ 𝐿n−1, i ≤ i ≤ 𝐿n, while

𝑤𝑖𝑗 denotes weights and x𝑖𝑗 represents external input for method, and 𝑍i indicates output of 𝑖th neuron of 𝑁th

layer. Similarly, 𝑤i𝑜n denotes additional weight variables, which denotes bias of 𝑖th neuron of 𝑁th layer, thus 𝑤

involves 𝑤ijn.

Given by

𝑤 = [𝑤ij1 , 𝑤ij

2, 𝑤ij3, … , 𝑤𝐿𝑁𝐿𝑁−1

𝑁 ], (14)

where

j = 0, 1, 2, … , 𝐿n−1,

i = 1, 2, 3, … , 𝐿, (15)

𝑛 = 1, 2, 3, … , 𝑁.

RF is employed to generate a DT for all arbitrary samples from the actual data, and integrate the outcomes of

several DT outputs to voting model as the end output. It undergoes arbitrary sampling and the combined outcome

of the outcomes is termed Bagging. The steps involved in it are listed as follows.

Random extraction of 𝑛 training instances from the input dataset by the use of Bootstrap;



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Perform 𝑘 iterations of extraction and 𝑘 training dataset is attained;

Train 𝑘 DT models for 𝑘 training dataset;

In order to attain plant disease detection, the average of the predictive outcome of every method is employed

as the final outcome.

To problem of charging load prediction: the average of forecast outcomes of all the models are utilized as last

forecast outcome.

Next to the bagging of preprocessed data instances, they are partitioned into k data packets. In case of every data

packet, the regression DT is individually generated [24]. It beings with the starting node (root node), regression

kind is aimed in the minimization of Gini coefficient (uncertainty) using CART technique and iterate the process

till the target or maximum depth is attained. During the classification process, the estimated data features are fed

as input to the model. Then, every DT produces a predictive outcome and the whole RF exploits the average

outcome of every DT as the end predictive outcome.

IV. PERFORMANCE VALIDATION

The performance validation of the proposed model takes place utilizing a benchmark PlantDoc dataset [25]. The

presented method is simulated utilizing Python 3.6.5 tool. The details of the dataset are given in Table 1 and

sample test images are displayed in Fig. 3. The dataset holds a set of 1000 images under Early_Blight, 1909

images under Late_Blight, 952 images under Leaf_Mold, and 1591 images under Healthy. The sample processes

obtained during simulation is given in Appendix.

Table 1 Dataset Descriptions

Classes Number of Images

Early_Blight 1000

Late_Blight 1909

Leaf_Mold 952

Healthy 1591

Total Images 5512



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Fig. 3. Sample Images

The confusion matrix generated by the LBP-RF technique on the detection of plant leaf diseases is given in Fig.

4. From the figure, it can be observed that the LBP-RF approach has classified a set of 827 images into

Early_Blight, 1558 images into Healthy, 1738 images under Late_Blight, and 747 images under Leaf_Mold.

Fig. 4. Confusion matrix of LBP-RF model

The confusion matrix generated by the LBP-MLP approach on the detection of plant leaf diseases is given in Fig.

5. From the figure, it can be stated that the LBP-MLPmethod has classified a set of 857 images into Early_Blight,

1564 images into Healthy, 1769 images under Late_Blight, and 736 images under Leaf_Mold. Besides, the

confusion matrix generated by the SIFT-RF approach on the detection of plant leaf diseases is given in Fig. 6.

From the figure, it is clear that the SIFT-RF technique has classified a set of 905 images into Early_Blight, 1537

images into Healthy, 1803 images under Late_Blight, and 729 images under Leaf_Mold.

Fig. 5. Confusion matrix of LBP-MLP model



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Fig. 6. Confusion matrix of SIFT-RF model

Fig. 7. Confusion matrix of SIFT-MLP model

The confusion matrix generated by the SIFT-MLP method on the detection of plant leaf diseases is given in Fig.

7. From the figure, it is apparent that the SIFT-MLP technique has classified a set of 971 images into

Early_Blight, 1558 images into Healthy, 1788 images under Late_Blight, and 720 images under Leaf_Mold.

Table 2 and Fig. 8 summarizes the plant leaf disease detection performance of the proposed models on the

applied test images. The proposed LBP-RF model has effectively classified the plant leaf diseases with an

accuracy of 0.8933, precision of 0.9027, recall of 0.8753, and F1-score of 0.8858.



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Table 2 Result Analysis of Proposed Methods in terms of Different Measures

Methods Accuracy Precision Recall F1-Score

LBP-RF 0.8933 0.9027 0.8753 0.8858

LBP-MLP 0.9035 0.9026 0.8850 0.8922

SIFT-RF 0.9123 0.9315 0.8953 0.9086

SIFT-MLP 0.9239 0.9349 0.9108 0.9179

Fig. 8. Result analysis of proposed method with different measures

Besides, the presented LBP-MLP method has effectively classified the plant leaf diseases with an accuracy of

0.9035, precision of 0.9026, recall of 0.8850, and F1-score of 0.8922. Moreover, the projected SIFT-RF approach

has efficiently classified the plant leaf diseases with an accuracy of 0.9123, precision of 0.9315, recall of 0.8953,

and F1-score of 0.9086. Furthermore, the presented SIFT-MLP methodology has effectually classified the plant

leaf diseases with an accuracy of 0.9239, precision of 0.9349, recall of 0.9108, and F1-score of 0.9179.

Fig. 9. ROC analysis of LBP-RF model



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Fig. 10. ROC analysis of LBP-MLP model

Fig. 9 examines the ROC analysis of the presented LBP-RF method on the detection of plant leaf diseases. From

the figure, it is apparent that the LBP-RF model has proficiently identified the plant leaf diseases with the ROC of

0.98 under Early_Blight, 0.99 under Healthy, 0.97 under Late_Blight, and 0.98 under Leaf_Mold images.

Fig. 10 shows the ROC analysis of the projected LBP-MLP method on the detection of plant leaf diseases. From

the figure, it is revealed that the LBP-MLP technique has proficient identified the plant leaf diseases with a ROC

of 0.98 under Early_Blight, 1 under Healthy, 0.99 under Late_Blight, and 0.97 under Leaf_Mold images.

Fig. 11. ROC analysis of SIFT-RF model

Fig. 11 determines the ROC analysis of the presented SIFT-RF technique on the detection of plant leaf diseases.

From the figure, it is stated that the SIFT-RF approach has proficient identified the plant leaf diseases with the

ROC of 0.99 under Early_Blight, 0.97 under Healthy, 0.96 under Late_Blight, and 0.98 under Leaf_Mold

images.



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Fig. 12. ROC analysis of SIFT-MLP model

Lastly, Fig. 12 inspects the ROC analysis of the proposed SIFT-MLP technique on the detection of plant leaf

diseases. From the figure, it is clear that the SIFT-MLP model has proficient identified the plant leaf diseases

with the ROC of 1 under Early_Blight, 0.99 under Healthy, 0.99 under Late_Blight, and 0.98 under Leaf_Mold

images.

A comprehensive comparative results analysis of the presented model with existing methods takes place in Table

3 and Fig. 13 [26, 27]. The figure showcased that the Inception v3 model has accomplished least performance

with an accuracy of 6.34%. Followed by, the ACNN and VGG-16CNN models have demonstrated certainly

increased outcomes with the accuracy of 76% and 77.2% respectively. Similarly, the HCF-QSVM and CNN-

LVQapproaches have depicted moderate results with the accuracy of 83.5% and 86%. Simultaneously, the HCF-

SVM model has showcased reasonable results with an accuracy of 88.89%. However, the proposed LBP-RF,

LBP-MLP, SIFT-RF, and SIFT-MLP models outperformed the earlier methods with the accuracy of 89.3%,

90.4%, 91.2%, and 92.4% respectively. Among the different proposed models, the SIFT-MLP model is found to

be superior and appeared as an effective plant leaf disease detection model.

Table 3Comparative analysis of Proposed Methods with Existing models in terms of Accuracy

Methods Accuracy (%)

Proposed SIFT-MLP 92.40

Proposed SIFT-RF 91.20

Proposed LBP-MLP 90.40

Proposed LBP-RF 89.30

HCF-QSVM 83.50

ACNN 76.00

CNN-LVQ 86.00

HCF-SVM 88.89

VGG-16 CNN 77.20



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INCEPTION V3 63.40

Fig. 13. Accuracy analysis of proposed method with existing techniques

V. CONCLUSION

This paper has proposed an efficient hand-crafted feature with ML based plant leaf disease diagnosis and

classification model. The proposed method employed theGF technique to preprocess the image and thereby

filtered the noise that exists in it. In addition, the Grabcut technique is applied for segmenting the diseased and

non-diseased portions in the plant leaf image. Besides, LBP and SIFT models are used for the extraction of

meaningful features which are essential for further examination. At last, the MLP and RF models are utilized to

classify the plant leaf images into normal and diseased ones. For examining the disease detection efficiency of the

proposed model, a set of simulations were performed on benchmark plant leaf disease dataset. The experimental

results demonstrated the promising results of presented method over the recent techniques interms of different

measures. As a part of future scope, the efficiency of the proposed method can be raised via deep learning

models.

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VI. APPENDIX

Image Preprocessing

Segmentation Process



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Classification Process


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