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result. The durability simulation helps them in protecting the plant
against the pathogen. The disease in the plant can be classified based on
morphological and chemical conditions. Visible and infrared spectral
reflection differs for each plant. Several methods like an artificial neural
network, fuzzy logic, and support vector machine are used in the clas
sification of disease in the plant.
Identifying disease based on spectral reflectance can be done using a
decision tree and k nearest neighbor method. For identifying weeds in
the rice field neural network and PCA methods are used. When an image
database of the plant is used for disease identification component
analysis technique is used. Observing the disease through the naked eye
is the common procedure followed for identifying disease in the plant.
But this process requires manpower and monitoring continuously. Some
of the advantages of automatic detection of disease are less time, min
imum effort and accurate prediction. The image segmentation technique
mainly helps in the disease detection process. Based on the features
present in the leaf segmentation process is done. In some cases, for
segmenting the color image, a genetic algorithm is used. There exist
many literatures that demonstrates the efficacy of their own developed
intelligent models, but still consists of some limitations. Mostly the
performance inefficiency, inability to adapt to the new environment,
erroneous predictions are the challenges in the existing studies. This
paper intends to proposes an effective solution, a loss-fused convolu
tional neural network model to improve the predictive performance of
the model.
The rest of the paper is organized as follows. Section 2 briefly dis
cusses the concepts related to this study. In section 3, an in-depth
overview of the convolutional neural network and its processing
layers is given. The proposed methodology is stated in section 4 with
formulations and algorithmic representations. The results obtained from
this study were projected with graphs and tables in section 5. Section 6
concludes the work and highlights the significance of the proposed
system.
2. Background study
Ferentinos (2018) proposed a model based on Convolution Neural
Network for identifying disease in diseased plant leaves. Deep learning
techniques are also used in it for identification. It includes 58 different
classes of diseased and healthy data for testing and training purposes.
The proposed model achieves a high success rate in a real-time envi
ronment. The database includes plant common and scientific name,
disease common and scientific name and list of images under laboratory
and field condition. Deep learning model like AlexNetOWTBn and VGG
is used and the success rate for it are 99.49% and 99.53% and the error
rate for both the model are 0.017 and 0.02. The performance of the
different models for both original and pre-processed images is analyzed.
In the future, this application can be integrated with mobile devices and
a pesticide prescription system can be made. Too et al. (2019), devel
oped a method to detect automatically, quickly and accurately from the
image. It mainly focused on fine-tuning and deep CNN for plant disease
identification. Architectures like Inception V4, VGG 16, and Res Net
with layers like 50, 101, and 152 and Dense Nets are evaluated. The
dataset contains 38 classes from 14 plants of healthy and diseased plant
image data. Based on the evaluation the Dense Net architecture shows a
constant increase in accuracy with the number of epochs increasing
simultaneously. The accuracy gets improved when less number of pa
rameters are used. The testing accuracy achieved during this process is
99.75%. The library used for it is Keras, CNMeM, CNN, and Theano.
Ip et al. (2018), mainly focused on the techniques used in crop
protection using big data. Several techniques used for protecting the
crop are also reviewed. Markov random field is also explored briefly in
it. The resistance of herbicide using Markov random field is discussed
and the parameters like pH of the soil, shire state, amount of cultivation
and stubble management are also taken into consideration while pre
dicting. Based on the results shown all the machine learning techniques
provides an efficient way of predicting the results from the given data
sets. Barbedo et al., employed deep learning technology for classifica
tion of plant disease and identification of disease. Some of the issues
faced during deep learning techniques are rectified. It also provides an
overview of factors that affect the design. Corn leaf samples with both
diseased and healthy leaves are taken and trained and implemented in a
convolution neural network and the results are comparatively good.
CNN shows greater accuracy when the background of images is removed
and when the training samples are subdivided. Factors that affect the
performance of CNN are clearly listed. The key objective of this paper
provides advancement in the field of disease identification using the
image. In another instance, Barbedo et al., have proposed a technique
for the detection of plant disease using visible range image. It also comes
across several problems like boundaries of symptom, characterization,
and capture condition. Comparative analysis of challenges, problems,
and techniques are briefly discussed. The capturing condition of the
image plays a vital role in image prediction accuracy. Illumination,
specular lighting, shadow is an important factor to be considered during
the image processing technique. Intrinsic factors such as symptom seg
mentation, symptom variation, multiple simultaneous disorder, same
symptoms but the different disorder are the important challenges faced
during this process.
Park et al. (2018), have proposed a technique for feature selection
named minimum redundancy and maximum relevance technique used
for the purpose of selecting the raw bands. For classifying the hyper
spectral image deep neural network framework is used. It is also inte
grated with a fully connected network and convolution neural network.
Apple leaf is used as a sample and at six different condition leaf samples
are taken with five different spectral band length is used for analysis.
The proposed technique mainly helps in the reduction of the band for
better prediction of the leaf. The computational complexity gets reduced
during this process. Karadag et al., have proposed a technique for
detecting diseased fusarium and mycorrhizal fungus. An algorithm like
naïve Bayes, K- nearest neighbor and artificial neural network is used for
disease classification. KNN and NB show 88.12% and 82% accuracy
respectively. In a similar study, Singh and Misra (2017), have presented
an algorithm that helps in the segmentation of the image and classifi
cation process. The genetic algorithm is also for the disease detection
process. It is performed in MATLAB. Minimum distance criterion and K-
mean clustering used for classification purpose and the accuracy ob
tained is 86.54%.
Bakhshipour and Jafari (2018) proposed a technique for detecting
weed. The pattern is determined for each type of plant using an artificial
neural network and support vector machine. The accuracy of ANN is
92% and SVM is 96.67%. Ray et al. (2017) developed a system for the
detection of fungal disease at an earlier stage and identifying the disease
accurately helps in preventing the disease. It provided a review of the
present and future techniques used in the field of disease detection. The
use of biosensors in this field helps in on-field validation. Carranza et al.,
proposed a technique for herbarium species identification using deep
learning technique. It mainly focuses on how convolution neural net
works help in automatic identification of plant species. Image-Net
classification performs very well in convolution neural network pro
cess. Transfer learning is also used for domain related training. Results
show a greater accuracy when it is trained and tested for a different set
of species. It has been shown in it that by using herbarium dataset
transfer learning is possible to another region even when the species
don’t match. Handwritten tags and noise can be removed by the pre-
processing technique. The transfer learning from herbarium to non-
dried plants are clearly listed in the table.
Lu et al. (2017) proposed a technique for detecting disease in rice. A
deep convolution neural network is used for this purpose. The dataset
used for the analysis purpose consists of 500 images. Ten types of rice
disease are used for identification purposes. The accuracy achieved
during this process is 95.48%. Gan et al. (2018) performing mapping of
the yield of citrus, which is an important task. The image-based
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technique is used to find whether the fruit is ripened or not. A combi
nation of color and the thermal image is used for green fruit detection.
CTCP algorithm known as color thermal combined probability algo
rithm is used for the classification of ripened and green fruit. Before
fusing the color, the precision rate has increased from 78.1% to 90.4%.
After fusing the color, the precision rate has increased from 86.6% to
95.5%. Liang et al. (2019) conducted a severity estimation for identi
fying disease severity in the plant. A technique named PD2SE-Net has
been proposed for diagnosing the disease. The visualization and
augmentation process is also done during the identification process.
ResNet50 architecture is used as an auxiliary structure. The accuracy is
0.99 and 0.98 for disease severity.
Chapman et al. (2018) have conducted a study for which the data
were taken from oil palm plantation for predicting yield using a
Bayesian network. This network shows a higher accuracy and the r2 (0.6
and 0.9) values. Several parameters used in the Bayesian network were
explained. Soil depth was analyzed with five different classes. Azad
bakht et al. (2019) have proposed a technique for detecting the wheat
leaf rust. The leaf area index is calculated. Canopy-scale is investigated
at various levels. Machine learning techniques like boosted regression
tree, Gaussian process regression, support vector machine, and random
forest techniques are used for identification purposes. Iqbal et al. (2018)
conducted a study that mainly focuses on the citrus plant disease and the
classification of the different disease that occurs in the citrus plant. It
also gives a detailed description of the different technique used for the
segmentation process, feature extraction, feature selection, image pro
cessing, and classification method. It also discusses the automated tools
used for the detection and classification purpose. Canker, black spot,
citrus scab, melanose, gearing is the disease that occurs in the citrus
plant. The techniques used for the different stage of analysis is compared
with the existing survey for the purpose of disease extraction K-mean
algorithm is used. Back Propagation Neural Network (BPNN) and Grey
Level Co-Occurrences Matrix (GLCM) are used for color feature
computation and classification. The techniques used for pre-processing,
color based transformation, image enhancement, noise reduction and
resize and segmentation are discussed. Different feature extraction
technique based on texture, color, and shape. The summary of different
classifier technique along with its application is given in it. From the
analysis, it is clear that segmentation accuracy is improved by the pre-
processing technique.
Kaya et al. (2019) found some major issues are faced while classi
fying the data manually and they are expensive, time-consuming and
required experts for this process. Deep Neural Network is proposed for
solving this problem during the classification process. The author
analyzed four types of transfer learning method applied in the deep
neural network for the purpose of plant classification. The performance
of plant classification model is improved. Comparative analysis of
different method and its best results are listed in it like (DF-VGG16/LDA
= 99.00, DF-Alex net/LDA = 96.20, CNN-RNN = 98.80, CNN = 99.60,
(CNN, SVM = 97.47)). The proposed architecture of CNN consists of
input, 3*3 conv, ReLu, pool, 3*3 conv, ReLu, pool ReLu, FC-class size,
Softmax. The classification accuracy for each model for the training
dataset and pre-trained model is listed in it.
3. Convolutional neural network
Convolutional Neural Network generally derived under the class of
deep neural network models and it has regularized multilayer percep
tion. Overfitting of data is mainly due it’s to a fully connected network.
It has an advantage over hierarchical patterns and helps in solving
complex patterns using simple patterns. The neuron pattern in a
convolution neural network is similar to the process taking place bio
logically. During the process of image classification, it uses only small
pre-processing. Its application includes a wide area that includes
recommender systems, recognition of image and video, analysis of the
medical image, natural language processing and classification of the
image.
It takes the image as an input and then assigns different weights to
the different objects present in the image so that each object is different
from each other. When compared with other classification algorithms
the pre-processing task is low in the convolution neural network. The
neurons connected to CNN are similar to the neuron connectivity in the
human brain. It has the ability to learn the filters present in it. With the
help of the relevant filter, it captures the dependencies present in the
spatial and temporal components. It plays a key role in reducing the
image to simpler forms during the process and there is no loss in the
image during the reduction process. Some of the key application of
convolution neural network are Object Detection (R-CNN, Fast R-CNN,
and Faster R-CNN), Semantic Segmentation (Deep parsing network,
Fully convolution network), Image Captioning.
CNN architectures consists of a sequence of layer and with the help of
differentiable functions, it transforms one activation volume to another.
Layers like convolution, pooling and fully connected are mainly used to
build CNN. The convolution layer is the key block on CNN. The
computational load of the network is carried in it. It mainly performs dot
products between the kernel and the restricted portion. In the kernel, the
depth is high but smaller spatial. Only the depth will be extended in
kernel whereas the height and width remain small. The sliding of the
kernel takes place in the height and the width of the image results in
image representation (Hang et al., 2019).
Pooling layer mainly helps in spatial size reduction and it also re
duces the number of weights and computation. The network output is
obtained by the statistic of output which is nearby placed. L2 norm in the
rectangular neighborhood, Rectangular neighborhood, Weighted
average are some of the pooling functions. Max pooling is the most
popular and widely used technique. From the neighborhood maximum
output is obtained using max pooling.
Dropout is a form of regularization in neural networks to prevent it
from overfitting. (Nitish Srivastava et al., 2014) introduced this tech
nique. The important functionality of dropout is to randomly drop the
nodes from the neural network during training the model. It prevents the
nodes from adapting too much to the samples thus reduces the problem
of overfitting. The performance of the model is improved significantly
after applying this regularization method to the neural network. But,
selecting the unit of dropout is randomly assigned. The approximation of
dropout range optimizes the learning model by reducing the iterations
required to drop all unnecessary nodes (Srivastava et al., 2014). Geof
frey Hinton suggested the dropout rate for hidden units as 50% and 20%
for visible units.
Neurons present in the preceding and succeeding layers are fully
connected. The fully connected layer helps in the matrix multiplication
and bias effect. The input and output representation is mapped in the
fully connected layer. In the convolution layers, the neurons are only
connected to the local region input and shares parameter. In Fig. 1, the
general architecture of CNN is given.
Convolution process is generally linear, whereas to introduce the
non-linearity into the network, activation is placed in it. It is placed
immediately after the convolution layer. There are several non-linear
layers some of the most widely used non-linear layers are Sigmoid,
Tanh and ReLU. Sigmoid is a real-valued number and it lies between
0 and 1. The gradient value in sigmoid becomes zero when the activation
is at the tail. It will kill the gradient when the local gradient becomes
small. If the neuron is positive, then the sigmoid output will be either
positive or negative and there will be a weight update in a zigzag dy
namic manner. Tanh is a real-valued number and it lies between − 1 and
1. It has zero centered output and the activation saturate in it. ReLU
stands for Rectified Linear Unit and the compute function is f (k) = max
(0,k). Its threshold is zero at activation. The rectified linear unit
convergence by six times and it is more reliable. During the training
process, it is fragile and the neurons present in it will not update.
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4. Materials and methods
4.1. Dataset description
Plant Village Dataset contains images of disease affected leaves and
healthy samples of different varieties of crops. It is open for all the users
to conduct experiments on the data. This dataset is accessed to develop
the disease classification model for potato and tomato crops. Out of all
7500 images, 5000 samples were selected from the entire images for
training the model. These 5000 images undergone data augmentation
and 25,000 images were generated along with the original samples. For
training, 5000 original and 25,000 augmented images (totally 30,000)
are employed with 2500 test samples without augmentation for vali
dation. Early blight, late blight, are two major diseases damages the
potato crop whereas yellow leaf and leaf mould disease affected tomato
leaf samples are also considered. Moreover, equal numbers of healthy
leaf samples of both the diseases were also included in this study as one
of five classes. In Table 1, the detailed description about the dataset is
given and the sample images are represented in Table 2.
In the initial level, the images are transformed into lower-
dimensional arrays. Each image from the dataset is cropped in an
equal size of 256*256. Data augmentation is performed on the training
image to make them invariant to any type of transformation. Horizontal,
vertical and mirror symmetry is made on the images using flip transform
technique. After augmentation, the numbers of images were increased
up to 5 times than the original data.
4.2. Loss functions
Loss or cost function maps the performance of an algorithm with
evaluation methods. It is an integral part of many modern optimization
techniques of a learning model. Lowering the loss in a model can lead to
attaining better results. Softmax and Centerloss functions are two well-
performing methods used to calculate the model loss.
4.2.1. Softmax function
In most cases of a deep learning model, the softmax layer is employed
to generate final results in classification problems. Generally, this
function is useful in image-related problems such as face recognition,
biometric evaluation system as it uses distance measure to identify
discriminant features effectively (Khamparia et al., 2019). The objective
function of a softmax loss is given as:
SL = −
1
2
∑
i
log
⎛
⎜
⎝
esyi
∑
j
esj
⎞
⎟
⎠ (1)
where the sj denotes the j-th component of the classification result from
the vector s and yi indicates i-th sample’s label.
4.2.2. Centerloss function
Wen et al. proposed face recognition center loss to obtain more
discriminating features by fusing center loss with softmax loss in a deep
neural network model (Wang et al., 2018). The central loss can be
defined as,
CL = −
1
2
∑
m
i=1
⃦
⃦xi − cy
⃦
⃦2
2
(2)
Where xi represents the extracted feature from i-th vector, yi is the
label of i-th image.
The combination of softmax loss with center loss is represented as.
L = (1 − θ)SL + θCL (3)
Here, θ represents the weight, which stabilizes both the loss func
tions. Algorithm for the proposed LF-CNN is given below
Fig. 1. Architecture of CNN associated with convolution, pooling, and fully connected layer.
Table 1
Dataset information.
Crop
Name
Disease Type Number of Samples (5
Transformations)
Class
Label
Tomato Yellow Leaf
virus
1000*5 1
Tomato Leaf Mould 1000*5 2
Potato Early Blight 1000*5 3
Potato Late Blight 1000*5 4
Healthy (Tomato,
Potato)
1000*5 0
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Table 2
Sample representation of Plant Leaf images of each category.
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Fig. 2. Workflow of the proposed method.
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In Fig. 2, the workflow of the LF-CNN model is given. The fusion in
loss function reduces the error rate of the model more significantly than
a single loss error evaluation methods. The performance of this algo
rithm is effective over other CNN models. The results are compared and
evaluated in Tables 3 and 4.
5. Results and discussion
The experiments were carried out in NVIDIA GEFORCE GTX 950 M
GPU. The software modeling is made through Windows 10 Operating
system. Python programming language supports the development of the
model using Keras deep learning library. The prediction outcome of the
basic CNN model with the proposed system is evaluated in the next
section.
The performance of the model is calculated through standard vali
dation metrics. Accuracy, precision, f-score, and recall were calculated
for the model to evaluate the discrimination performance of the pro
posed model. The results were briefed in Table 5 and to visually inter
pret the variations, the values are plotted as Fig. 5.
In Figs. 3 and 4, the graph shows the accuracy and loss of the system.
During every epoch, the values were changed drastically in test data
with more fluctuations when compared with training set in both the
cases. But, at the time of the final epoch, the test set reaches optimal
results and produces better performance from the model.
For each epoch from 10 to 50, the model accuracy is calculated for
both training and test data and is projected in Table 6. Gradual
improvement has been identified over each batch of epochs in the test
set and attains optimal solution point.
Table 3
CNN Layer Information.
Layers Size of Kernel Number of Kernels
Convolutional Layer - 1 3*3 32
MaxPooling Layer – 1 3*3 –
Convolutional Layer – 2 3*3 64
MaxPooling Layer – 2 2*2 –
Convolutional Layer - 3 3*3 128
MaxPooling Layer – 3 2*2 –
Fully Connected Layer – –
Alongside, the other parameters of CNN are Feature Map (50), Activation
Function (Leaky ReLU), Learning Rate (10–4) and Dropout (0.1) is employed.
Table 4
Performance comparison of existing CNN models with the proposed system.
CNN Model Test set accuracy (%)
AlexNet 92.86
VGG16 94.13
Resnet-50 92.51
GoogleNet 95.27
Proposed Work 98.93
Table 5
Scores calculated for each model.
Model F-Score (%) Precision (%) Recall (%)
AlexNet 91.97 91.22 92.43
VGG16 92.35 92.10 93.81
Resnet-50 91.78 90.34 92.16
GoogleNet 94.12 92.98 95.98
Proposed Work 96.65 95.61 97.16
Based on the observed outcomes, the proposed model shows better results than
the previous models. The proposed model generated 98.93% accuracy, out
performed other benchmarked algorithms.
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6. Conclusion
Plant disease classification is an important activity in ensuring the
protection of the plants from havoc. Plant disease and pests have a larger
impact on food security and the environment. Biosecurity measures help
in reducing the spreading of disease. In this paper, a loss-fusion based
resilient convolution neural network model is proposed that classifies
the presence of disease in a plant leaf sample. Four crop diseases were
used to train the model with healthy leaf samples. In the next phase, the
category of disease affected the plant is classified in the fully connected
layer of the proposed CNN model. As an outcome, this model obtained
the maximum accuracy of 98.93% on discriminating the samples. The
result shows the optimality of the proposed model on its performance.
Moreover, the fusion performed on loss function in this model improved
the performance of the overall system. Thus plant disease identification
technique helps in improving the biosecurity of plants by improving the
health and life cycle of the plant. The main limitation in the proposed
system is that the model isn’t been tested under different conditions such
as illumination, occlusion, pose variation, lightning factors etc. In these
cases, the system performance might lag, but could be improved by
training the model with images captured and collected under different
conditions. In the future, more plants with different disease types will be
gathered to develop a more accurate system that could classify most of
the plant diseases.
Declaration of Competing Interest
None.
Acknowledgements
This research received no specific grant from any funding agency in
the public, commercial, or not-for-profit sectors.
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