Plant Disease Detection Using
Convolutional Neural Network
Abstract
When plants and crops are affected by pests it
affects the agricultural production of the country. Usually farmers or experts
observe the plants with naked eye for detection and identification of disease.
But this method can be time processing, expensive and inaccurate. Automatic
detection using image processing techniques provide fast and accurate results.
This paper is concerned with a new approach to the development of plant disease
recognition model, based on leaf image classification, by the use of deep
convolutional networks. Advances in computer vision present an opportunity to
expand and enhance the practice of precise plant protection and extend the
market of computer vision applications in the field of precision agriculture.
Novel way of training and the methodology used facilitate a quick and easy
system implementation in practice. All essential steps required for
implementing this disease recognition model are fully described throughout the
paper, starting from gathering images in order to create a database, assessed
by agricultural experts, a deep learning framework to perform the deep CNN
training. This method paper is a new approach in detecting plant diseases using
the deep convolutional neural network trained and fine-tuned to fit accurately to
the database of a plant’s leaves that was gathered independently for diverse
plant diseases. The advance and novelty of the developed model lie in its
simplicity; healthy leaves and background images are in line with other
classes, enabling the model to distinguish between diseased leaves and healthy
ones or from the environment by using CNN.
The problem of efficient plant
disease protection is closely related to the problems of sustainable
agriculture Inexperienced pesticide usage can cause the development of
long-term resistance of the pathogens, severely reducing the ability to fight
back. Timely and accurate diagnosis of plant diseases is one of the pillars of
precision agriculture. It is crucial to prevent unnecessary waste of financial
and other resources, thus achieving healthier production in this changing
environment, appropriate and timely disease identification including early
prevention has never been more important. There are several ways to detect
plant pathologies. Some diseases do not have any visible symptoms, or the
effect becomes noticeable too late to act, and in those situations, a
sophisticated analysis is obligatory. However, most diseases generate some kind
of manifestation in the visible spectrum, so the naked eye examination of a
trained professional is the prime technique adopted in practice for plant
disease detection. In order to achieve accurate plant disease diagnostics a
plant pathologist should possess good observation skills so that one can identify
characteristic symptoms. Variations in symptoms indicated by diseased plants
may lead to an improper diagnosis since amateur gardeners and hobbyists could
have more difficulties determining it than a professional plant pathologist. An
automated system designed to help identify plant diseases by the plant’s
appearance and visual symptoms could be of great help to amateurs in the
gardening process and also trained professionals as a verification system in
disease diagnostics. Advances in computer vision present an opportunity to
expand and enhance the practice of precise plant protection and extend the
market of computer vision applications in the field of precision agriculture.
Exploiting common digital image processing techniques such as colour analysis
and thresholding were used with the aim of detection and classification of
plant diseases. In machine learning and cognitive science, ANN is an
information-processing paradigm that was inspired by the way biological nervous
systems, such as the brain, process information. Neural networks or
connectionist systems are a computational approach used in computer science and
other research disciplines, which is based on a large collection of neural
units (artificial neurons), loosely mimicking the way a biological brain solves
problems with large clusters of biological neurons connected by axons. Each
neural unit is connected with many others, and links can be enforcing or
inhibitory in their effect on the activation state of connected neural units.
Each individual neural unit may have a summation function which combines the
values of all its inputs together. There may be a threshold function or
limiting function on each connection and on the unit itself, such that the
signal must surpass the limit before propagating to other neurons. These
systems are self-learning and trained, rather than explicitly programmed, and
excel in areas where the solution or feature detection is difficult to express
in a traditional computer program. Neural networks typically consist of
multiple layers or a cube design, and the signal path traverses from front to
back. Back propagation is the use of forward stimulation to reset weights on
the "front" neural units and this is sometimes done in combination
with training where the correct result is known. More modern networks are a bit
more free flowing in terms of stimulation and inhibition with connections
interacting in a much more chaotic and complex fashion. Dynamic neural networks
are the most advanced, in that they dynamically can, based on rules, form new
connections and even new neural units while disabling others.
The goal of the neural network is to solve problems in the same
way that the human brain would, although several neural networks are more
abstract. Modern neural network projects typically work with a few thousand to
a few million neuralunits and millions of connections, which are still several
orders of magnitude less complex than the human brain and closer to the
computing power of a worm. New brain research often stimulates new patterns in
neural networks. One new approach is using connections which span much further
and link processing layers rather than always being localized to adjacent
neurons. Other research being explored with the different types of signal over
time that axons propagate, such as Deep Learning, interpolates greater
complexity than a set of Boolean variables being simply on or off. Their inputs
can also take on any value between 0 and 1. Also, the neuron has weights for
each input and an overall bias. The weights are real numbers expressing
importance of the respective inputs to the output. The bias is used for
controlling how easy the neuron is getting to output 1. For a neuron with
really big bias it is easy to output 1, but when the bias is very negative then
it is difficult to output 1.
Materials and Methods:-
Ø The
Dataset:-
The Dataset was taken from kaggle of PlantVillage dataset
present online as such the code was also written on the online kernel of Kaggle
for better computation and analysis of training loss and validation.
Ø Image
Preprocessing and Labelling:
Preprocessing images commonly
involves removing low-frequency background noise, normalizing the intensity of
the individual particles images, removing reflections, and masking portions of
images. Image preprocessing is the technique of enhancing data Furthermore,
procedure of image preprocessing involved cropping of all the images manually,
making the square around the leaves, in order to highlight the region of
interest (plant leaves). During the phase of collecting the images for the
dataset, images with smaller resolution and dimension less than 500 pixels were
not considered as valid images for the dataset. In addition, only the images
where the region of interest was in higher resolution were marked as eligible
candidates for the dataset. In that way, it was ensured that images contain all
the needed information for feature learning. Many resources can be found by
searching across the Internet, but their relevance is often unreliable. In the
interest of confirming the accuracy of classes in the dataset, initially
grouped by a keywords search, agricultural experts examined leaf images and
labeled all the images with appropriate disease acronym. As it is known, it is
important to use accurately classified images for the training and validation
dataset. Only in that way may an appropriate and reliable detecting model be
developed. In this stage, duplicated images that were left after the initial
iteration of gathering and grouping images into classes were removed from the
dataset.
Ø Neural
Network Training :-
Training the deep convolutional
neural network for making an image classification model from a dataset was
proposed. Tensor Flow is an open source software library for numerical
computation using data flow graphs. Nodes in the graph represent mathematical
operations, while the graph edges represent the multidimensional data arrays
(tensors) communicated between them. The flexible architecture allows you to
deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile
device with a single API. Tensor Flow was originally developed by researchers
and engineers working on the Google Brain Team within Google's Machine
Intelligence research organization for the purposes of conducting machine
learning and deep neural networks research, but the system is general enough to
be applicable in a wide variety of other domains as well. In machine learning,
a convolutional neural network is a type of feed-forward artificial neural
network in which the connectivity pattern between its neurons is inspired by
the organization of the animal visual cortex. Individual cortical neurons
respond to stimuli in a restricted region of space known as the receptive
field. The receptive fields of different neurons partially overlap such that
they tile the visual field. The response of an individual neuron to stimuli
within its receptive field can be approximated mathematically by a convolution
operation. Convolutional networks were inspired by biological processes and are
variations of multilayer perceptron designed to use minimal amounts of
pre-processing. They have wide applications in image and video recognition,
recommender systems and natural language processing. Convolutional neural
networks (CNNs) consist of multiple layers of receptive fields. These are small
neuron collections which process portions of the input image. The outputs of
these collections are then tiled so that their input regions overlap, to obtain
a higher-resolution representation of the original image; this is repeated for
every such layer. Tiling allows CNNs to tolerate translation of the input
image. Convolutional networks may include local or global pooling layers, which
combine the outputs of neuron clusters. They also consist of various
combinations of convolutional and fully connected layers, with point wise
nonlinearity applied at the end of or after each layer. A convolution operation
on small regions of input is introduced to reduce the number of free parameters
and improve generalization.One major advantage of convolutional networks is the
use of shared weight in convolutional layers, which means that the same filter
(weights bank) is used for each pixel in the layer; this both reduces memory
footprint and improves performance. The layer’s parameters are comprised of a
set of learnable kernels which possess a small receptive field but extend
through the full depth of the input volume. Rectified Linear Units (Re LU) are
used as substitute for saturating nonlinearities. This activation function
adaptively learns the parameters of rectifiers and improves accuracy at
negligible extra computational cost. In the context of artificial neural
networks, the rectifier is an activation function defined as:
f (x)=max(0,x)
,where x is the input to a neuron.
This is also known as a ramp function and is analogous to half-wave
rectification in electrical engineering. This activation function was first
introduced to a dynamical network by Hahn loser et al. in a 2000 paper in
Nature with strong biological motivations and mathematical justifications. It
has been used in convolutional networks more effectively than the widely used
logistic sigmoid (which is inspired by probability theory; see logistic regression)
and its more practical counterpart, the hyperbolic tangent. The rectifier is,
as of 2015, the most popular activation function for deep neural networks. Deep
CNN with ReLUs trains several times faster. This method is applied to the
output of every convolutional and fully connected layer. Despite the output,
the input normalization is not required; it is applied after ReLU nonlinearity
after the first and second convolutional layer because it reduces top-1 and
top-5 error rates. In CNN, neurons within a hidden layer are segmented into
“feature maps.” The neurons within a feature map share the same weight and
bias. The neurons within the feature map search for the same feature. These
neurons are unique since they are connected to different neurons in the lower
layer. So for the first hidden layer, neurons within a feature map will be
connected to different regions of the input image. The hidden layer is
segmented into feature maps where each neuron in a feature map looks for the
same feature but at different positions of the input image. Basically, the
feature map is the result of applying convolution across an image. The
convolutional layer is the core building block of a CNN. The layer's parameters
consist of a set of learnable filters (or kernels), which have a small
receptive field, but extend through the full depth of the input volume. During
the forward pass, each filter is convolved across the width and height of the
input volume, computing the dot product between the entries of the filter and
the input and producing a 2-dimensional activation map of that filter. As a
result, the network learns filters that activate when it detects some specific
type of feature at some spatial position in the input. Stacking the activation
maps for all filters along the depth dimension forms the full output volume of
the convolution layer. Every entry in the output volume can thus also be
interpreted as an output of a neuron that looks at a small region in the input
and shares parameters with neurons in the same activation map. When dealing
with high-dimensional inputs such as images, it is impractical to connect
neurons to all neurons in the previous volume because such network architecture
does not take the spatial structure of the data into account. Convolutional networks
exploit spatially local correlation by enforcing a local connectivity pattern
between neurons of adjacent layers: each neuron is connected to only a small
region of the input volume. The extent of this connectivity is a hyper
parameter called the receptive field of the neuron. The connections are local
in space (along width and height), but always extend along the entire depth of
the input volume. Such architecture ensures that the learnt filters produce the
strongest response to a spatially local input pattern. Three hyper parameters
control the size of the output volume of the convolutional layer: the depth,
stride and zero-padding.
1. Depth of the output volume controls the
number of neurons in the layer that connect to the same region of the input
volume. All of these neurons will learn to activate for different features in
the input. For example, if the first Convolutional Layer takes the raw image as
input, then different neurons along the depth dimension may activate in the
presence of various oriented edges, or blobs of color.
2. Stride controls how depth columns
around the spatial dimensions (width and height) are allocated. When the stride
is 1, a new depth column of neurons is allocated to spatial positions only 1
spatial unit apart. This leads to heavily overlapping receptive fields between
the columns, and also to large output volumes. Conversely, if higher strides
are used then the receptive fields will overlap less and the resulting output
volume will have smaller dimensions spatially.
3. Stride controls how depth columns around the spatial dimensions
(width and height) are allocated. When the stride is 1, a new depth column of
neurons is allocated to spatial positions only 1 spatial unit apart. This leads
to heavily overlapping receptive fields between the columns, and also to large
output volumes. Conversely, if higher strides are used then the receptive
fields will overlap less and the resulting output volume will have smaller
dimensions spatially.
Parameter sharing scheme is used in convolutional layers to
control the number of free parameters. It relies on one reasonable assumption:
That if one patch feature is useful to compute at some spatial position, then
it should also be useful to compute at a different position. In other words,
denoting a single 2-dimensional slice of depth as a depth slice, we constrain
the neurons in each depth slice to use the same weights and bias. Since all
neurons in a single depth slice are sharing the same parameterization, then the
forward pass in each depth slice of the CONV layer can be computed as a
convolution of the neuron's weights with the input volume (hence the name:
convolutional layer). Therefore, it is common to refer to the sets of weights
as a filter (or a kernel), which is convolved with the input. The result of
this convolution is an activation map, and the set of activation maps for each
different filter are stacked together along the depth dimension to produce the
output volume. Parameter Sharing contributes to the translation invariance of
the CNN architecture. It is important to notice that sometimes the parameter
sharing assumption may not make sense. This is especially the case when the
input images to a CNN have some specific centred structure, in which we expect
completely different features to be learned on different spatial locations. One
practical example is when the input is faces that have been centred in the
image: we might expect different eye-specific or hair-specific features to be
learned in different parts of the image. In that case it is common to relax the
parameter sharing scheme, and instead simply call the layer a locally connected
layer. Another important layer of CNNs is the pooling layer, which is a form of
nonlinear down sampling.
Pooling operation gives the form of translation invariance; it
operates independently on every depth slice of the input and resizes it
spatially. Overlapping pooling is beneficially applied to lessen over fitting.
Also in favour of reducing over fitting, a dropout layer is used in the first
two fully connected layers. But the shortcoming of dropout is that it increases
training time 2-3 times comparing to a standard neural network of the exact
architecture. Bayesian optimization experiments also proved that ReLUs and
dropout have synergy effects, which means that it is advantageous when they are
used together. The advance of CNNs refers to their ability to learn rich
mid-level image representations as opposed to hand-designed low-level features
used in other image classification methods.
ResultsAnd Conclusion
The Results presented in this section are related to training with
the whole database containing both original and augmented images. As it is
known that convolutional networks are able to learn features when trained on
larger datasets, results achieved when trained with only original images will
not be explored. After fine-tuning the parameters of the network, an overall
accuracy of 96.77% was achieved.Furthermore, the trained model was tested on
each class individually. Test was performed on every image from the validation
set. As suggested by good practice principles, achieved results should be
compared with some other results. In addition, there are still no commercial
solutions on the market, except those dealing with plant species recognition
based on the leaves images. In this paper, a approach of using deep learning
method was explored in order to automatically classify and detect plant dise ases from leaf images. The complete
procedure was described, respectively, from collecting the images used for
training and validation to image pre-processing and augmentation and finally
the procedure of training the deep CNN and fine-tuning. Different tests were
performed in order to check the performance of newly created model. As the
presented method has not been exploited, as far as we know, in the field of
plant disease recognition, there was no comparison with related results, using
the exact technique.
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