IRJET- IoT based Preventive Crop Disease Model using IP and CNN

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1270
IoT BASED PREVENTIVE CROP DISEASE MODEL USING IP AND CNN
Vrushabh Shende1, Dr. Mahendra Pund2
1ME, CSE Department, PRMIT&R College, Maharashtra, India
2Associate Professor, CSE Department, PRMIT&R College, Maharashtra, India
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Abstract - Agriculture is the strength of Indian Economy. As
agriculture consist almost 70% of the total financeofIndia. As
like any other field, agriculture field also has to face many
problems. Agriculture is dependent on many factors like
weather, monsoon, financial, etc. the major problem that
affects the agriculture is the crop diseases that can cause to
reduce its productivity. Preventing crop diseases can help to
grow agriculture. There is no specific method to do that. Most
of the time, diseases are detected when the harm is done. As
this is technological era, many things are depending on
technology and technology has better solution on many
problems. Agriculture is the field where technology can help a
lot. To detect and preventing the diseases can be achieved
using technology.
Key Words: Technology, Agriculture
1. INTRODUCTION
Identification of the crop diseases is very main in anyfieldto
preventing the losses. Health monitoring and disease
detection on crop is very critical for sustainable agriculture.
To identify the leaf diseases mean the studies of visually
observable patterns seen on the leaf. Leaf disease detection
requires huge amount of work, knowledge in the crop
diseases, and also require the more processing time. Now a
day’s technology plays vital role in all the fields but till today
we are using some old methodologies in agriculture. If
disease is wrongly detected it will leads to a loss of yield,
time, production and money. We can use Image Processing
techniques and Convolution Neural network together for
identification of diseases.
The aim of this proposal is to develop a user friendly system
for the farmers that will help farmers in finding out diseases
of leaves without bringing an expert to the field. We are
providing remedies of diseases to the user. For this purpose
we first get the image from user then perform feature
extraction on that image. For extracting the features we use
color (HSI model). The convolution neural network isusefor
classifying the diseases. The system can uses one image
databases, 80% of the data can use for training the model
and 20% of data can use for testing the model.
2. RELATED DATA
This section of Literature Survey eventually reveals some
facts of Crop Disease Identification System based on the
analysis of many authors work as follows:
R. Newlin Shebiah [1], proposes to develop a system that
automatically finding the solutions for plant leaf disease by
analyzing the texture of leaf. At the initial stage diseases are
analyzed. The identification rate is short which needs to be
optimized to avoid the misclassification of the variable
symptoms of the plant disease.
Sateesh K.Peddoju [2], aimed to develop a mobile vision
based plant leaf recognition system which monitor crop
diseases having different patterns. It classifies its suitable
class which can be used to help the botanical students in
their research work. The feature factor used in the system
planned by the author in this work has enormous
computation cost. The quality of captured image is affected
by the shadow and season.
H. Al-Hiary [3], proposed an improved solution for
classification of leaf diseases by using algorithms like k-
means and neural networks. Otsu’s method are used in
segmentation to mark green pixels and then the boundary
pixels are removed. In proposed work clustering and
classification of diseases is done.
3. PROPOSED WORK
We aimed to develop a system which helps the farmers to
identify the diseases of crop. We are using Convolution
neural network and Image processing to give accurate and
fast result to the farmers. The main objective of this system
to detect the disease of leaf and provide the remedies for
that disease using image processing technique and
convolution neural network technique. The purpose of this
system is to provide advanced image processing tools in a
format that is easy to use and is cost efficient too. We are
using various algorithm and perform various operation for
detecting the disease of leaf. Some operation are like
converting image format to RGB to HIS, create histogram,
perform flood fill algorithm. This operation are used for
feature extraction. For detecting the disease, we are using
convolution neuron network.

Fig-1: System Service Diagram
3.1 Methodology:
3.1.1 Features Extraction:
Conversion from RGB to HIS model and flood fill algorithmis
use for feature extraction.
A. Conversion from RGB to HIS model:
Basically the input image is in format ofRGBmodel. TheRGB
model, which encodes by their intensity of color of Red,
Green, and Blue. Each color red, green and blue defines their
intensity of color by an integer value between 0 to 255. The
HSI model, which encodes colors according to their Hue,
Saturation, and Intensity. The HIS model is used by some
graphics software and image monitoring system alongside
the RGB Model.
B. Flood Fill Algorithm:
For segmentation purposed flood fill algorithm is very
important. Flood fill algorithm is also called region growing
algorithm. At a first view, it looks very simple algorithm to
implement but it has very interesting implementation for
segmenting the image. Flood fill algorithm finds the same
color area from starting point to last point with same color.
Flood fill algorithm includes 4 or 8 direction of neighbors
that check for condition to same area color and perform the
operation recursively for neighbors in case they passed that
condition.
3.1.2 Role of CNN in image classification:
After performing the features extraction, training the
database images is very important phase in Convolution
neural network. After training the module, we test the
module by 20% of images in database to check the accuracy
of the module. The accuracy of the model is depend on the
size of database, higher the size of database higher the
accuracy of the model. So if the accuracy is low, increase the
database size. Convolution neural network acceptall thenew
images for image classification and for that there is no need
to train the model every time. CNN has a different layers of
receptive fields. For detect the disease of leaf we providethe
image to train model by convolution neural network and
model predict the disease of leaf. After finding the disease,
we provide the remedies of this disease.
3.2 System Components:
The system components are as follows:
1. Data acquisition subsystem then converting to required
format
2. Image processing and filtration thus identification of
data to be extracted

3. Train and Test the data for CNN
4. Identification process(CNN)- Existence of types of
diseases
5. Matching with symptoms database and providing
solution
6. Sending data to Farmer through web technology
4. CONCLUSION
In this paper, we study the system which can automatically
recognize, identify and detect crop diseases. Sensor device
such as camera or smartphone has a key role in collecting
pictures of plants for the checking scheme. This work will
help farmer to utilize their time and work. And can reduce
the efforts of farmer. Proposed work can expressively
support a correct detection of leaf infections in a little
computational strength.
REFERENCES
1. S. Arivazagan, R. Newlin Shebiah*, “Detection of
unhealthy region of plant leaves and classification
of plant leaf diseases using texture features”, CIGR
journal March-2013.
2. Shitala Prasad • Sateesh K. Peddoju • Debashis
Ghosh “Multi-resolution mobile vision system for
plant leaf disease Diagnosis”, Springer-Verlag
London 2015
3. H. Al-Hiary , Z, M. Braik and S. Bani-Ahmad,” Fast
and Accurate Detection and classification of Plant
Diseases” International Journal of Computer
Applications (0975 – 8887) Volume 17– No.1,
March 2011.
4. Jyotismita Chaki,, Ranjan Parekh Plant Leaf
Recognition using Shape basedFeaturesandNeural
Network classifiers.
5. Mr. Sachin B. Jagtap1, Mr. S.M. Hambarde2
“Agricultural Plant Leaf Disease Detection and
Diagnosis Using ImageProcessing Based on
Morphological Feature Extraction” university Of
Pune , Jspm’s Jscoe, Pune, India.
6. Sindhuja Sankarana, Ashish Mishraa, Reza
Ehsania,∗, Cristina DavisbA review of advanced
techniques for detecting plant diseases.
7. 1Suneeta Budihal, 2Sandhya R., 3Soumya D
Hajawagol, 4Soumya R Navi B.V.B.C.E.T., Hubli,
India,” Detection of Disease in Tomato Leaf”.
8. C. Stroutopolos, I. Andreadis, Multithresholding of
color and gray-level images through a neural
network.
9. Karimi, Kaivan, and Gary Atkinson. "What the IoT
wants to become a reality." White Paper, FreeScale
and ARM (2013).
10. Gubbi, Jayavardhana, et al. "IoT: Architectural
elements ,a vision and future directions." Future
Generation Computer Systems 29.7 (2013): 1645-
1660
11. "Understanding the Internet of Things (IoT) “, July
2014.
12. P. Y. Simard, D. Steinkrans and J .C. Platt, “Best
Practice for Convolutional Neural Network Applied
to Visual Document Analysis”, The 7th ICDAR,
(2003).
13. B. Kowlek, “Detection Face Using CNN and Gabor
Filters, Artificial Neural Networks”, Biological
Inspirations, vol.3696, (2005)
14. Doug Tidwell, James Snell, Pavel Kulchenko
“Programming Web Services with SOAP”, First
edition, December 2001.

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