CHAPTER 1

INTRODUCTION

India is an agricultural country wherein most of the population depends on agriculture.

Research in agriculture is aimed towards increase of productivity and food quality at reduced

expenditure, with increased profit. Agricultural production system is an outcome of a

complex interaction of soil, seed, and agro chemicals. Vegetables and fruits are the most

important agricultural products. In order to obtain more valuable products, a product quality

control is basically mandatory. Many studies show that quality of agricultural products may

be reduced due to plant diseases. Diseases are impairment to the normal state of the plant that

modifies or interrupts its vital functions such as photosynthesis, transpiration, pollination,

fertilization, germination etc. These diseases are caused by pathogens viz., fungi, bacteria and

viruses, and due to adverse environmental conditions. Therefore, the early stage diagnosis of

plant disease is an important task. Farmers require continuous monitoring of experts which

might be prohibitively expensive and time consuming. Therefore looking for fast, less

expensive and accurate method to automatically detect the diseases from the symptoms that

appear on the plant leaf is of great realistic significance. This enables machine vision that is

to provide image based automatic inspection, process control and robot guidance. The

objective of this paper is to concentrate on the plant leaf disease detection based on the

texture of the leaf. Leaf presents several advantages over flowers and fruits at all seasons

worldwide.

1.1. Plant diseases analysis and its symptoms

The RGB image feature pixel counting techniques is extensively applied to agricultural

science. Image analysis can be applied for the following purposes:

1. To detect plant leaf, stem, and fruit diseases.

2. To quantify affected area by disease.

3. To find the boundaries of the affected area.

4. To determine the color of the affected area

5. To determine size & shape of fruits.

Following are some common symptoms of fungal, bacterial and viral plant leaf diseases

1

1.2 Bacterial disease symptoms

The disease is characterized by tiny pale green spots which soon come into view as water-

soaked. The lesions enlarge and then appear as dry dead spots as shown in figure 1(a), e.g.

bacterial leaf spot have brown or black water-soaked spots on the foliage, sometimes with a

yellow halo, generally identical in size. Under dry conditions the spots have a speckled

appearance.

1.3 Viral disease symptoms

Among all plant leaf diseases, those caused by viruses are the most difficult to diagnose.

Viruses produce no telltale signs that can be readily observed and often easily confused with

nutrient deficiencies and herbicide injury. Aphids, leafhoppers, whiteflies and cucumber

beetles insects are common carriers of this disease, e.g. Mosaic Virus, Look for yellow or

green stripes or spots on foliage, as shown in figure 1(b). Leaves might be wrinkled, curled

and growth may be stunted.

(A) Bacterial leaf spot (B) Mosaic virus

Figure 1: Bacterial and Viral disease on leaves

2

1.4 Fungal disease symptoms

Among all plant leaf diseases, those caused by fungus some of them are discussed below and

shown in figure 2, e.g. Late blight caused by the fungus Phytophthora infesters shown in

figure 2(a). It first appears on lower, older leaves like water-soaked, gray-green spots. When

fungal disease matures, these spots darken and then white fungal growth forms on the

undersides. Early blight is caused by the fungus Alternariasolani shown in figure 2(b). It first

appears on the lower, older leaves like small brown spots with concentric rings that form a

bull’s eye pattern. When disease matures, it spreads outward on the leaf surface causing it to

turn yellow. In downy mildew yellow to white patches on the upper surfaces of older leaves

occurs. These areas are covered with white to greyish on the undersides as shown in figure

2(c).

(A) Late blight (B) Early blight

(C) Downy mildew

Figure 2: Fungal disease on leaves

3

Farmers have wide range of diversity to select suitable Fruit and Vegetable crops. However,

the cultivation of these crops for optimum yield and quality produce is highly technical. It can

be improved by the aid of technological support. The management of perennial fruit crops

requires close monitoring especially for the management of diseases that can affect

production significantly and subsequently the post-harvest life. The image processing can be

used in agricultural applications for following purposes:

1. To detect diseased leaf, stem, fruit

2. To quantify affected area by disease.

3. To find shape of affected area.

4. To determine colour of affected area

5. To determine size & shape of fruits.

In case of plant the disease is defined as any impairment of normal physiological function of

plants, producing characteristic symptoms. A symptom is a phenomenon accompanying

something and is regarded as evidence of its existence. Disease is caused by pathogen which

is any agent causing disease. In most of the cases pests or diseases are seen on the leaves or

stems of the plant. Therefore identification of plants , leaves, stems and finding out the pest

or diseases, percentage of the pest or disease incidence , symptoms of the pest or disease

attack, plays a key role in successful cultivation of crops.

It is found that diseases cause heavy crop losses amounting to several billion dollars annually.

1.5 Objective

Looking at the current situation of the farmers and the agriculture development status of our

country,we thought of contributing towards its development by working on this topic.Our

objective is to make it easier for the farmers to detect the disease that affect the crops.Also,to

make the spraying and use of pesticides optimal and easier, we are going to design the system

which will serve to its best content.

4

CHAPTER 2

LITERATURE SURVEY

In this chapter, we have written about the literature survey that we did on monitoring if plant

leaf diseases and pesticide management.

An Overview of the research on plant disease detection using image processing

technique: The present paper reviews and summarizes image processing techniques for

several plant species that have been used for recognizing plant diseases. The major

techniques for detection of plant diseases are: BPNN, SVM, K-means clustering, and SGDM.

These techniques are used to analyses the healthy and diseased plants leaves. Some of the

challenges in these techniques viz. effect of background data in the resulting image,

optimization of the technique for a specific plant leaf diseases, and automation of the

technique for continuous automated monitoring of plant leaf diseases under real world field

conditions. The review suggests that this disease detection technique shows a good potential

with an ability to detect plant leaf diseases and some limitations. Therefore, there is scope of

improvement in the existing research.

There are five main steps used for the detection of plant leaf diseases. The processing scheme

consists of image acquisition through digital camera or web, image pre-processing includes

image enhancement and image segmentation where the affected and useful area are

segmented, feature extraction and classification. Finally the presence of diseases on the plant

leaf will be identified. In the initial step, RGB images of leaf samples were picked up. The

step-by-step procedure as shown below:

1) RGB image acquisition;

2) Convert the input image into color space;

3) Segment the components;

4) Obtain the useful segments;

5) Computing the texture features;

6) Configuring the neural networks for recognition.

Image acquisition: Firstly, the images of various leaves acquired using a digital camera

with required resolution for better quality. The construction of an image database is clearly

dependent on the application. The image database itself is responsible for the better efficiency

of the classifier which decides the robustness of the algorithm.

5

Image pre-processing: In the second step, this image is pre-processed to improve the image

data that suppress undesired distortions, enhances some image features important for further

processing and analysis task. It includes color space conversion, image enhancement, and

image segmentation. The RGB images of leaves are converted into color space

representation. The purpose of the color space is to facilitate the specification of colors in

some standard accepted way. RGB images converted into Hue Saturation Value (HSV) color

space representation. Because RGB is for color generation and his for color descriptor. HSV

model is an ideal tool for color perception. Hue is a color attribute that describes pure color as

perceived by an observer. Saturation termed as relative purity or the amount of white light

added to hue and value means amplitude of light. After the color space transformation

process, hue component used for further analysis. Saturation and value are dropped since it

does not give extra information.

Feature extraction: After segmentation the area of interest i.e. diseased part extracted. In the

next step, significant features are extracted and those features can be used to determine the

meaning of a given sample. Actually, image features usually includes color, shape and texture

features. Currently most of the researchers targeting plant leaf texture as the most important

feature in classifying plants. With the help of texture features, plant diseases are classified

into different types.

Classifier: A software routine was written in MATLAB. In which training and testing

performed via several neural network classifier. [1]

Advances In Image Processing For Detection Of Plant Diseases :This paper provides a

new insight in detection of the disease.This paper gives the detection of plant diseases by

using parts of plants such as leaf,roots,stem,etc. It is found that the following methods are

used by different researchers for plant disease detection & analysis:

1.Back propagation neural network.

2.Airbornehyperspectral imagery & red edge techniques.

3.Image analysis integrated with Central Lab. Of Agricultural Expert System (CLASE )

diagnostic model.

4.Combination of morphological features of leaves, image processing ,feed forward neural

network based classifier & fuzzy surface selection technique for feature selection.

5.Support vector machines for developing weather based prediction models of plant diseases.

6.Wavelet based image processing technique and neural network.

7.Image Processing with PCA & Probabilistic.

6

The literature survey done in this paper provides a new insight in detection of the diseases of

plant. The scope in doing research in this field is as follow:

1.There are two main characteristics of plant disease detection using machine-learning

methods that must be achieved, they are: speed and accuracy. Hence there is a scope for

working on development of innovative, efficient & fast interpreting algorithms which will

help plant scientist in detecting disease.

2. Work can be done for automatically estimating the severity of the detected disease.

3. Work proposed by researcher Yao can be extended for development of hybrid algorithms

such as genetic algorithms & neural networks in order to increase the recognition rate of the

final classification process. [2]

2011 2012 2013 20140

0.5

1

1.5

2

2.5

3

3.5

4

Work doneDetection rate

Figure 3: Bar Graph of the Literature Survey

7

PC (Disease Detection)

Micro Controller

89s52

Max232

Relay driver Relay

Flow sensor

LCD

Figure 4:Monitoring of Plant Leaf Diseases And Pesticides Management

CHAPTER 3

DESIGN AND DRAWING

3.1 INTRODUCTION

Design is one of the most important part of our project. We have to go for step by step

designing, drawing, implementation and experimentation.

3.2 BLOCK DIAGRAM

Explanation:

The picture of the leaf will be taken with the help of the camera. This picture will be saved

into the PC and plant disease is identified by image processing with the help of MATLAB

coding. This information will be given to the microcontroller by serial communication using

MAX232. With this information it will also send the information as to which pesticide is to

be sprayed on the crops thereby stating which valve is to be opened. Microcontroller will

decide the time for which the valves should be open. For the working of the valves the relays

8

are used. The flow sensors will continuosly monitor the flow of the pesticide from the tanks

to the field and keep the farmers updated with the information of the same.The output of flow

sensor is feeded back to the micro-controller which will process this and display amount of

pesticide on LCD.Thus our proposed project can ensure complete atomization as regards the

protection of farms from pests.

3.3 CIRCUIT DIAGRAM

Figure 5: Circuit Diagram

9

Figure 6: Power Supply

EXPLANATION

Following components are used in the above circuit:

1.Micro Controller AT89S52

We use a micro controller as it provides on chip microprocessor, RAM, ROM, Parallel I/O

port, Serial I/O port etc. hence its cost is less, size is less, power consumption is less and

speed is more. Software development tools like assembler, C compilers etc are easily

available and are easy to upgrade. In the parking sensor circuit, we have used microcontroller

89s52. Because of the following reasons it makes it more efficient.

•8K Bytes of In-System Reprogrammable Flash Memory 

•256x8-bit Internal RAM

•Three 16-bit Timer/Counters 

•Eight Interrupt Sources

2.Regulator

The regulator (7805) provides circuit designers with an easy way to regulate DC voltages to

5v. Here 78 stand for positive and 05 stands for 5 volts. The 7805 is a positive voltage DC

regulator that has only 3 terminals. They are: Input voltage, Ground, Output Voltage.

10

General Features

Output Current up to 1A

Short Circuit Protection

Thermal Overload Protection

3.Capacitors

A capacitor or condenser is a passive electronic component consisting of a pair of

conductors separated by a dielectric. When a voltage potential difference exists between the

conductors, an electric field is present in the dielectric. This field stores energy and produces

a mechanical force between the plates.

In this circuit our capacitor is used to remove ripples. In this we have used both electrolytic

and ceramic capacitor of various ratings.

4.Resistors

A resistor is a two-terminal electronic component that produces a voltage across its terminals

that is proportional to the electric current through it in accordance with Ohm’s law.

Resistors of various ratings are used in this circuit. Resistance is used in front of led to drop

the voltage from 5v which is coming from microcontroller to 3v which is required by the led

to glow.

11

5.Transistors

In this we have used NPN and PNP transistors. NPN transistor will be used to turn the motor

on and PNP to convert negative voltage to positive voltage.

6.Crystal oscillator

A crystal oscillator is an electronic circuit that uses the mechanical resonance of a vibrating

crystal of piezoelectric material to create an electrical signal with a very precise frequency.

This frequency is commonly used to keep track of time to provide a stable clock signal for

digital integrated circuits.

7.Diodes

A Diode is a semiconductor device, which allows the current to flow easily in one direction,

and provides a very high resistance when the current flows in the reverse direction. The

direction in which current flow easily, with little resistance, is called forward direction and

the opposing direction is called reverse direction.

Figure 7: Diode

12

A diode has two leads, anode and cathode. The conventional current can flow from anode to

the cathode but will face very high resistance when tries to flow from cathode to the anode.

So, allows the current to flow easily in one direction. The direction in which current flow

easily, with little resistance, is called forward direction and the opposing direction is called

reverse direction. The cathode is often marked by a band at one end.

8.Regulator Circuit

This final DC output when given to equipment must provide a constant DC supply. But the

DC output from the filter circuit changes according to change in the load value or according

to change in the input AC mains voltage.

To keep this DC output constant irrespective of change in input AC mains voltage and the

load, a circuit known as regulator circuit is used. This regulator is the last block in the power

supply. The output of the regulator will be a constant DC voltage, which can be used to

power the required equipment.

9.Voltage Regulator

The Digital board can use any power supply that creates a DC voltage between 6 and 12

volts. A 5V voltage regulator (7805) is used to ensure that no more than 5V is delivered to

the Digital board regardless of the voltage present at the J12 connector (provided that voltage

is less than 12VDC). The regulator functions by using a diode to clamp the output voltage at

5VDC regardless of the input voltage – excess voltage is converted to heat and dissipated

through the body of the regulator. If a DC supply of greater than 12V is used, excessive heat

will be generated, and the board may be damaged. If a DC supply of less than 5V is used,

insufficient voltage will be present at the regulator output.

Figure 8: Voltage Regulator

13

10.Crystal Circuit

In AT 89C51 two pins viz. Pin no 18 & 19 (XTAL1 & XTAL2) are provided for connecting

a resonant network to form an oscillator. A quartz crystal is used with ceramic capacitors as

shown in Fig 4.4. The crystal frequency is the basic internal frequency of the micro

controller. The range of the crystal that can be connected to the micro controller is 1Mhz to

16 MHz. Different crystals are available such as the Quartz, Rochelle salts, and Tourmaline

etc. the system uses Quartz crystal because it is inexpensive and readily available.

Figure 9: Crystal circuit

C1 and C2 are between 10pF to 40 pF. The capacitors C1, C2 are used for stable frequency

operation i.e. in the condition where there is high noise and humidity.

Generally quartz crystal and ceramic capacitors are used for the purpose. The crystal

frequency is the basic internal frequency of micro controller. The requirement of crystal

frequency ranges from 1 MHZ to 16 MHZ. Minimum frequencies imply that some internal

memories are dynamic and must always operate above a minimum frequency or data will be

lost. Serial data communication needs often dictate the frequency of the oscillator, because of

the requirement that internal counters must divide the basic clock rate to yield standard

communications bit per seconds rates.(i.e. baud rates).If the basic clock frequency is not

divisible without remainder, then the resulting communication frequency is not standard. The

oscillator formed by the crystal capacitors and an on-chip inverter generates a pulse train at

the frequency of the crystal. It is shown in above figure. When we use quartz crystal with

value 11.0592 MHZ, we get cycle frequency as 921.6 KHZ, which is divisible by the

standard communication baud rates of 19200, 9600,4800,2400,1200 and 300HZ.

14

11.Power Supply Calculations

The basic step in the designing of any system is to design the power supply required for that

system. The steps involved in the designing of the power supply are as follows:

1) Determine the total current that the system sinks from the supply.

2) Determine the voltage rating required for the different components.

Figure 10: Power supply

Transformer selection we required 12V for relay.

Min Input for 7805 is

= Drop across IC 7805 + Required Output voltage

= 3 V+ 5V

= 8 V

So at Input of 7805 we required 8 V with margin

Consider drop across diode 0.7V so 2 diode conducts drop is 1.4 V

= 1.4 V +8 V

= 9.4 V

So at secondary we required 10 V

For filter capacitor design

C= (Il * t1)/Vr

Vr= ripple voltage

Il = load current

T1= time during which the capacitor being discharge by load current

15

θ1= sin-1[(E0 min)/ (E0 max)]

So unregulated power supply is design for 10 V

Vr = ripple voltage 10% of output voltage

Vr = 1.0 V

E0 min/E0 max = (10-0.7) / 10+0.7

= 9.3 / 10.7

θ1 = sin-1 [9.3/10.7]

= 60°

Frequency 50 HZ

T1 = 1/50 = 20 ms

T for 360° = 20ms

For 180° = 10ms

For 60° = 20ms * (60°/360)

= 3.4ms

For bridge

T1 = [time for 90° + time for θ1]

= 5ms + 3.4ms

= 8.4ms

Il = load current supplied to various IC

Il = (O/P current of IC 89c51 +

O/P current of IC 232 +

Current req. for display)

= 71mA + 30mA + 15.2 mA

=116.2 mA

C = (Il * t1)/Vr

= (116.2 mA * 8.4 ms)/ 1 V

= 976.04 µf

So we select V 1000 µf capacitor

For diode design

PIV = Vm

Vm = E0 max + 2 Vf

16

= 10.7 + 1.4 V

= 12.1 V

I0 = Il/2

= 116.2 mA/ 2

= 58.1 mA

Peak repetitive current

Ifm = [Il (t1+t2)]/t2

T2 = time for 90° - time for θ1

= 5ms - 3.4ms

=1.2ms

Ifm = 116.2mA (8.6ms+1.2ms) /1.2ms.

=833mA

From above specification diode 1N4007 is selected

PIV =100V

I = 1A

Reasons for choosing Bridge rectifier are

a)The TUF is increased to 0.812 as compared the full wave rectifier.

b)The PIV across each diode is the peak voltage across the load =Vm, not 2Vm as in the two

diode rectifier

Output of the bridge rectifier is not pure DC and contains some AC some AC ripples in it. To

remove these ripples we have used capacitive filter, which smoothens the rippled out put that

we apply to 7805 regulators IC that gives 5V DC. We preferred to choose capacitor filters

since it is cost effective, readily available and not too bulky.

The value of the capacitor filter can be found by following formula,

C=IL*t1 / Vr

17

12. 16*2 LCD and its Interfacing with Micro controller

Advantages of LCD over LED display

It can display numbers, characters and graphics, whereas LED displays are limited to

numbers and few characters.

LCD has its own processor, so there is no need for refreshing it through micro controller.

Ease of programming for characters and graphics

It is cost effective.

Figure11: LCD display

Artificial neural networks

One type of network sees the nodes as ‘artificial neurons’. These are called artificial neural

networks (ANNs). An artificial neuron is a computational model inspired in the natural

neurons. Natural neurons receive signals through synapses located on the dendrites or

membrane of the neuron. When the signals received are strong enough (surpass a certain

threshold), the neuron is activated and emits a signal though the axon. This signal might be

sent to another synapse, and might activate other neurons.

Figure 12. Natural neurons (artist’s conception).

18

The complexity of real neurons is highly abstracted when modelling artificial neurons. These

basically consist of inputs (like synapses), which are multiplied by weights (strength of the

respective signals), and then computed by a mathematical function which determines the

activation of the neuron. Another function (which may be the identity) computes the output

of the artificial neuron (sometimes in dependance of a certain threshold). ANNs combine

artificial neurons in order to process information.

Figure 13. An artificial neuron

The higher a weight of an artificial neuron is, the stronger the input which is multiplied by it

will be. Weights can also be negative, so we can say that the signal is inhibited by the

negative weight. Depending on the weights, the computation of the neuron will be different.

By adjusting the weights of an artificial neuron we can obtain the output we want for specific

inputs. But when we have an ANN of hundreds or thousands of neurons, it would be quite

complicated to find by hand all the necessary weights. But we can find algorithms which can

adjust the weights of the ANN in order to obtain the desired output from the network. This

process of adjusting the weights is called learning or training.

The number of types of ANNs and their uses is very high. Since the first neural model by

McCulloch and Pitts (1943) there have been developed hundreds of different models

considered as ANNs. The differences in them might be the functions, the accepted values, the

topology, the learning algorithms, etc. Also there are many hybrid models where each neuron

has more properties than the ones we are reviewing here. Because of matters of space, we

will present only an ANN which learns using the back propagation algorithm (Rumelhart and

McClelland, 1986) for learning the appropriate weights, since it is one of the most common

models used in ANNs, and many others are based on it.

Since the function of ANNs is to process information, they are used mainly in fields related

with it. There are a wide variety of ANNs that are used to model real neural networks, and

19

study behaviour and control in animals and machines, but also there are ANNs which are

used for engineering purposes, such as pattern recognition, forecasting, and data

compression.

4. The Back propagation Algorithm

The back propagation algorithm (Rumelhart and McClelland, 1986) is used in layered feed-

forward ANNs. This means that the artificial neurons are organized in layers, and send their

signals “forward”, and then the errors are propagated backwards. The network receives inputs

by neurons in the input layer, and the output of the network is given by the neurons on an

output layer. There may be one or more intermediate hidden layers. The back propagation

algorithm uses supervised learning, which means that we provide the algorithm with

examples of the inputs and outputs we want the network to compute, and then the error

(difference between actual and expected results) is calculated. The idea of the back

propagation algorithm is to reduce this error, until the ANN learns the training data. The

training begins with random weights, and the goal is to adjust them so that the error will be

minimal.

20

Software used

EAGLE (EASILY APPLICABLE GRAPHICAL LAYOUT EDITOR)

The design of our printed circuit board has been done using EAGLE. EAGLE software is a

complete EDA (ELECTRONIC DESING AUTOMATION) system for PC compatible

computer and windows 95/98/NT/2k/XP operating system. It includes schematic and PCB

(PRINTED CIRCUIT BOARD) modules.

EAGLE Modules

A number of EAGLE editions are offered. You can add an Autorouter Module and/or a

Schematic Editor to the Layout Editor. A standalone Schematic Editor can be used for

drawing wiring diagrams. Inthis case you won't need the Layout Editor. The user interface is

identical for all parts of the program.

The Layout Editor

The Layout Editor, which allows you to design Printed Circuit Boards (PCBs) comes with the

Library Editor, the Computer Aided Manufacturing (CAM) Processor, and the Text Editor.

With the Library Editor you can already design Packages (footprints), Symbols and Devices

(for a schematic). The CAM Processor is the program which generates the output data for the

production of the PCB (e.g. Gerber or drill files). It is also possible to use User Language

programs and Script files.

Schematic Editor

The Schematic Editor without Layout Editor is applicable for drawing electrical wiring

diagrams (connection scheme, contact plans...). The Schematic Editor comes, as well as the

Layout Editor, with the full Library Editor for designing Symbols for the Schematic and

Packages for the Layout, with the CAM Processor, and the Text Editor. You can also use

User Language

programs and Script files. If you want to draw Schematic diagrams for electronic systems you

should have Schematic and Layout Editor. You can generate the associated circuit board at

any time with a mouseclick. EAGLE then changes to the Layout Editor, where the packages

are placed next to an empty board connected via airwires (rubber bands). From here you can

go on designing with the Layout Editor as usual. Schematic and layout are automatically kept

consistent by EAGLE (Forward&Back Annotation). Schematic diagrams can consist of a

maximum of 999 sheets in the Professional Edition .

21

General

Maximum drawing area 64 x 64 inches

Resolution 1/10,000 mm (0.1 microns)

Mm or inch grid

Up to 255 drawing layers

Command (script) files

Clike

User language for data export and import and the

Realization of selfdefined

Commands

Easy library editing

Composition of self defined

Libraries with already existing elements by drag &drop

Easy generation of new package variants from other libraries by drag &drop

Free rotation of package variants (0.1degree Steps)

library browser and powerful component search function

technology support (e. G. 74l00, 74ls00..)

easy definition of labelled drawing frames

free definable attributes, applicable for devices in the library and in schematic or layout

Integrated pdf data export function,export function for graphic files (bmp, tif, png...)printouts

via the OS's printer drivers with print preview partlist generation with database support.

Software used

PROTEUS 7 PROFESSIONAL

We did the LCD simulation in the proteus software and obtained the result ‘Leaf Disease

Detection’ on the LCD.

Proteus 7.0 is a Virtual System Modelling (VSM) that combines circuit simulation, animated

components and microprocessor models to co-simulate the complete microcontroller based

designs. This is the perfect tool for engineers to test their microcontroller designs before

constructing a physical prototype in real time. This program allows users to interact with the

design using on-screen indicators and/or LED and LCD displays and, if attached to the PC,

22

switches and buttons. One of the main components of Proteus 7.0 is the Circuit Simulation --

a product that uses a SPICE3f5 analogue simulator kernel combined with an event-driven

digital simulator that allow users to utilize any SPICE model by any manufacturer.  Proteus

VSM comes with extensive debugging features, including breakpoints, single stepping and

variable display for a neat design prior to hardware prototyping. In summary, Proteus 7.0 is

the program to use when you want to simulate the interaction between software running on a

microcontroller and any analog or digital electronic device connected to it.

23

5.2 ALGORITHM AND FLOWCHART

Input : Image File

Output: Plant disease

Steps

1. Resize take image as per database images

2. Read the different planes i.2 R and G and B plane

3. Apply color segmentation on the image matrix we will get the resultant image matrix

4. Apply feature extraction on the segmented image

5. Apply morphological operation if needed

6. Calculate different features such as area, eccentricity, solidity

7. Give this feature to the neural network

8. Check the output of neural network that will be the expected result

9. Send detected result to the hardware serially

10.Hardware will decide which relay should be on and for how much time

24

FLOWCHART

No

Yes

No

Yes

Figure 16: Flowchart

25

Image Acquisition

Segmentation of RGB plane

Thresholding

Decision?

Feature Extraction

Feature calculations

Decision?

Morphological operations

Detected result

Serial transmission to

hardware

Neural networks

CONCLUSION

Thus our proposed project aims to offload the shoulders of farmers by incorporating complete

automation in the field of agriculture. Diseases will be identified with so much of ease that it

may revolutionize the entire agriculture domain. Automatic spraying of the pesticides through

the solenoid valves is significant in itself. It will prove to be a boon for all the farmers

directly or indirectly as it will help them to get better yield of crops and simultaneously it will

reduce the threats caused to the crops. And in addition to that, there will be optimum use of

pesticides which will avoid wastage of pesticides and also save the crops from damage done

due to less or more use of pesticides.

26

REFERENCES

[1] An Overview of the research on plant disease detection using image processing technique.[IOSR Journal of Computer Engineering (IOSR-JCE) Jan 2014]

[2] Advances In Image Processing For Detection Of Plant Diseases.[ Journal of Advanced Bioinformatics Applications and Research June 2011]

[3] Abdullah NE, Rahim AA, Hashim H, Kamal MM (2007) Classification of rubber tree leaf diseases using multilayer perceptron neural network. In: 2007 5th student conference on research and development. Selangor: IEEE. pp 1-6

[4] Ahmad IS, Reid JF, Paulsen MR, Sinclair JB (1999) Color classifier for symptomatic soybean seeds using image processing. Plant Dis 83(4):320-327

[5] Al Bashish D, Braik M, Bani-Ahmad S (2010) A framework for detection and classification of plant leaf and stem diseases. In: 2010 international conference on signal and image processing. Chennai: IEEE. pp 113-118

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