DESIGN AND DEVELOPMENT VISION SORTING MACHINE A PROJECT REPORT Submitted by D

DESIGN AND DEVELOPMENT VISION SORTING
MACHINE
A PROJECT REPORT
Submitted by
D.BANUPRIYA(511914105004)
B.NIVEDHITHA(511914105015)
T.PRIYANKA(511914105023)
In partial fulfillment for the award of the degree
of
BACHELOR OF ENGINEERING
IN
ELECTRICAL AND ELECTRONICS ENGINEERING

5119:PRIYADHARSHINI ENGINEERING COLLEGE
ANNA UNIVERSITY::CHENNAI 600 025
APRIL 2018

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

ANNA UNIVERSITY: CHENNAI-600 025
Bonafide Certificate
Certified that this project report titled “DESIGN ANDDEVELOPMENT OF VISION SORTING MACHINE “is the bonfire work of D. BANUPRIYA (511914105004), B. NIVEDHITHA (511914105015), T. PRIYANKA (511913105023) who carried out the project work under my supervision.

Signature signature
Mrs.R.RAJESWARI.M.Tech.,(Ph.D)., Mr.M.C.ANNAMALAI.M.E.,
HEAD OF THE DEPARTMENT SUPERVISOR,
Department of Electrical and Assistant Professor,
Electronic Engineering Department of Electrical and
Pryiadarshini Engineering Collage, Electronic Engineering
Vaniyambadi-635 751. Priyadarshini Engineering College
Vaniyambadi-635 751.

CERTIFICATE OF EVALUATION
CollegeName : Priyadarshini Engineering College,
Vaniyambadi
Branch : Electrical and Electronic Enginering
Semester : 8th semester
SI.NO NAME OF THE
STUDENT NAME OF THE
PROJECT SUPERVISOR
1 D.BANUPRIYA
(511914105004) DESIGN AND
DEVELOPMENT OF VISION SORTING
MACHINE MR.M.C.ANNAMALAI.M.E.,
(AP/EEE)
2 B.NIVEDHITHA
(511914105015) 3 T.PRIYANKA
(511914105023)
The report of project work submitted by the above students in partial fulfillment of the award of Bachelor of Engineering Degree in ELECTRICAL AND ELECTRONICS ENGINEERING of Anna University were evaluate and confined to be report of the work done by the above students and then evaluated.

Submitted for the University project work held on ……………… at Priyadarshini Engineering College,Vaniyambadi.
Internal Examiner External Examiner
ACKNOWLEDGEMENT
“A major Endeavour such as this project would never have been completed without the guidance and support of many people and organizations”.

I express my sincere thanks to our beloved Administrator, honourable Justice V.RENGASWAMY, Priyadarshini Engineering College,Vaniyambadi for providing adequate facilities to complete this project work.

I immensely grateful and sincerely convey my heart full thanks to our principal Dr.P.NATRAJAN.M.E.,Ph.D., who has been my constant source of inspiration.

I express my sincere thanks to Head of Department and project
coordinator of Electrical and Electronics Engineering Mrs.R.RAJESWARI.M.Tech.,(Ph.D)., for providing adequate laboratory facilities to complete this project.

I wish to express my sincere thanks to my guide Asst.prof for being the beacon of guidance Mr.M.C.ANNAMALAI.M.E., in my project and motivating me in every possible manner and without whom this project could not have materialized.

I thank all faculty members and supporting staff for the help they extended, in completing this project. I also express my sincere thanks to my family members and all my friends for their continuous support.

ABSTRACT
Sorting of the products in the industry is very difficult task and continuous manual sorting also creates an issues so It is very difficult to create a machine that identify the objects and relocate them if the object meets certain criteria. This project gives a solution to sort the coloured objects with the help of the vision sensor. The objects when placed on the rotating disc are sorted based on colour sensing and are relocated to their specific location. when an object moves from one location to another on the rotating disc, the camera takes an image which is fed to raspberry pi3 a single board computer(SBC).Then Raspberry pi detects the colour of the object by using image processing technique, After image processing technique the raspberry pi 3gives command to the servomotor to take necessary action for colour sorting process.
 
CHAPTER-I
INTRODUCTION
1.1GENERAL
Sorting of the object in the industries are difficult task, so the traditional manual sorting method is preferred by the industries to sort the object. In this approach, visual inspection performed by human. This traditional approach is tedious , time-consuming, slow and non-consistent. Therefore the vision sorting machine were implemented to determine colour of object and to sort the object by using image processing technique then relocated them to their specific location. Nowadays various automation techniques were implemented in the industries to increase the productivity, accuracy and to eliminating the human errors. Vision sorting machine is one such advancement in automatic systems. Vision sorting machine performs the tasks that are equivalent to human vision. It helps to automate the systems where there are limitations of human vision like detecting various shades of colors and thus permitting human employees to serve in more appropriate positions. Now, what happens when the questions turn to “Is this part of correct color?” or “Which parts are blue and which red?” So in our system, colour based identification of the parts will be done and then it will be sorted according to different colours. After recognizing the colour of the object, robotic arm will automatically pick &place it accordingly. If the colour of the work piece is not found in accordance to the required one then it will be rejected. The complete sorting system operates on image processing using the MATLAB and raspberrypi3.the Arduino uno used to control the servomotors and LCD in the system. vision based on color concept has found its wide application in the pharmaceuticals industry, agriculture industry, food industry and assembly of parts especially in automobile industry. Due to the advancement in the vision sorting system and their software tools have enabled manufacturers to apply vision sorting machine on the factory floor in real time. This technology can solve problems in the industries due to manual inspections & sorting.

1.2SORTING:
Sorting is the process of arranging an objects in some sequence or in different sets. It has two common distinct meanings are arranging and classifying. Arranging is ordering the objects of the same kind, class, nature, etc. in some order sequence while categorizing is grouping and labelling items with similar properties together. The main purpose of sorting information in industries to optimize its usefulness for specific tasks.
1.3COLOUR SORTER:
The device which is used to sort the object are known as sorter. Colour sorter is the device used to sort the object based on the colour. Their are different types sorter are used in the industries to sort the object. The colour sorter are used in many industries like food industries, medical industries, diamond industry and agricultural industry. There are different types of colour sorter used in the industries are belt type sorter and chute type sorter.

Fig.1.colour sorter
1.4 OBJECTIVE:
The objectives of the project are to be able to design and development of vision sorting machine interfacing with the MATLAB software using raspberrypi3 and Arduino uno.

The main objective of this work are,
Build a unique kind of algorithm to achieve new kind of approachability in the field of automation in the industry.

Sorting the object based on the colour and relocated them to their specific location.

Display the number of objects and colour of the object on the LCD.

1.5 ORGANIZATION OF THE PROJECT WORK
This report is organized into seven chapters.

Chapter 2 Presents the literature review of the project.

Chapter 3 presents the difference between colour and light.

Chapter 4 Describes the Hardware and software details.

Chapter 5 To implementation of vision sorting machine in real time
Chapter 6 Describes the simulation results of vision sorter.

Chapter 7 Advantages and applications.

Chapter 8 Describes conclusion and future work
CHAPTER 2
2.1LITERATURE REVIEW:
Bozma and Yal-cin (2002) in their journal explain about a Visual sorting setup in an industrial setting. They state how items at random position to be moved on a conveyor. A camera located above the conveyor views the items orthographically. They assume that there is an item separator placed before the camera so that the incoming items are not overlapping which is a realistic assumption in many manufacturing environments. A sensing device signals the presence of perception that is instead of processing the whole image, only areas that are deemed ”interesting” and thus calling for attention are analyzed. Visual sorting setup in an industrial setting.
The paper in presents a smart approach for a real time inspection and selection of objects in continuous flow. The basic theme of this project is object flowing on conveyor are sensed, selected and sorted depending on their color. For this, camera is used as input to the microcontroller. Camera is mounted on PC and connected to it by USB. The camera takes a snap and feeds to PC for color processing. In PC MATLAB is used for processing on color, depending on this signal given to microcontroller Atmega 328. The microcontroller in turn controls the servomotors by PWM signals. These servomotors control the movement of robotic arm, by controlling their angular movement. Thus the robotic arm is fully controlled by servomotors. The gripper of robotic arm picks the object and places it depending on its color.This is a full automatic process no manual support is needed. The microcontroller used here is with the support of Arduino kit. The Arduino is a good platform for robotics application. It is the software and hardware also; using both the above system is developed. Thus the real time, continuous object sorting can be done.

Trinesh, T. M. and VijayavithalBongale propose another way of sorting; the proposed system is an embedded system which increases the speed of color sorting procedure, provides the accurate color sorting process, decreases the cost of color sorting process and optimizes the productivity of an industrial object. The system comprises of color sensor, stepper, servo motors and microcontroller. It synchronizes the movement of robotic arm to pick the objects moving on a conveyor belt. It aims in sorting the colored objects which are coming on the conveyor by picking and placing them in their respective pre-programmed places. There by eliminating the monotonous work done by human, achieving accuracy and speed in the work. The project involves color sensors that senses the object’s color and sends the signal to the microcontroller. The microcontroller sends signals to a circuit which drives the various motors of the robotic arm to grip the object and place it in the specified location. Based upon the color detected, the robotic arm moves to the specified location, releases the object and comes back to the original position. base which has wheels helps the robot move from one place to other.
CHAPTER 3
3.1 Definition of Light:-
Light is an electromagnetic radiation within a certain portion of the electromagnetic spectrum, the word usually refers to visible light, which is visible to the human eye and is responsible for the sense of sight. In physics, the term light sometimes refers to electromagnetic radiation of any wavelength, whether visible or not. In the sense, gamma rays, X-rays, microwaves and radio waves are also light.
3.2 Color Concept:-
A ‘color’ is an interaction between a very small range of electromagnetic waves and the eyes and brain of a person.
Color drives from the spectrum of light interacting in the eye with the spectral sensitivities of the light receptors. Color categories and physical specifications of color are also associated with objects or materials based on their physical properties such as light absorption, reflection, or emission spectrum.

3.3 The Relationship between Color and Light:-
Color is a sensation of the brain, and the ability to see colors is only possible in the presence of light.
Sir Isaac Newton discovered that white daylight is actually made up of a spectrum of colors, namely; Red, Orange, Yellow, Green, Blue and Violet. It is only when light falls on an object that it’s characteristic color is seen and there are only three ways that an object interacts with light rays. When all the rays of light are absorbed human eye sees the color black; when all are reflected it sees white and when all but one are absorbed, it sees the color that is reflected. An apple, for example, is red. Under white light, the apple appears red because it tends to reflect light in the red portion of the spectrum and light of other wavelengths. If a filter is used to remove red from the light source, the apple reflects very little light and appears black. Each color has a unique wavelength that is processed and recognized in the eye and then transmitted to the brain. The rods absorb and cones of the retina of the eye recognize the colors of light and degree of black and white thus making it possible to perceive the color characteristics of objects. The brain then translates this information to be perceived by the human eye. Hence, seeing is actually a function of the brain, as it is known that one can have perfectly healthy eyes but still be totally blind if the vision part of the brain is damaged.
3.4 THE VISIBLE COLORS AND THEIR CORRESPONDING WAVELEVGTH:
VISIBLE COLORS WAVE LENGTH OF THE COLOR IN (nm)
VIOLET 380-410
INDIGO 410-450
BLUE 450-510
GREEN 510-560
YELLOW 560-600
ORANGE 600-630
RED 630-710
CHAPTER 4
HAREWARE DETAILS
4.1INTROCUCTION
The hardware implementation of project consist of following components
Raspberry pi3
Arduino Uno
DC Servo Motor
Camera
6V Lead Acid Battery
LCD display
Switch
Robotic arm.

4.2 RASPBERRY PI 3:
The Raspberry pi3 is the single board computer (SBC), which perform all the task that the computer does. The original model became popular than anticipated, selling outside its market for uses such a robotics. It doesn’t include peripherals (such as keyboards, mice and cases). Several generations of Raspberry Pi have been released. All the models of a Boardcom system on a chip (SoC) with integrated ARM, Central processing unit (CPU) and on-chip graphics processing unit (GPU).Processor speed range from 700 MHz to 1.4 GHz for the Pi 3; on-board memory ranges from 256 MB to 1 GB RAM secure digital (SD) cards were used to store the operating system and program memory in either Micro SDHC size. The boards have 4 USB ports. For video output, HDMI composite video are supported, with a standard 3.5 mm phone jack for audio outputs. Low-level output is provided by a number of GPIO pins which support common protocols like I^2C.The B-models have 8P8C Ethernet port and the Pi 3 have on-board Wi-Fi 802.11n and blue tooth.

4.2.1 Hardware:
The Raspberry Pi hardware has evolved through several versions. Only the variations in memory capacity and peripheral-device support. This block diagram depicts Models A, B, A+, and B+. Model A and the Pi Zero lack the Ethernet and USB hub components. The Ethernet adapter is internally connected to addition USB port. In Model A, A+ the USB ports are directly connected to the System on chip (SoC). On the Pi 1 Model B+ and later models the Ethernet chip contains a five-point USB hub, of which four ports are available, On the Pi Zero, the USB port is also connected directly to the SoC, but it use a micro USB.

Fig.2.Raspberry pi3
4.2.2 Use in industrial automation:
In June 2014, TECHBASE, Polish industrial automation manufacture designed the world first industrial computer based on the Raspberry Pi Module, called Mod Berry. The device has numerous interfaces, notably RS-485/232 serial ports, digital inputs/outputs, CAN and economical 1-Wire buses. Which are used in the automation industry. The design allows the use of the Compute Module in industrial environments, leading to the conclusion that the Raspberry Pi is no longer limited to home and science projects, but used in Industrial IoT solution and achieve goals of industry.

4.2.3 Operating systems:

Fig.3.operating system image
Various operating systems for the Raspberry Pi can be installed on a MicroSD, MiniSD card, depending upon the board available adapters; seen here is the MicroSD slot located on the bottom of a Raspberry Pi 2 board. The Raspberry Pi Foundation recommended the uses of Raspbian, a Debian-based Linux operating system. Other third-party operating systems available via the official website include ubuntu MATE, Windows 10 IoT core, RISC OS and specialized distributions for the Kodi media Centre.

4.2.4 General purpose input-output (GPIO) connector:
Raspberry Pi 1 Models A+ and B+, Pi 2 Model B, Pi 3 Model B and B+, and Pi Zero and Zero W GPIO J8 have a 40-pin pinout.
4.2.5 Technical Specifications of RaspberryPi3:
SoC BCM2837
CPU Quad cortex [email protected] 1.2GHz
Instruction set ARMv8-A
GPU 400MHz Video Core IV
RAM 1GB SDRAM
Storage Micro-SD
Ethernet 10/100
Wireless 802.11n/ Bluetooth 4.0
Video output HDMI/Composite
Audio output HDMI/Headphone
GPIO 40 pin
USB 2.0 ports 4
4.2.6. RAM:
On the older beta Model B boards, 128 MB was allocated by default to GPU, leaving 128 MB for the CPU. On the first 256 MB release Model B three different splits were possible. The default split was 192 MB (RAM for CPU), which should be sufficient for standalone 1080p video decoding, but probably not for both together. 224 MB for Linux only, with only a 1080p frame buffer, and was likely to fail for 3D or video. 128 MB was for heavy 3D, possible with
video decoding (e.g. XBMC). Comparatively the Nokia 701 used 128 MB for the Broadcom Video Core IV.
4.2.7. Real-time clock:
The current Raspberry Pi models have a built-in real-time clock, so they should have unable to keep track of the time of day independently. As a work around, a program running on the Pi can retrieve the time from a network time server ,user input at boot time, thus knowing the time while powered on. To provide consistency of time for the system files, the Pi automatically save the time it has on shutdown, and re-installs that time at boot.A real-time hardware clock with battery backup, such as the DS1307, may be added (often via the I²C interface).

4.3 ARDUINO UNO:
The Arduino UNO is a widely used in open-source microcontroller board based on the ATmega328P microcontroller. It has developed by Arduino.cc.The board is connected with sets of digital and analog input/output (I/O) pins. Its may be interfaced to various expansion boards and other circuits. The board having 14 Digital pins and 6 Analog pins. It is programmable with Arduino IDE (Integrated Development Environment) a type B USB cable. It can be powered by a USB cable , though it accepts voltages 7-20 volts. It is similar to the Arduino Nano . “Uno” means one in Italian and was chosen to mark the release of Arduino Software (IDE) 1.0. The Uno board and version 1.0 of Arduino Software (IDE) were the reference versions of Arduino, now evolved to new releases. The Uno board is a series of USB Arduino board, and the reference model for the Arduino platform. The ATmega328 on the Arduino Uno comes preprogrammed with a boot loader that allows to load new code to it without use of an external hardware programmer. It communicate using the original STK500 protocol. The Uno differs from all preceding boards in that it does not use the FTDI USB-to-serial driver chip. Instead, it features the Atmega16U2 programmed as a USB-to-serial converter. The Arduino UNO is generally considered the most user-friendly.

Fig.4.Arduino Uno image
Back ground:
The Arduino project begin at the Interaction Design Institute Ivrea (IDII) in Ivrea, Italy. At that time, the students used a BASIC Stamp microcontroller at a cost of $100, a considerable expensive for many students. In 2003 Hernando Barragán created the development platform wiring as a Master’s thesis project at IDII, under the supervision of Massimo Banzi and Casey Reas, who are known for work on the Processing language. The project goal was to create simple, low-cost Arduino tools for creating digital projects by non-engineers. The Wiring platform consisted of a printed circuit board (PCB) with an ATmega168 microcontroller, an IDE based on Processing and library functions to program the microcontroller. In 2003, Massimo Banzi, with David Mellis, another IDII student, and David Cuartielles, supported for the cheaper ATmega8 microcontroller to Wiring. Instead of continuing the work on Wiring, they forked the project and renamed it Arduino. Early Arduino boards used the FTDI USB-to-serial driver chip and an ATmega168. The Uno differed from preceding boards by featuring the ATmega328P microcontroller and an ATmega16U2 (Atmega8U2 up to version R2) programmed as a USB-to-serial converter.

Fig.5.Arduino Uno back ground image
Technical specifications:
Micontroller Atmega328p
Operating voltage 5V
Input voltage 7-20V
Digtal I/O pins 14
Analog input pins 6
Dc current per I/O pin 20mA
Dc current for 3.3V pin 50mA
SRAM 2KB
Flash memory 32 KB of which 0.5 KB used by bootloaderClock speed 16MHz
EEPROM 1KB
Length 68.8mm
Width 53.4mm
Weight 25g
4.4 HIGH TORQUE SERVO MOTOR:
This High-Torque MG996R Digital Servo features metal gearing resulting in extra high 10kg stalling torque in a tiny package which is shown in Figure 4.4 The MG996R is essentially an upgraded version of the famous MG995 servo, and features upgraded shock-proofing and a redesigned PCB and IC control system that make it much more accurate than its predecessor.

The gearing and motor have also been upgraded to improve dead bandwith and centering. The unit comes complete with 30cm wire and 3 pin ‘S’ type female header connector that fits most receivers, including Futaba, JR, GWS, Cirrus, Blue Bird, Blue Arrow, Corona, Berg, Spektrum and Hitec. This high-torque standard servo can rotate approximately 120 degrees (60 in each direction). You can use any servo code, hardware or library to control these servos. The MG996R Metal Gear Servo also comes with a selection of arms and hardware to get set up.

Fig.4.4servomotor
4.4.1 SPECIFICATIONS
Weight: 55 g
Dimension: 40.7 x 19.7 x 42.9 mm approx.

Stall torque: 9.4 kgf·cm (4.8 V ), 11 kgf·cm (6 V)
Operating speed: 0.17 s/60º (4.8 V), 0.14 s/60º (6 V)
Operating voltage: 4.8 V a 7.2 V
Running Current 500 mA –
Stall Current 2.5 A (6V)
Dead band width: 5 ?s
Stable and shock proof double ball bearing design
4.5 CAMERA:

Fig. 7 .web camera image
The Raspberry Pi camera module can be used to take high-definition video, as well as stills photographs. It’s easy to use for beginners, but has plenty to offer advanced users if you’re looking to expand your knowledge. There are lots of examples online of people using it for time-lapse, slow-motion and other video cleverness. You can also use the libraries we bundle with the camera to create effects.
If you’re interested in the nitty-gritty, you’ll want to know that the module has a five megapixel fixed-focus camera that supports 1080p30, 720p60 and VGA90 video modes, as well as stills capture. It attaches via a 15cm ribbon cable to the CSI port on the Raspberry Pi. It can be accessed through the MMAL and V4L APIs, and there are numerous third-party libraries built for it, including the Pi camera Python library.
The camera module is very popular in home security applications, and in wildlife camera traps. You can also use it to take snapshots.
4.5.1 Features
5MP sensor
Wider image, capable of 2592×1944 stills, 1080p30 video
1080p video supported
CSI
Size: 25 x 20 x 9 mm
4.5.2 Camera Details
The camera consists of a small (25mm by 20mm by 9mm) circuit board, which connects to the Raspberry Pi’s Camera Serial Interface (CSI) bus connector via a flexible ribbon cable. The camera’s image sensor has a native resolution of five megapixels and has a fixed focus lens. The software for the camera supports full resolution still images up to 2592×1944 and video resolutions of 1080p30, 720p60 and 640x480p60/90. The camera module is shown below:

Fig.8 .camera module image
Installation involves connecting the ribbon cable to the CSI connector on the Raspberry Pi board. This can be a little tricky, but if you watch the videos that demonstrate how it is done, you shouldn’t have any trouble.
When you purchase the camera, you will receive a small camera board and cable. You’ll want to devise some method of supporting the camera in order to use it. Some camera stands and Raspberry Pi cases are now available.
4.6 ROBOTIC ARM
A robotic arm is a type of mechanical arm, usually programmable, with similar functions to a human arm; the arm may be the sum total of the mechanism or may be part of a more complex robot. The links of such a manipulator are connected by joints allowing rotational motion (such as in an articulated robot). The links of the manipulator can be considered to form a kinematic chain. The terminus of the kinematic chain of the manipulator is called the end effector and it is analogous to the human hand.

4.7 6V LEAD ACID BATTERY:
The lead–acid battery was invented in 1859 by French physicist Gaston plante and is the oldest type of rechargeable battery. Despite having a very low energy-to-weight ratio and a low energy-to-volume ratio, its ability to supply high surge currents means that the cells have a relatively large power-to-weight ratio. These features, along with their low cost, make them attractive for use in motor vehicles to provide the high current required by automobile starter motors.

As they are inexpensive compared to newer technologies, lead–acid batteries are widely used even when surge current is not important and other designs could provide higher energy densities. Large-format lead–acid designs are widely used for storage in backup power supplies in cell phone towers, high-availability settings like hospitals, and stand-alone power systems. For these roles, modified versions of the standard cell may be used to improve storage times and reduce maintenance requirements. Gel-cells and absorbed glass-mat batteries are common in these roles, collectively known as VRLA (valve-regulated lead–acid) batteries.

4.7.1 Discharge:

Fig. 9.discharge image
Fully discharged: two identical lead sulfate plates. In the discharged state both the positive and negative plates become lead(II) sulfate(PbSO4), and the electrolyte loses much of its dissolved sulfuric acid and becomes primarily water. The discharge process is driven by the conduction of electrons from the negative plate back into the cell at the positive plate in the external circuit. As electrons accumulate they create an electric field which attracts hydrogen ions and repels sulfate ions, leading to a double-layer near the surface. The hydrogen ions screen the charged electrode from the solution which limits further reactions unless charge is allowed to flow out of electrode. The sum of the molecular masses of the reactants is 642.6 g/mol, so theoretically a cell can produce two faradays of charge (192,971 coulombs) from 642.6 g of reactants, or 83.4 ampere-hours per kilogram (or 13.9 ampere-hours per kilogram for a 12-volt battery). For a 2 volts cell, this comes to 167 watt-hours per kilogram of reactants, but a lead–acid cell in practice gives only 30–40 watt-hours per kilogram of battery, due to the mass of the water and other constituent parts
4.7.2 Charging:

Fig.10.charging image
Fully recharged: Lead anode, Lead oxide cathode and sulfuric acid electrolyte. In the fully charged state, the negative plate consists of lead, and the positive plate lead dioxide, with the electrolyte of concentrated sulfuric acid. Overcharging with high charging voltages generates oxygen and hydrogen gas by electrolysis of water, which is lost to the cell. The design of some types of lead-acid battery allow the electrolyte level to be inspected and topped up with any water that has been lost. Due to the freezing-point depression of the electrolyte, as the battery discharges and the concentration of sulfuric acid decreases, the electrolyte is more likely to freeze during winter weather when discharged.

The 6v lead acid baterry is used to given the power supply to the arduino Uno. The Arduino is used to control the LCD and servomotors.

Fig.11.Baterry image
4.8 LIQUID CRYSTALINE DISPLAY(LCD):
A liquid-crystal display (LCD) is a flat-panel display or other electronically modulated optical device that uses the light-modulating properties of liquid crystals. Liquid crystals do not emit light directly, instead using a backlight or reflector to produce images in colour or monochrome.1 LCDs are available to display arbitrary images (as in a general-purpose computer display) or fixed images with low information content, which can be displayed or hidden, such as preset words, digits, and 7-segment displays, as in a digital clock. They use the same basic technology, except that arbitrary images are made up of a large number of small pixels, while other displays have larger elements.

LCDs are used in a wide range of applications including LCD televisions, computer monitors, instrument panels, aircraft cockpit displays, and indoor and outdoor signage. Small LCD screens are common in portable consumer devices such as digital cameras, watches, calculators, and mobile telephones, including smartphones. LCD screens are also used on consumer electronics products such as DVD players, video game devices and clocks. LCD screens have replaced heavy, bulky cathode ray tube (CRT) displays in nearly all applications. LCD screens are available in a wider range of screen sizes than CRT and plasma displays, with LCD screens available in sizes ranging from tiny digital watches to huge, big-screen television sets.

Since LCD screens do not use phosphors, they do not suffer image burn-in when a static image is displayed on a screen for a long time (e.g., the table frame for an aircraft schedule on an indoor sign). LCDs are, however, susceptible to image persistence.2 The LCD screen is more energy-efficient and can be disposed of more safely than a CRT can. Its low electrical power consumption enables it to be used in battery-powered electronic equipment more efficiently than CRTs can be. By 2008, annual sales of televisions with LCD screens exceeded sales of CRT units worldwide, and the CRT became obsolete for most purposes.

Fig. .lcd display image
4.9 5V POWER SUPLLY:
5v power supply is given to the Raspberry pi 3 through the 5v, 2.1amperes adaptor for continuous power supply to the Raspberry pi 3. Fi
Fig.12. 5v adpator
4.10 SOFTWARE DETAILS:
The vision sorter is interfaced with the MATLAB software (version 2016) to find the colour of the object by using image processing technique and it enable the person to see the whole operation of the vision sorter through the computer.

4.10.1 MATLAB:
MATLAB is a short form of(matrix laboratory) is a multi-paradigm numerical computing environment. A proprietary programming language developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, C#, Java, Fortran and Python.

Although MATLAB is intended primarily for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing abilities. An additional package, Simulink, adds graphical multi-domain simulation and model-based design for dynamic and embedded systems.

As of 2017, MATLAB has roughly 1 million users across industry and academia. MATLAB users come from various backgrounds of engineering, science, and economics.

4.10.2 IMAGE PROCESSING TECHNIQUE:
The MATLAB and Mathcad environments are ideally suited to image processing. In particular, MATLAB’s matrix-oriented language is well suited for manipulating images, which are nothing more than visual renderings of matrices. The result is a very easy and economical way of expressing image processing operations. In addition, both programs have Image Processing Toolboxes which provide a powerful and flexible environment for image processing and analysis. Both programs were used to perform different calculations on images, for example, Mathcad was used to generate all examples of contour functions discussed in Part 2. There are several advantages of using Mathcad and MATLAB for image analysis. One of them is the ability to have direct access to any portion of available information what in general is not possible with many commercial image analysis systems. With these programs it is possible to stop any calculations at any time, change a portion of the calculation procedure and then restart the calculations from the point which was affected by the changes without recompiling the code as it usually happens with programming in C, or even restarting the calculations from the beginning. Such ability is very useful for research and the development of new techniques. However, the main disadvantage of these programs is the relatively slow computational speed compared to compiled C code. It is caused by the need of the code to be translated first into a machine code and only then to be executed. Therefore, complex image processing applications can be better implemented using high level programming languages such as C or C++, rather than using software like MATLAB or Mathcad.

fig .13.colour sorting
CHAPTER 5
HARDWARE IMPLEMENTATION OF VISION SORTER
5.1 INTRODUCTION:
Colour sorter interfaced with MATLAB is designed to sort the object based on the colour and to relocate the object to their specific location. The vision sorter consists of Raspberry pi3, Arduino Uno, camera, Computer with MATLAB software, servomotors, robotic arm, LCD display, 6v Lead acid battery. MATLAB software is interfaced with the Raspberry pi3 and the 5v power supply is given to the raspberry pi3 through the 5volt,2.1amps adaptor. Here 240*320pixel size camera is used to capture the image on the rotating disc. Raspberry pi 3 detect the colour of the object by using image processing technique. The programming is done on Arduino Uno microcontroller using Arduino programming and 5volt Lead acid battery is used to give the power supply to the Arduino. Here two 180degree servomotors are used to pick and place the object. The two servomotors are controlled by using by Arduino Uno. The20*4LCD (liquid crystalline display) is used here to display the number and colour of the object to be sorted and it is controlled by using Arduino Uno. The objects are placed on the rotating disc. In the vision sorter 4 LED’s are used to give the contrast to the object.

5.2 PROTOTYPE CONSTRUCTION:
Designed prototype vision sorter consist of Raspberry pi3, Arduino Uno, computer with MATLAB software, 240*320pixel size camera, 180degree servomotors, LCD. The Raspberry pi 3 is the main part of the vision sorter. computer with the MATLAB software is interfaced with the Raspberry pi3 through the TPLINK modem. By using image processing technique the Raspberry pi3 detect the colour of the object. The 5volt power supply is given to the Raspberry pi3 through the 5v,2.1amps adaptor. 20*4pixel size camera is used to capture the image and also 4 LED’s are used in it to give the contrast to the object. The object is placed on the rotating disc. Arduino Uno is programmed with MATLAB software which control the two 180degree servomotors and LCD display. The 6volt Lead acid battery is used to give the power supply to the Arduino Uno. Here two 180degree servomotors are used one is used to pick the object and another one is used to place the object to their specific location. The 20*4 LCD (liquid crystalline display) is used to display the number and colour of the object to be sorted which is also controlled by using Arduino Uno. A robotic arm programmable with similar functions to a human arm is used here to place the object to their specific location. The whole operation of the vision sorter can be viewed on the computer. The block diagram for the vision sorter is shown in below.
5.3 BLOCK DIAGRAM:
CAMERA
RASPBERRY PI 3
COMPUTER
(MATLAB)
ARDUINO
SERVO MOTOR 1
SERVO MOTOR 2
LCD 20*4
SWITCH
5 V POWER SUPPLY

Fig.14 .block diagram
5.4 CIRCUIT DIAGRAM:

Fig.15.Circuit diagram
5.5 SIMULATION DIAGRAM:
The simulation diagram for image processing technique to the colour sorter is given below.

Fig.16.simulink diagram
5.6 PROGRAMMING:

The programming for the Arduino Uno is given below.

/* Sweep by BARRAGAN <http://barraganstudio.com> This example code is in the public domain. modified 8 Nov 2013 by Scott Fitzgerald http://www.arduino.cc/en/Tutorial/Sweep*/#include ;LiquidCrystal.h;// initialize the library by associating any needed LCD interface pin// with the arduino pin number it is connected toconst int rs = 8, en = 9, d4 = 10, d5 = 11, d6 = 12, d7 = 13;LiquidCrystal lcd(rs, en, d4, d5, d6, d7);int red = 30;int green = 45;int blue = 60;int orange = 80;int vilote = 90;int yellow = 105;int pink = 120;int nocolor = 150;int c1=0,c2=0,c3=0,c4=0,c5=0,c6=0,c7=0;#include ;Servo.h;Servo myservo;Servo myservo1; int pos = 0; boolean a,b,c;void setup() { myservo.attach(5); //BLACK myservo1.attach(6); // BROWN pinMode(2, INPUT); pinMode(3, INPUT); pinMode(4, INPUT); lcd.begin(20, 4); lcd.print(“System booting …”); delay(5000); }void loop() { for (pos = 0; pos ;= 180; pos += 1) { myservo.write(pos); delay(5); } for (pos = 180; pos ;= 0; pos -= 1) { myservo.write(pos); if(pos == 75) { delay(1500); a = digitalRead(2); b = digitalRead(3); c = digitalRead(4); if(a == 1 ;; b == 0 ;; c == 0) { myservo1.write(red); c1++; } if(a == 0 ;; b == 1 ;; c == 0) { myservo1.write(green); c2++; } if(a == 1 ;; b == 1 ;; c == 0) { myservo1.write(blue); c3++; } if(a == 0 ;; b == 0 ;; c == 1) { myservo1.write(orange); c4++; } if(a == 1 ;; b == 0 ;; c == 1) { myservo1.write(vilote); c5++; } if(a == 0 ;; b == 1 ;; c == 1) { myservo1.write(yellow); c6++; } if(a == 1 ;; b == 1 ;; c == 1) { myservo1.write(pink); c7++; } if(a == 0 ;; b == 0 ;; c == 0) { myservo1.write(nocolor); } } if(pos == 2) { delay(100); } delay(5); } lcd.clear(); lcd.setCursor(0,0); lcd.print(“Red :”); lcd.setCursor(8,0); lcd.print(c1); lcd.setCursor(11,0); lcd.print(“Green :”); lcd.setCursor(18,0); lcd.print(c2); lcd.setCursor(0,1); lcd.print(“Blue :”); lcd.setCursor(8,1); lcd.print(c3); lcd.setCursor(11,1); lcd.print(“Orange:”); lcd.setCursor(18,1); lcd.print(c4); lcd.setCursor(0,2); lcd.print(“Vilote:”); lcd.setCursor(8,2); lcd.print(c5); lcd.setCursor(11,2); lcd.print(“Yellow:”); lcd.setCursor(18,2); lcd.print(c6); lcd.setCursor(0,3); lcd.print(“Pink :”); lcd.setCursor(8,3); lcd.print(c7);}
5.7 WORKING OF VISION SORTER:
This chapter deals with the working principle of vision sorter. The vision sorter consist of rotating disc, whenever the object is placed on the rotating disc the 320*240pixel size camera capture the image of the object in 60 frames per second(60f/sec), four LED’s connected above the rotating disc to give the contrast to the image. Whenever the image is captured which is send to the Raspberry pi3 which is interfaced with the MATLAB software. By using image processing technique the Raspberry pi3 detect the colour of the object. Whenever the colour is detected the Raspberry pi3 sends the information to Arduino uno which is programmed with MATLAB software to control the servomotor and LCD display. Then the Arduino controls the servomotors1and2. The servomotor one is used to pick the object and another one is used to placed the object. The servomotor control the robotic arm, which is used to locate the object to their specific location. The LCD display is used to display the number and colour of the object to be sorted. When the object is not in the specified colour or if the object is damage one then it is located to the rejected bin.
CHAPTER 6
SIMULATION RESULTS
6.1 INTRODUCTION:
This chapter discuss about the simulation results for the vision sorting system. Simulation results based on which colour to be sorted in the vision sorting system by using MATLAB/Simulink software.

6.2 RESULTS OF VISION SORTER:
When the camera takes the image of the object immediately it sends the information to the Raspberry pi3 it detect the colour using image processing technique. If the colour is detected, the detected colour value become high (1) and other values are low (0) in the Simulink graph. The hardware implementation of the vision sorting system and the simulation results are shown in below. 6.2.1 shows the real time implementation of vision sorter.

Fig.17.hardware image

fig.18. simulation results

CHAPTER 7
7.1 ADVANTAGES:
Sorting the object according to their colour.

The vision sorter takes less time to complete the work.

In our proposed system human work can be reduced.

Easy analysis.

7.2 APPLICATIONS:
Medical industries:
It is used in the medical industries to sort the tablets based on the colour.

Food industries:
It is also used in the food industries to sort the vegetables, fruits, candies etc based on the colour.

Diamond industries:
It is also used in the diamond industries to sort the diamonds
based on the reflecting colour.

CHAPTER 8
8.1 CONCLUSION:
The vision sorting system can detect and sort the objects based on the colour and display the number and colour of the object to be detected. In this system the hardware is interfaced with the MATLAB software (version 2016), so the whole operation of the vision sorter can be viewed through the computer. Hence this system has a great ability applying into practice especially in food industries. The accuracy of this program is suitable for sorting applications with automated actuators as industrial robots.
8.2 FUTURE ENHANCEMENT:
Instead of Raspberry pi3 the digital signal processor can be used.

This proposed system can sort the object based on the colour only, in future we can modify it to sort the object based on the shape and size.

The proposed system will be a sample version ,so for a large scale manufacture the number of robotic arms, camera and length of disk system can be attributed.

8.3 REFERENCES:
Tiong Phou Though, Nguyen Truong Thinh,Nguyen Huy Bich,”design and development of colour sorting system”,”3rdInternational Conference on Green Technology and Sustainable Development”IEEE 2016.

Dhanoj M1, Reshma K V2, Sheeba V3,Marymol in “colour sensor based object sorting robot using Embedded system,””International Journal of Advanced Research” in Computer and Communication Engineering Vol. 4, Issue 4, April 2015.

Ahmed M. A, Haidar1, Chellali, Benachaiba2, Mohamad, Zahir, ” Software Interfacing of Servo Motor with Microcontroller”, Journal of Electrical Systems, 9-1, pp 84-99, 2013.

A. Rama Krishna, G. SowmyaBala, A.S.C.S. Sastry, B. BhanuPrakashSarma, GokulSaiAlla,” Design And Implementation Of A Robotic ArmBased On Haptic Technology”, International Journal of Engineering Research and Applications. Vol. 2, Issue 3, pp.3098-3103, May-Jun 2012.

ShwetaPatil, Sanjay Lakshminarayan,” Position Control of Pick and Place Robotic Arm”, EIE’s 2nd Intl’ Conf.Comp., Energy, Net., Roboticsand Telecom. EIE Con2012.

Bundit Jarimopas,Nitipong Jaisin, “An experimental machine vision system for sorting sweet tamarind” Journal of Food Engineering, Volume 89, Issue 3, December 2008, Pages 291–297
D. Bulanon, T. Kataoka, Y. Ota, T. Hiroma, “A Machine Vision System for the Apple Harvesting Robot”,SICE 2004 Annual Conference2004.
H. Isil Bozma, Hülya Yalcin, “Visual processing and classification of items on a moving conveyor: a selective perception approach”, Robotics and Computer Integrated manufacturing 12,2002.

J Blasco,N Aleixos,E Moltó, “Machine Vision System for Automatic Quality Grading of Fruit” Biosystems EngineeringVolume 85, Issue 4, August 2003, Pages 415–423
W. Zhang, J. Mei, Y. Ding, “Design and Development of a Hight Speed Sorting.