Artificial Vision for the Recognition of Exportable Mangoes by Using Neural Networks

cartcletchAI and Robotics

Oct 19, 2013 (3 years and 9 months ago)

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Hugo Froilán Vega Huerta


Ana María Huayna Dueñas

Artificial Vision for the Recognition

of Exportable Mangoes

by Using Neural Networks


UNMSM

Antecedents

3

Antecedents

4

Percentages of Export for Types of Mangoes

Antecedents

5

Antecedents

6

BRIOFRUIT staff are separating mangoes that won’t be exportable.

Antecedents

Antecedents

8

THE PROBLEM

Statistics plant selection (purification of mangoes malformed)


9

¿ How the neural networks allow the
recognition of the quality of export mangoes
in Biofruit?

THE PROBLEM

10



Achieve to train a neural network that is able of


recognize export mangoes.


Achieve to reduce the margin of error from 6.5% to
3%.


OBJECTIVES

11

Theoretical Framework



DEFINITION
[James A. Anderson 2007]

Is

a

set

of

units

of

processing

called

Neurons,

cells

or

nodes,

interconnected

to

each

other

by

bonds

of

communication

direct

called

connections,

with

the

purpose

of

receiving

input

signals,

process

them

and

emit

output

signals
.

Each

connection

is

associated

to

a

weight

that

they

represent

the

knowledge

of

the

RN


They

are

models

Mathematical

inspired

in

the

operation

of

the

biological

neural

networks,

consequently,

central

processing

units

of

a

RNA,

will

be

the

Artificial

Neurons
.


Next

we

present

the

graphic

representation

of

a

RNA





12

RN TRAINING

[
Edgar N. Sánchez, 2006
]

It consists on presenting to the system a set of pairs of data, representing the input and
the wanted output for this input. This set receives the name of group of training. The
objective is to try to minimize the error between the Wanted output and the current one.
The weights are adjusted in function of the difference between the wanted values and the
obtained output values.





Theoretical Framework



13

STATE OF THE ART





Doctoral

Thesis

-

Facial

recognition

techniques

using

neural

networks

(Enrique

Cabello

P
.



Politécnica

de

Madrid

University,

2004
)



Master

Thesis

-

Techniques

to

improve

voice

recognition

in

the

Presence

of

Out

of

Vocabulary

Speech

(Heriberto

Cuayáhuitl

Portilla

-

Las

Américas

de

Puebla

University

Foundation)



Article
-

Shape

Recognition

of

Film

Sequence

with

Application

of

Sobel

Filter

and

Backpropagation

Neural

Network

(A
.

Glowacz

and

W
.

Glowacz

2008
)


14




COMPARATIVE EVALUATION OF METHODS OF PATTERN
RECOGNITION

(
Eybi Gil Z, 2010
)

STATE OF THE ART

15




STATE OF THE ART

COMPARATIVE EVALUATION OF METHODS OF PATTERN
RECOGNITION

(
Eybi Gil Z, 2010
)

METHODOLOGY

Neural Network For

Recognition of Exportable Mangos


17

METHODOLOGY

Artificial Vision

18

METHODOLOGY

Artificial Vision

19

METHODOLOGY

Artificial Vision

20

METHODOLOGY

Artificial Vision

21

METHODOLOGY

Artificial Vision

22

Functional dependency between input and output data

in a Neural Network

METHODOLOGY

Recognition of Exportable Mangos


23

Architecture of the NN for the recognition of mangoes


METHODOLOGY

24

Knowledge Base for Neuronal Network Training


METHODOLOGY

25

Knowledge Base for Neuronal Network Training

METHODOLOGY

26

Knowledge Base for Neuronal Network Training

METHODOLOGY

27

Neural Network Training

METHODOLOGY

28

Recognition of Exportable Mangoes

¡ Exportable Mangoes !

¿ Exportable Mangoes?

? ? ?

METHODOLOGY

29

Recognition of Exportable Mangoes

METHODOLOGY

30

We execute the program of Recognition

Interpretation

Output information


Recognition of Exportable Mangoes

METHODOLOGY

31

Automated System

32

Automated System

33

Automated System

34

Automated System

35

C1: It is feasible to train neural networks for recognition of
exportable mangoes


C2: The recognition of exportable mangoes by Artificial Neural
Networks has reduced the margin of error of 6.7% 2.3%


R1:
In processes where you need recognize one or more species,
types or subsets of elements where the elements that belong to
each type are different but have a common pattern that
identifies them, we recommend to use NN of Multilayer
Perceptron type with algorithm Backpropagation.


R2:
For the success of the pattern recognition is recommended to
analyze and identify properly the characteristic of similarity
between units of the same pattern and the differences between
elements of other patterns


CONCLUSIONS AND


RECOMMENDATIONS

36

THANK YOU VERY MUCH