Hugo Froilán Vega Huerta

Ana María Huayna Dueñas

Artificial Vision for the Recognition of Exportable Mangoes

by Using Neural Networks

UNMSM

Antecedents

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Antecedents

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Percentages of Export for Types of Mangoes

Antecedents

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Antecedents

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

Antecedents

Antecedents

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THE PROBLEM

Statistics plant selection (purification of mangoes malformed)

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¿ How the neural networks allow the recognition of the quality of export

mangoes in Biofruit?

THE PROBLEM

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•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

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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

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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

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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)

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COMPARATIVE EVALUATION OF METHODS OF PATTERN RECOGNITION (Eybi Gil Z, 2010)

STATE OF THE ART

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STATE OF THE ART

COMPARATIVE EVALUATION OF METHODS OF PATTERN RECOGNITION (Eybi Gil Z, 2010)

METHODOLOGYNeural Network For Recognition of Exportable Mangos

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METHODOLOGYArtificial Vision

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METHODOLOGYArtificial Vision

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METHODOLOGYArtificial Vision

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METHODOLOGYArtificial Vision

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METHODOLOGYArtificial Vision

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Functional dependency between input and output datain a Neural Network

METHODOLOGY Recognition of Exportable Mangos

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Architecture of the NN for the recognition of

mangoes METHODOLOGY

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Knowledge Base for Neuronal Network Training

METHODOLOGY

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Knowledge Base for Neuronal Network Training

METHODOLOGY

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Knowledge Base for Neuronal Network Training

METHODOLOGY

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Neural Network TrainingMETHODOLOGY

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Recognition of Exportable Mangoes

¡ Exportable Mangoes !

¿ Exportable Mangoes? ? ? ?

METHODOLOGY

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Recognition of Exportable Mangoes

METHODOLOGY

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We execute the program of Recognition

Interpretation Output information

Recognition of Exportable Mangoes METHODOLOGY

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Automated System

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Automated System

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Automated System

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Automated System

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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

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THANK YOU VERY MUCHTHANK YOU VERY MUCH