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Hugo Froilán Vega Huerta Ana María Huayna Dueñas

Artificial Vision for the Recognition of Exportable Mangoes by Using Neural Networks. Hugo Froilán Vega Huerta Ana María Huayna Dueñas. UNMSM. Antecedents. Antecedents. 3. Antecedents. Percentages of Export for Types of Mangoes. 4. Antecedents. 5. Antecedents.

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Hugo Froilán Vega Huerta Ana María Huayna Dueñas

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  1. Artificial Vision for the Recognition of Exportable Mangoes by Using Neural Networks Hugo Froilán Vega Huerta Ana María Huayna Dueñas UNMSM

  2. Antecedents

  3. Antecedents 3

  4. Antecedents Percentages of Export for Types of Mangoes 4

  5. Antecedents 5

  6. Antecedents BRIOFRUIT staff are separating mangoes that won’t be exportable. 6

  7. Antecedents

  8. THE PROBLEM Statistics plant selection (purification of mangoes malformed) 8

  9. THE PROBLEM ¿ How the neural networks allow the recognition of the quality of export mangoes in Biofruit? 9

  10. OBJECTIVES • 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%. 10

  11. Theoretical Framework DEFINITION [James A. Anderson 2007] Is a setof 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 11

  12. Theoretical Framework 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. 12

  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) 13

  14. STATE OF THE ART • COMPARATIVE EVALUATION OF METHODS OF PATTERN RECOGNITION (Eybi Gil Z, 2010) 14

  15. STATE OF THE ART • COMPARATIVE EVALUATION OF METHODS OF PATTERN RECOGNITION (Eybi Gil Z, 2010) 15

  16. METHODOLOGYNeural Network For Recognition of Exportable Mangos

  17. METHODOLOGYArtificial Vision

  18. METHODOLOGYArtificial Vision

  19. METHODOLOGYArtificial Vision

  20. METHODOLOGYArtificial Vision

  21. METHODOLOGYArtificial Vision

  22. METHODOLOGY Recognition of Exportable Mangos Functional dependency between input and output datain a Neural Network

  23. METHODOLOGY Architecture of the NN for the recognition of mangoes

  24. METHODOLOGY Knowledge Base for Neuronal Network Training

  25. METHODOLOGY Knowledge Base for Neuronal Network Training

  26. METHODOLOGY Knowledge Base for Neuronal Network Training

  27. METHODOLOGY Neural Network Training

  28. METHODOLOGY Recognition of Exportable Mangoes • ¡ Exportable Mangoes ! • ¿ Exportable Mangoes? ? ? ?

  29. METHODOLOGY Recognition of Exportable Mangoes

  30. METHODOLOGY Recognition of Exportable Mangoes • We execute the program of Recognition • Interpretation • Output information

  31. Automated System

  32. Automated System

  33. Automated System

  34. Automated System

  35. CONCLUSIONS AND RECOMMENDATIONS • 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

  36. THANK YOU VERY MUCH

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