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Dispositivo que permite la identificación por tipo y tamaño de la naranjilla y del tomate de árbol mediante visión artificial para mejorar la producción en PYMES

 

Device that allows the identification by type and size of the naranjilla and the tree tomato by artificial vision to improve production in SMEs

1

Vanessa Maribel Ati Andaluz                                = https://orcid.org/0000-0003-2999-1840

Ingeniera en Electrónica y Automatización, Escu= ela Superior Politécnica de Chimborazo (ESPOCH)

vane22_mj@outlook.com

2

Cris= tian Antonio Erazo Peñafiel                                            https://orcid.org/0000-0001-5378-2016<= o:p>

Ingeniero en Electrónica y Automatización, Escue= la Superior Politécnica de Chimborazo (ESPOCH)

cristronick@gmail.com

3

Diana Carolina Ati Andaluz                                   https://orcid.org/0000-0003-1452-4801<= o:p>

Ingeniera en Electrónica, Telecomunicaciones y Redes, Escuela Superior Politécnica de Chimborazo (ESPOCH)

carolina16_andaluz@yahoo.es<= /a>

4

Oswa= ldo Geovanny Martinez                                  https://orcid.org/0000-0001-9018-7777<= o:p>

Máster Universitario= En Sistemas Tecnologías y Redes De Comunicaciones, Escuela Superior Politécn= ica de Chimborazo (ESPOCH), Facultad de informática y Electrónica<= /span>

omartinez@espoch.edu.ec<= span style=3D'mso-bookmark:_Hlk497040450'>

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Artículo de Investigación Científica y Tecnológica

Enviado: 13/05/2022

Revisado: 28/06/2022

Aceptado: 15/07/2022

Publicado:05/08/2022

DOI:     = https://doi.org/10.33262/ap.v4i3.1.2= 42        

 

 

 

 

Cítese:

 

 

Ati Andaluz, V. M., Erazo Peñafiel, C. = A., Ati Andaluz, D. C., & Geovanny Martinez, O. (2022). Dispositivo que permite la identificación por tipo y tamaño de la naranjilla y del tomate= de árbol mediante visión artificial para mejorar la producción en PYMES. AlfaPublicaciones, 4(3.1), 127–147. https://doi.org/10.33262/ap.v4i3.1.242

 =

 

 

ALFA PUBLICACIONES, es una Revista Multidisciplinar, Trimestral, que se publicará en soporte electrón= ico tiene como misión contribuir a la   formación de  profesionales competentes con visión humanística y crítica que sean capac= es de  exponer  sus resultados  investigativos y científicos = en la misma medida que se promueva mediante su intervención cambios positivos en  la sociedad. https:/= /alfapublicaciones.com  

3Deditorial1.png<= span style=3D'mso-bookmark:_Hlk497040450'>La revista es editada= por la Editorial Ciencia Digital (Editorial de prestigio registrada en la Cám= ara Ecuatoriana de Libro con No de Afiliación 663) www.cel= ibro.org.ec

&nb= sp;

 

 

Esta revista está protegida bajo una licencia Creative Commons AttributionNonCommercialNoDerivatives 4.0 International. Copia de la licencia: http://creativecommons.org/licenses/by-nc-nd/= 4.0/

 

Palabras claves: Visión artificial, Identificador de fru= tas, Algoritmo de identificación, Red neuronal convolucional, Interfaz gráfica, Banda transportadora.

 

 

Resumen

El presente trabajo consistió en el diseño e implementación de un prototipo que permita la identificación por tipo y tamaño de la naranjilla y del tomate de árbol, para poder lograrlo se uti= lizó un sistema de visión artificial por computador basado en redes neuronales= . El prototipo dispone de tres sistemas: el sistema de adquisición de imágenes conformado por una cámara web e iluminación para la captura de datos; el sistema de visión artificial utiliza el método de procesamiento de imágen= es para realizar la clasificación por tamaño y redes neuronales para la clasificación por tipo, finalmente el sistema mecánico está conformado po= r un sistema de almacenamiento y una banda transportadora. Para el entrenamien= to de la Red Neuronal Artificial se utilizó el algoritmo convolucional que a través de la adquisición de imágenes entrena el identificador, se estable= ció tres categorías: tomate de árbol, naranjilla y objeto desconocido, cada u= na posee una base de datos. Los algoritmos de programación fueron desarrolla= dos en Raspbian, con lenguaje de programación Python utilizando librerías de OpenCV y TensorFlow. La interfaz gráfica fue desarrollada mediante la biblioteca Tkinter, permitiendo al usuario controlar seis botones: iniciar cámara, tomar foto, cargar foto, tipo de fruta, tamaño de fruta, cerrar programa. En base a las pruebas realizadas, se establece que el sistema funciona de manera óptima con un 95% al detectar el tipo de fruta, el 93% para identificar el tamaño, el tiempo de respuesta oscila entre 0.3 y 1.3 segundos. Se concluye que el prototipo logra la identificación de los fru= tos por su tipo y tamaño con un margen de error mínimo.

 

 

Keywords: Machine vision, Fr= uit identifier, Identification algorithm, Convolutional neural network, Graph= ical interface, Conveyor belt.

<= /span>

 

Abstract

The present work consisted of t= he design and implementation of a prototype that allows the identification by type and size of the orange tree and the tree tomato, to achieve this, a computer vision system based on neural networks was used. The prototype h= as three systems: the image acquisition system consisting of a webcam and lighting for data capture; the artificial vision system uses the image processing method to perform the classification by size and neural networ= ks for the classification by type, finally the mechanical system is made up = of a storage system and a conveyor belt. For the training of the Artificial Ne= ural Network, the convolutional algorithm was used that through the acquisitio= n of images trains the identifier, three categories were established: tree tom= ato, naranjilla and unknown object, each one has a database. The programming algorithms were developed in Raspbian, with Python programming language u= sing OpenCV and TensorFlow libraries. The graphical interface was developed us= ing the Tkinter library, allowing the user to control six buttons: start came= ra, take photo, upload photo, fruit type, fruit size, close program. Based on= the tests conducted, it is established that the system works optimally with 9= 5% when detecting the type of fruit, 93% to identify the size, the response = time ranges between 0.3 and 1.3 seconds. It is concluded that the prototype achieves the identification of the fruits by their type and size with a minimum margin of error.

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Introducción

El sistema de visión por computador en la agricultura ha aumentado de forma considerable en los últimos tiempos, principalmente en la monitorización de cultivos, agricultura de precisión, identificación en líneas de procesamien= to y su mayor auge en la automatización de procesos. La principal característica= de este sistema es proporcionar cantidades de información acerca de la natural= eza y atributos de la escena analizada. La visión artificial representa una de = las herramientas transversales más relevantes dentro de la industria 4.0, esta tecnología es rápida, precisa, no es destructiva o invasiva para los alimen= tos y evita la inspección humana, que en jornadas largas tiende a ser costosa, subjetiva e imprecisa. En Ecuador el uso de la Inteligencia Artificial dent= ro de la automatización de procesos es muy baja en las microempresas y pymes, = un sistema de visión artificial que sea capaz de detectar el tipo y tamaño de = fruta garantizará una identificación de mayor calidad, obviando la subjetividad d= el humano al momento de realizar dicho trabajo.

Objetivos. - Realizar un prototipo que permita la clasificación= por tipo y tamaño de frutas mediante visión artificial teniendo como fin mejora= r y automatizar los procesos de selección y clasificación de productos dentro de las microempresas agrícolas.

Diseñar un prototipo que ejecute la clasificación de frutas y que permita cumplir c= on los requerimientos tales como: capturar las imágenes en alta resolución par= a la correcta identificación de frutas, controlar la posición de la fruta para su identificación, diseñar un sistema de transporte y almacenamiento óptimo pa= ra el traslado de la fruta evitando en todo momento la manipulación humana, establecer un límite de tiempo para el paso de cada fruta por la banda transportadora y contabilizar las frutas identificadas por categoría.<= /o:p>

Metodología

A continuación, detallaremos los principales métodos y técnicas que fueron utilizados para la implementación del dispositivo propuesto en este trabajo= de investigación.

Normas INEN para la identificació= n de la naranjilla y tomate de árbol

Identificación de la naranjilla. - La norma INEN 2 303:2009 que se muestra en la tabla 1, manifiesta que el tamaño o calibre de la Naranjilla se determina por el diámetro en mm, la longitud en mm y la masa en g.

Tabla 0(Alonso= , 2009, p.1)

La IA permite que los sistemas tecnológicos perciban su entorno, se relacionen= con él, resuelvan problemas y actúen con un fin específico. La máquina recibe d= atos (ya preparados o recopilados a través de sus propios sensores, por ejemplo,= una cámara), los procesa y responde a ellos (Notici= as Parlamento Europeo, 2021, p.1)

 

Sistemas de Visión Artificial

Es un subcampo de la Inteligencia artificial que a partir de diversas tecnolog= ías permite reconocer, procesar y analizar la información mediante imágenes digitales para finalmente tomar decisiones con las características extraída= s (Nogué = & Antiga, 2012, p.66).<= /o:p>

Para desarrollar un sistema de visión artificial que identifique los frutos se requiere definir la arquitectura que posee mediante el hardware/software y = las etapas a seguir. El hardware está compuesto de: una cámara, una tarjeta que permita el procesamiento de las imágenes, la banda transportadora y un sist= ema de almacenamiento y dosificación. En la etapa del software se tiene el algoritmo programado a emplear y la red neuronal para la identificación de objetos (Parra = & Cuervo, 2018, p.3).<= /o:p>

·         Etapas de un Sistema de Visión Artificial

Un sistema de Visión artificial tiene cinco etapas: adquisición de imágenes, preprocesamiento, segmentación, extracción y selección de características y= reconocimiento e interpretación.

Figura 1

Etapas de un sistema de visión artificial

 

Adquisi= ción de Imágenes. -  Consiste en obtener la imagen o secuencia de imágenes que se desea analizar, depende de dos factores: el primero es determinar el objeto de interés (rostros, pinturas, frutas, etc.) y el segundo el propósito de estu= dio (Minard= i, 2014, pp.3,4).<= /o:p>

Preproc= esamiento. - Rep= ara los defectos producidos o no eliminados por el hardware, los algoritmos permiten modificar la imagen eliminando el ruido, mejorando el contraste y realizando una conversión geométrica de la imagen (Gonzál= ez et al., 2016, p.33)

Segment= ación. - Se = basa en la comparación de varias imágenes tomadas en el tiempo por lo que se pue= de detectar el objeto en movimiento y sus características. Una vez segmentada = la imagen original se procede a un análisis, como se muestra en la figura en d= icho análisis pueden ser interesantes características como el área, perímetro, momentos, curvatura (Peña, = 2010, p.10) .

Figura 2

Separación del objeto de estudio, del fondo de la imagen

<= /a> =

Extracc= ión y selección de características. - Este proceso extrae la información cualitativa de los objetos de la imagen segmentada, e= stas características son las entradas de los algoritmos que se encargan de identificar los objetos, estas características morfológicas pueden ser: la textura, color, forma, además se puede obtener información relacionada al c= olor (Herrer= a & Medina, 2015, p.28).<= /o:p>

Reconoc= imiento e Interpretación.- Se encarga de filtrar todas las imágenes capturadas, se realiza el reconocimiento mediante el análisis de fotogramas, para lograr el reconocimiento se utilizan técnicas de procesami= ento de imágenes, visión por computador, reconocimiento de patrones y redes neuronales (Caball= ero, 2017, p.10).<= /o:p>

Redes Neuronales

Las Redes Neuronales (CNN) son esquemas computacionales que tratan de emular el comportamiento del cerebro humano, estas están formadas por múltiples procesadores elementales los cuales poseen una baja capacidad de procesamie= nto.

El algoritmo de predicción toma la imagen capturada por la cámara, carga los modelos creados por la red neuronal convolucional, realiza una comparación e inmediatamente da como resultado el tipo de fruta que se encuentra en la ba= nda transportadora.  =

Algoritmos para la identificación de objetos<= /span>

El reconocimiento de objetos es una técnica de visión artificial para identifi= car objetos en imágenes o vídeos. El reconocimiento de objetos constituye = una salida clave de los algoritmos de aprendizaje automático y profundo = (Mathwo= rks, 2021, p.1). =

Aprendizaje Automático. - Es especializado en el reconocimiento de patrones complejos en un conjunto de datos, en un programa se puede extraer información relevante de los datos procesados sin ser programados para esto. La información adquirida permite = que el programa aprenda y ejecute su tarea de manera sofisticada. Al desarrollar algoritmos eficientes, estos acercamientos permiten identificar relaciones invisibles para el ojo humano (Gonzal= es, 2018, p.1).<= /o:p>

Existen diferentes maneras en las que se puede modelar un algoritmo para que aprend= a de los datos, pero de forma general se han definido las siguientes categorías = de aprendizaje.

Figura 3

Categorías de un algoritmo= de aprendizaje profundo

Fuente:= Altamirano y Pazmiño, (2018,  p.16)

Aprendizaje Profundo. - Forma parte del aprendizaje automático es decir requiere de datos entrenados, pero no necesitan estar etiquetados, es similar al sistema neurológico humano (Universidad Internacional de la Rioja [= UNIR], 2021, p.1).

Este algoritmo posee estadísticas y modelos predictivos, tiene la tarea de recopilar, analizar e interpretar altas cantidades de datos; el aprendizaje profundo hace que este proceso sea más rápido y sencillo (Burns,= 2021, p.1).<= /o:p>

Requerimientos generales

·      =    Capturar las imágenes en alta resolución para identificar las frutas correctamente.

·      =    Seleccionar la tarjeta de control que permita proc= esar los datos de las imágenes capturadas.

·      =    Controlar la posición de la fruta para su identificación.

·      =    Identificar la fruta por su tipo y tamaño.

·      =    Emitir mensaje de alerta cuando se identifique una fruta diferente a las establecidas.

·      =    Diseñar un sistema de transporte y almacenamiento óptimo para el traslado de la fruta.

·      =    Establecer un límite de tiempo para el paso de cada fruta por la banda transportadora.

·      =    Contabilizar las frutas identificadas por categorí= a.

 

Descripción general del sistema

El sistema de visión artificial se encarga del procesamiento digital de imágen= es de las frutas y posteriormente puede hallar sus características como tipo y tamaño, incluye un dispositivo para la captura y visualización, contiene algoritmos y redes neuronales. El sistema mecánico está conformado por los elementos que permiten el transporte e identificación de frutas. Finalmente= , el sistema de control dispone de los sensores que conlleva el prototipo, el cu= al actúa mediante la información que trasmita el sistema de visión artificial y que permite el monitoreo entre HMI.

Figura = 4

Descripción general del sistema

<= !--[if gte vml 1]>

Fuente:= Altami= rano & Pazmiño (2018, p.30)

El     proceso   de   clasificación   que   ha   sido   considerado se basa   en 3   etapas:   Dosificación, Dete= cción   y Actuación.  Las acciones objetivas del sistema están limitadas al accionamiento = de 2 actuadores.  Estos se interpretan c= omo las   variables   de   control   y, en   consecuencia, serán   los   encargados   de   clasificar   las frutas designadas.

Flujograma para la detección del tipo y tamañ= o de las frutas

Una vez obtenida la imagen de la fruta el algoritmo de identificación mediante = una red neuronal convolucional detecta si la imagen obtenida es una naranjilla, tomate de árbol u objeto desconocido.

Esta imagen si es reconocida como naranjilla o tomate de árbol se aplicará un algoritmo de reconocimiento de contornos para obtener el tamaño de dicha fr= uta, después el algoritmo clasificador accionara un servomotor y la banda transportadora para enviar a la fruta a su destino final, caso contrario si esta imagen es reconocida como objeto desconocido solo se accionará la banda trasportadora para enviar a este objeto a su cesta correspondiente.

Figura = 5

Flujograma para el funcionamiento del dispositivo<= /i>

Técnicas para el desarrollo del prototipo

Las técnicas aplicadas para el desarrollo del software son: el procesamiento de imágenes y el aprendizaje profundo. Se realiza una inspección rápida tomand= o en cuenta solo las imágenes capturadas por la cámara Genius FaceCam, es decir = no se realiza ninguna inspección de calidad de la fruta, esto ayuda a reducir = el tiempo de respuesta y análisis.

 

 

 

 

 

Figura 6

Procesamiento de una imagen

<= !--[if mso & !supportInlineShapes & supportFields]> <= span style=3D'font-size:12.0pt;line-height:115%;mso-ansi-language:ES-EC'>

 

Para realizar la predicción del tipo de fruta se utiliza una red neuronal convolucional (CNN), este algoritmo permite entrenar la CNN con imágenes obtenidas de la base de datos donde múltiples capas de filtros convoluciona= les realizan un mapeo, después se aplica una reducción por muestreo y por últim= o se obtiene neuronas de perceptrón más sencillas para realizar la clasificación final sobre las características extraídas.

Detectar presencia de frutas<= /i>

En esta etapa se trata de detectar la fruta sobre la banda transportadora para capturar una imagen y después continuar con las instrucciones del programa,= la resolución de las imágenes es de 260x318 pixeles, la fruta se encuentra baj= o una caja en donde está ubicada la cámara y sobre una banda transportadora, la c= aja posee un sensor de distancia el cual una vez que detecta la fruta envía una señal a la Raspberry, este a su  ve= z  apaga el motor DC  a través del módulo L298  y de esta manera detiene el movimiento = de la banda trasportadora.

Algoritmo de entrenamiento y predicción

El algoritmo de predicción toma la imagen capturada por la cámara, carga los modelos creados por la red neuronal convolucional, realiza una comparación e inmediatamente da como resultado si el objeto a identificar es naranjilla, tomate de árbol u objeto no identificado, en la figura 7 se aprecia el algoritmo de predicción y su respuesta.

 

 

 

 

 

 

Figura 7

Algoritmo de predicción

<= !--[if mso & !supportInlineShapes & supportFields]> <= span style=3D'font-size:12.0pt;line-height:115%;mso-ansi-language:ES-EC'>

 

Estimación de las dimensiones de las frutas

Después de realizar la predicción del tipo de fruta, se le debe aplicar el método de FindCountours para detectar los contornos de la fruta para ello es necesario transformar las imágenes al espacio de color HSV, se delimita un rango de c= olor para la naranjilla y para el tomate de árbol. Los contornos de la figura son encerrados en un pequeño cuadrado, este posee el número de pixeles de dicha imagen, donde se le aplica una regla de tres para obtener el área de la fru= ta en  , se usó como referencia los pixeles que posee una cuadrado de .

Figura 8

Estimación de las dimensiones de la fruta

 

 

Selección de Hardware<= /span>

Como elemento central de procesamiento se selecciona una tarjeta Raspberry Pi 3 = B. Los periféricos de comunicación de este sistema embebido son las vías para = el envío y recepción de datos. La tarjeta Raspberry emite    las =    órdenes    a    los =    diferentes    actuadores    empleados   en   el   proceso para el correcto funcionamient= o de la banda transportadora y del sistema de clasificación del prototipo. 

Figura 9

Esquema de conexión del hardware del prototipo=

3D"Diagrama,

Implementación del software=

Para diseñar el software encargado de identificar el tipo de fruta y clasificar según su tamaño, se requiere determinar las funciones que va a tener, por ejemplo: Detectar la fruta en la banda transportadora, identificar el tipo = de fruta, clasificar la fruta según su tamaño, contabilizar el total de frutas detectadas.

&n= bsp;

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&n= bsp;

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Figura = 10

Impleme= ntación y funcionamiento del dispositivo

&n= bsp;

Interfaz del sistema

El diseño de la interfaz gráfica para el prototipo de identificación fue elabo= rado en Python y la librería Tkinter que es una capa orientada a objetos de forma sencilla y versátil. La interfaz cuenta con widgets desde la ventana, boton= es, etiquetas de texto. La primera ventana al ejecutar el programa es fácil de manipular, cuenta con un solo botón para el paso a la segunda ventana.=

Figura 11

Interfaz Gráfica del menú principal

Al momento de ingresar a la segunda ventana denominada Identificación de Fruta= s, se puede visualizar en la parte izquierda un cuadrado donde se proyecta la cámara, en la parte intermedia existe seis opciones: abrir cámara, tomar la foto, cargar foto, tipo de fruta, tamaño de la fruta y por último cierra el programa. En la parte derecha se muestra la imagen procesada y en la parte inferior izquierda se muestra el número de tomates de árbol, naranjillas y objetos desconocidos que fueron contabilizados.

Figura 12

Interfaz gráfica de la programación

 

Resultados

Pruebas de identificación

En esta prueba se realiza dos tipos de identificación: por tipo (naranjilla y tomate de árbol) y tamaño (grande, mediano y pequeño).

Identificación por tipo

La primera prueba que se realiza es la identificación de fruta según su tipo. = Las frutas que se identifica son tomates de árbol y naranjillas, para esto se emplea la red neuronal convolucional (CNN) entrenada con una base de datos = de 1100 imágenes.

 <= /p>

&n= bsp;

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Tabla 3

Resulta= do de la prueba de identificación por tipo

Frut= a

Prue= ba

Mues= tras

Acie= rtos

Fall= as

%

Naranjilla

1

15

14

1

93.33

2

15

15

0

100.00

Tomate de Árbol

3

15

14

1

93.33

4

15

15

0

100.00

Objeto desconocido<= /span>

5

15

14

1

93.33

6

15

15

0

100<= /o:p>

TOTAL

90

87

3

&n= bsp;

Se ejecutaron un total de 90 pruebas para determinar el funcionamiento del prototipo. Una vez analizados los datos recolectados, se presenta los siguientes resultados: se ha obtenido el 95.56% de pruebas acertadas y el 4= .44% de pruebas fallidas como se muestra en el Gráfico 2-3 Las fallas se present= aron debido a que el tomate de árbol tenía irregularidades en su forma, el objet= o no identificado no formaba parte de la adquisición de imágenes de la red neuro= nal y en la naranjilla existió un problema con la ejecución del programa.

Identif= icación por tamaño

La segunda prueba es la identificación según el tamaño de las frutas, para realizar esta identificación se usa redes neuronales, se procesa las imágen= es y se mide los contornos de las frutas. Existen tres categorías de identificac= ión: pequeña, mediana y grande.

Tabla 4

Resulta= do de la prueba de identificación por tamaño del tomate de árbol

Prueba

Tamaño Real Diámetro (Mm)

Tamaño Estimado

Categoría

Acierto

1

62

63

Grande

Si

2

60

59

Grande

Si

3

45

47

Mediana

Si

4

48

51

Mediana

Si

5

40

39

Pequeña

No

6

44

43

Pequeña

Si

7

56

55

Grande

Si

8

39

41

Pequeña

No

9

55

56

Grande

Si

10

53

56

Mediana

Si

11

52

50

Mediana

Si

12

54

55

Mediana

Si

Tabla 4=

Resultado de la prueba de identificación por tamaño del tomate de árbol (continuación)

Prueba

Tamaño Real Diámetro (Mm)

Tamaño Estimado

Categoría

Acierto

13

62

63

Grande

Si

14

41

40

Pequeña

Si

15

44

46

Pequeña

No

En base a la tabla anterior se determinó que hubo 3 fallas al determ= inar el tamaño. Se realiza la misma prueba para la clasificación de las naranjil= las por tamaño.

Tabla 5

Resulta= do de la prueba de identificación por tamaño del tomate de árbol

Prueba

Tamaño Real Diámetro (Mm)

Tamaño Estimado

Categoría

Acierto

1

50

52

Mediana

No

2

55

57

Grande

Si

3

40

39

Pequeña

Si

4

38

38

Pequeña

Si

5

52

51

Grande

No

6

51

53

Grande

Si

7

44

46

Grande

No

8

48

47

Mediana

SI

9

45

44

Mediana

No

10

41

43

Pequeña

Si

11

29

31

Pequeña

Si

12

38

41

Pequeña

Si

13

36

38

Pequeña

SI

14

33

35

Pequeña

Si

15

30

33

Pequeña

SI

Prueba de separación de frutas

El propósito de esta prueba es determinar que las frutas se trasladen de forma exitosa a sus respectivos contenedores, mediante el uso de 2 servomotores q= ue funcionarán junto a la banda, en el caso que exista una fruta desconocida s= olo se accionara la banda.

&n= bsp;

Separación de las = frutas por tipo

Para la evaluación del funcionamiento de separación de frutos del prototipo, se ejecutaron 25 pruebas, la tabla presenta los resultados obtenidos. La prime= ra columna representa el número de pruebas, la segunda el tipo de fruta: tomat= e de árbol, naranjilla y objeto desconocido, finalmente en la tercera columna se asigna un Sí o No, dependiendo si la fr= uta fue dirigida al contenedor correcto o incorrecto.

Tabla 6=

Resultados de la prueba de separación por tip= o

Numero de prueba

Fruta

Separa

1

Tomate de árbol

Si

2

Tomate de árbol

Si

3

Tomate de árbol

Si

4

Tomate de árbol

Si

5

Tomate de árbol

No

6

Tomate de árbol

Si

7

Tomate de árbol

Si

8

Tomate de árbol

Si

9

Tomate de árbol

Si

10

Naranjilla

Si

11

Naranjilla

Si

12

Naranjilla

Si

13

Naranjilla

Si

14

Naranjilla

Si

15

Naranjilla

Si

16

Naranjilla

Si

17

Naranjilla

No

18

Naranjilla

Si

19

Naranjilla

Si

20

Naranjilla

Si

21

Objeto desconocido

Si

22

Objeto desconocido

Si

23

Objeto desconocido

Si

24

Objeto desconocido

Si

25

Objeto desconocido

Si

&n= bsp;

Se ejecutaron un total de 25 pruebas para separar las frutas. Una vez que los datos fueron recolectados, se presentan los siguientes resultados: 23 frutas fueron separadas de forma correcta a sus respectivos contenedores, 2 frutas presentaron falla debido a un error de identificación.

Análisis de Costos

Para el análisis económico se dividió en los diferentes sistemas y componentes q= ue conforman el prototipo. Donde se observa que el costo total de la implementación es de 806,59 dólares americanos.

Tabla 7

Costo y descripción de materiales

Sist= emas

Comp= onentes

Cant= idad

P. U=

P.T = ($)

 

 

 

Sistema de Transporte

Banda

1

20,00

20,00

Chumaceras

4

2,00

8,00

Motor DC<= o:p>

1

29,90

29,90

Rodillos

2

8,50

17,00

Ejes=

2

5,80=

11,60

Acople Eje-Rodillo

4

4,00

16,00

Sistema de Almacenamiento

1/2 Lamin= a de INOX

1

20,00

20,00

Servomotor

2

5,50

11,00

 

 

 

 

Sistema de Adquisición de imágenes

Cámara We= b

1

21,00

21,00

SENSOR E18-D80NK

1

9,90

9,90

LED HI-SP= OT GU5.3

2

2,50=

5,00=

Pantalla LCD

1

380,50

380,50

Tarjeta microSD

1

17,90

17,90

Raspberry Pi3

1

84,99

84,99

Lamina de madera 8 líneas

1

15,80

15,80

 

Sist= ema de Clasificación

Servomotor

2

5,50

11,00

Aluminio = Tipo L

2

15,00

30,00

Base de motor

2

3,50

7,00

Estructura

Tubo cuad= rado de aluminio

4

22,50

90,00

COSTO TOT= AL

$ 806,59

 

Este sistema permite la identificación por tipo y tamaño del tomate de árbol y naranjilla, al ser comparado con otros sistemas comerciales que permiten el control de una sola variable de identificación. Además, que su costo se ve reducido en un 60%. En vista de esta relación y comparando costos mostrados= en la tabla se concluye que el sistema contiene un muy bajo costo de implementación.

Conclusiones

·      =    Se diseñó e implementó un sistema de visión artifi= cial para la identificación de frutas, a través de la adquisición de imágenes con una cámara web y el uso de una Red Neuronal con un total del 95% de acierto= s en la identificación por tipo y 93% por tamaño.

·&nb= sp;        El estado alto de madurez del Tomate de Árbol por = su pigmentación generaba errores de identificación del tipo de fruta, dando co= mo resultado una Naranjilla y existió errores con el tamaño de la fruta debido= a la iluminación generada por el entorno.

·&nb= sp;        Se implementó un sistema de identificación para las frutas que está conformado por un sistema de transporte, sistema de visión artificial y sistema mecánico, que son accionados por un Raspberry Pi 3b a través de la comunicación PtP. 

·&nb= sp;        Se realizó un algoritmo de procesamiento de imágen= es con OpenCV y Python para estimar las dimensiones de las frutas e identifica= rlas según su tamaño: grande, mediano o pequeño. <= /p>

Referencias Bibliográficas

Alonso, Laura.  (2009). Inteligencia Artificial 2009. http= s://www.cs.famaf.unc.edu.ar/~laura/ia/ia.html.

Altamirano, Victor & Pazmiño, Esteban. (2018). Diseño e implementación de un módulo didáctico de inspección y clasificación de frut= as usando visión artificial por medio de imágenes fuera del espectro visible p= ara el laboratorio de mecatrónica de la Universidad de las Fuerzas Armadas Espe= extensión Latacunga. [Tesis de pregrado, Escuela Superior Politécnica del Ejércit= o] http://repositorio.espe.edu.ec/bitstream/21000/14945/1/T-ESPEL-MEC-0146.pdf= .

Burns, E.D. (2021) Aprendizaje profundo (deep learning). https://www.computerweekly.com/es/definicion/Aprendizaje-profundo-deep-lear= ning.

Caballero, Edison. (2017). Aplicación práctica de la visión artifi= cial para el reconocimiento de rostros en una imagen, utilizando redes neuronale= s y algoritmos de reconocimiento de objetos de la biblioteca opencv. [Tesis= de pregrado, Universidad Distrital Francisco José de Caldas]. https://reposito= ry.udistrital.edu.co/bitstream/handle/11349/6104/CaballeroBarrigaEdisonRene= 2017.pdf

Gonzales, Carlos. (2018). En = qué consiste el Aprendizaje Automático (Machine Learning) y qué está aportando = a la Neurociencia Cognitiva. http://www.cienciacognitiva.org/?p=3D1697.

González, Ana., Martínez, F.J., Pernía, A.V., Elias, F.A., Castejón, = M., Ordieres, J., & Vergara, E. (2016). técnicas y algoritmos básicos de visión artificial. Universidad de la Rioja.<= /i> Logroño - España. Editorial Anaya Multimedia. p. 33.  https://publicaciones.unirioja.es/catalogo/online/VisionArtificial.p= df.

Herrera, Jean., & Medina, Silfri. (2015). Diseño de un sistema automático de selección de frutos de café mediante técnicas de visión artificial [en línea]. [Tesis de posgrado, Universidad Autónoma del Caribe]. http://repositorio.uac.edu.co/bitstream/handle/11619/1656/TMCT 0022C.pdf

Instituto Ecuatoriano de Normalización [INEN]. (2010). Instituto Ecuatoriano de Normalización 2303: Frutas Frescas, Naranjilla, Requisitos. https://181.112.149.204/buzon/normas/2303.pdf

Instituto Ecuatoriano de Normalización [INEN].  (2006). Instituto Ecuatoriano de Normalización 1909: Frutas Frescas, Tomate de Árbol, Requisitos.  = https://www.normalizacion.gob.ec/buzon/normas/1909-1.pdf<= /p>

Mathworks. (2021).  Image Processing and Computer Vision. https://es.mathworks.com/solutions/image-video-processing/object-recognitio= n.html.

Minardi, F. (2014). Visión Artificial. http://jeuazarru.com/wp-content/uploads/2014/10/computer_vision.pdf. <= /o:p>

Nogué, Al., & Antiga, J. (2012). Aplicación práctica de la visión artificial en el control de procesos industriales. http://www.infoplc.net/f= iles/documentacion/vision_artificial/infoPLC_net_Conocimientos_visionartifi= cial.pdf.

Noticias Parlamento Europeo. (2021). ¿Qué es la inteligencia artificial y cómo se usa? https://www.europarl.europa.eu/news/es/headli= nes/society/20200827STO85804/que-es-la-inteligencia-artificial-y-como-se-us= a

Parra, S., & Cuervo, W. (2018). Sistema de visión artificial integrado a plataforma aérea para la detección de personas en tiempo real. https://repository.unimilitar.edu.co/handle/10654/17513.<= /p>

Peña Baeza, A. (2010). Módulo de visión artificial del robot human= oide HOAP3. Aplicación al seguimiento de objetivos móviles. http://orff.uc3m.es/handle/10016/10754.

Universidad Internacional de la Rioja [UNIR]. (2021). Deep learnin= g: en qué consiste, ejemplos y aplicaciones. https://www.unir.net/ingenieria/revista/deep-learning/. <= /p>

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3Deditorial1.png

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                                      Científico e Industrial              Página 127 | 1

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