Semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed

Miguel Chicchón; Ronny Huerta

doi:10.1007/978-3-030-76228-5_17

Semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed

Miguel Chicchón, Ronny Huerta

Producción científica: Libro o Capítulo del libro › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

Peru is one of the main producers of a wide variety of native potatoes in the world. Nevertheless, to achieve a competitive export of derived products is necessary to implement automation tasks in the production process. Nowadays, volume measurements of native potatoes are done manually, increasing production costs. To reduce these costs, a deep approach based on convolutional neural networks have been developed, tested, and evaluated, using a portable machine vision system to improve high-speed native potato volume estimations. The system was tested under different conditions and was able to detect volume with up to 90% of accuracy.

Idioma original	Inglés estadounidense
Título de la publicación alojada	Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
Editores	Juan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	236-249
-	14
ISBN (versión impresa)	9783030762278
DOI	https://doi.org/10.1007/978-3-030-76228-5_17
Estado	Indizado - 2021
Publicado de forma externa	Sí
Evento	7th Annual International Conference on Information Management and Big Data, SIMBig 2020 - Virtual, Online Duración: 1 oct. 2020 → 3 oct. 2020

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	1410 CCIS
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

Conferencia	7th Annual International Conference on Information Management and Big Data, SIMBig 2020
Ciudad	Virtual, Online
Período	1/10/20 → 3/10/20

Nota bibliográfica

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Acceder al documento

10.1007/978-3-030-76228-5_17

Citar esto

Chicchón, M., & Huerta, R. (2021). Semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed. En J. A. Lossio-Ventura, J. C. Valverde-Rebaza, E. Díaz, & H. Alatrista-Salas (Eds.), Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings (pp. 236-249). (Communications in Computer and Information Science; Vol. 1410 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-76228-5_17

Chicchón, Miguel ; Huerta, Ronny. / Semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed. Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. editor / Juan Antonio Lossio-Ventura ; Jorge Carlos Valverde-Rebaza ; Eduardo Díaz ; Hugo Alatrista-Salas. Springer Science and Business Media Deutschland GmbH, 2021. pp. 236-249 (Communications in Computer and Information Science).

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title = "Semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed",

abstract = "Peru is one of the main producers of a wide variety of native potatoes in the world. Nevertheless, to achieve a competitive export of derived products is necessary to implement automation tasks in the production process. Nowadays, volume measurements of native potatoes are done manually, increasing production costs. To reduce these costs, a deep approach based on convolutional neural networks have been developed, tested, and evaluated, using a portable machine vision system to improve high-speed native potato volume estimations. The system was tested under different conditions and was able to detect volume with up to 90% of accuracy.",

keywords = "Convolutional neural network, SegNet, Semantic segmentation, Transfer learning",

author = "Miguel Chicch{\'o}n and Ronny Huerta",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 7th Annual International Conference on Information Management and Big Data, SIMBig 2020 ; Conference date: 01-10-2020 Through 03-10-2020",

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Chicchón, M & Huerta, R 2021, Semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed. En JA Lossio-Ventura, JC Valverde-Rebaza, E Díaz & H Alatrista-Salas (eds.), Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. Communications in Computer and Information Science, vol. 1410 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 236-249, 7th Annual International Conference on Information Management and Big Data, SIMBig 2020, Virtual, Online, 1/10/20. https://doi.org/10.1007/978-3-030-76228-5_17

Semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed. / Chicchón, Miguel; Huerta, Ronny.
Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. ed. / Juan Antonio Lossio-Ventura; Jorge Carlos Valverde-Rebaza; Eduardo Díaz; Hugo Alatrista-Salas. Springer Science and Business Media Deutschland GmbH, 2021. p. 236-249 (Communications in Computer and Information Science; Vol. 1410 CCIS).

Producción científica: Libro o Capítulo del libro › Contribución a la conferencia › revisión exhaustiva

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T1 - Semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed

AU - Chicchón, Miguel

AU - Huerta, Ronny

PY - 2021

Y1 - 2021

N2 - Peru is one of the main producers of a wide variety of native potatoes in the world. Nevertheless, to achieve a competitive export of derived products is necessary to implement automation tasks in the production process. Nowadays, volume measurements of native potatoes are done manually, increasing production costs. To reduce these costs, a deep approach based on convolutional neural networks have been developed, tested, and evaluated, using a portable machine vision system to improve high-speed native potato volume estimations. The system was tested under different conditions and was able to detect volume with up to 90% of accuracy.

AB - Peru is one of the main producers of a wide variety of native potatoes in the world. Nevertheless, to achieve a competitive export of derived products is necessary to implement automation tasks in the production process. Nowadays, volume measurements of native potatoes are done manually, increasing production costs. To reduce these costs, a deep approach based on convolutional neural networks have been developed, tested, and evaluated, using a portable machine vision system to improve high-speed native potato volume estimations. The system was tested under different conditions and was able to detect volume with up to 90% of accuracy.

KW - Convolutional neural network

KW - SegNet

KW - Semantic segmentation

KW - Transfer learning

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U2 - 10.1007/978-3-030-76228-5_17

DO - 10.1007/978-3-030-76228-5_17

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BT - Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings

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A2 - Valverde-Rebaza, Jorge Carlos

A2 - Díaz, Eduardo

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Chicchón M, Huerta R. Semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed. En Lossio-Ventura JA, Valverde-Rebaza JC, Díaz E, Alatrista-Salas H, editores, Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 236-249. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-76228-5_17