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

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Information Management and Big Data (SIMBig 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1410))

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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.

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Authors and Affiliations

  1. Exponential Technology Group (GITX-ULIMA), Institute of Scientific Research (IDIC), University of Lima, Lima, Peru

    Miguel Chicchón

  2. Pontificia Universidad Católica del Perú, Lima, Peru

    Ronny Huerta

Authors
  1. Miguel Chicchón
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  2. Ronny Huerta
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Correspondence to Miguel Chicchón .

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Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

  3. University of Valencia, Valencia, Spain

    Eduardo Díaz

  4. Universidad del Pacífico, Lima, Peru

    Hugo Alatrista-Salas

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Chicchón, M., Huerta, R. (2021). Semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed. In: Lossio-Ventura, J.A., Valverde-Rebaza, J.C., Díaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-76228-5_17

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  • DOI: https://doi.org/10.1007/978-3-030-76228-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-76227-8

  • Online ISBN: 978-3-030-76228-5

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