Semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed
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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.
How to citeChicchón 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: Seventh Annual International Conference, SIMBig 2020, Lima, Perú, October 1–3, 2020, Proceedings, Communications in Computer and Information Science (vol.1410, pp. 236-249). Springer. https://doi.org/10.1007/978-3-030-76228-5_17
Category / SubcategoryPendiente / Pendiente
JournalCommunications in Computer and Information Science
Indexado en Scopus