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Deploying Deep Neural Networks on Edge Devices for Grape Segmentation

Part of the Communications in Computer and Information Science book series (CCIS,volume 1470)


Deep learning (DL) is a hot trend for object detection and segmentation, thanks to the use of Deep Neural Networks (DNNs). Image recognition is a powerful tool for precision viticulture, having a strong potential in cases such as yield estimation and automatic quality estimation of the grapes. Developing the models is one part of the problem, deploying them in the field, at the edge of the network, is another problem that comes with its own constraints. This paper studies the use of embedded devices to run Deep Neural Network algorithms for real-time grape segmentation at the wine press.


  • Grape detection
  • Precision viticulture
  • Deep learning
  • Edge computing

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This work has been performed in the project AI4DI: Artificial Intelligence for Digitizing Industry, under grant agreement No 826060. The project is co-funded by grants from Germany, Austria, Finland, France, Norway, Latvia, Belgium, Italy, Switzerland, and the Czech Republic and - Electronic Component Systems for European Leadership Joint Undertaking (ECSEL JU).

We also would like to thank the ROMEO Computing Center ( of the University of Reims Champagne-Ardenne, where part of the models were developed, and Vranken-Pommery Monopole, our partner in the AI4DI project, for allowing image collection in their vineyards and facilities.

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Correspondence to Luiz Angelo Steffenel .

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Roesler, M., Mohimont, L., Alin, F., Gaveau, N., Steffenel, L.A. (2021). Deploying Deep Neural Networks on Edge Devices for Grape Segmentation. In: Boumerdassi, S., Ghogho, M., Renault, É. (eds) Smart and Sustainable Agriculture. SSA 2021. Communications in Computer and Information Science, vol 1470. Springer, Cham.

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