Data for: Branch Detection for Apple Trees Trained in Fruiting Wall Architecture using Depth Features and Regions-Convolutional Neural Network (R-CNN)

Published: 14 Nov 2018 | Version 8 | DOI: 10.17632/kvpmv75hvg.8
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Description of this data

This data mainly includes the testing images of apple tree branches in our paper, and the corresponding R-CNN detection results.

Experiment data files

Latest version

  • Version 8

    2018-11-14

    Published: 2018-11-14

    DOI: 10.17632/kvpmv75hvg.8

    Cite this dataset

    Zhang, Jing; He, Long; Gao, Zongmei; Zhang, Xin; Zhang, Qin; Karkee, Manoj (2018), “Data for: Branch Detection for Apple Trees Trained in Fruiting Wall Architecture using Depth Features and Regions-Convolutional Neural Network (R-CNN)”, Mendeley Data, v8 http://dx.doi.org/10.17632/kvpmv75hvg.8

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Categories

Agricultural Engineering, Machine Vision, Deep Learning

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Licence

CC BY NC 3.0 Learn more

The files associated with this dataset are licensed under a Attribution-NonCommercial 3.0 Unported licence.

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You are free to adapt, copy or redistribute the material, providing you attribute appropriately and do not use the material for commercial purposes.

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