Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘MangoYOLO’
Author:
Funder
Horticulture Australia
Central Queensland University
Publisher
Springer Science and Business Media LLC
Subject
General Agricultural and Biological Sciences
Link
http://link.springer.com/article/10.1007/s11119-019-09642-0/fulltext.html
Reference39 articles.
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2. Bargoti S, Underwood J (2017a) Deep fruit detection in orchards. In: Proceedings—IEEE international conference on robotics and automation, pp 3626–3633. https://doi.org/10.1109/icra.2017.7989417
3. Bargoti, S., & Underwood, J. P. (2017b). Image segmentation for fruit detection and yield estimation in apple orchards. J Field Robot, 34, 1039–1060. https://doi.org/10.1002/rob.21699 .
4. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: Proceedings—IEE conference on computer vision and pattern recognition, pp 248–255. https://doi.org/10.1109/cvpr.2009.5206848
5. Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. Int J Comput Vis, 88, 303–338. https://doi.org/10.1007/s11263-009-0275-4 .
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