The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Apple flower detection using deep convolutional networks
Open accessAbstract
Abstract To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate bloom intensity, but their overall performance is still far from satisfactory even in relatively controlled environments. With the goal of devising a technique for flower identification which is robust to clutter and to changes in illumination, this paper presents a method in which a pre-trained convolutional neural network is fine-tuned to become specially sensitive to flowers. Experimental results on a challenging dataset demonstrate that our method significantly outperforms three approaches that represent the state of the art in flower detection, with recall and precision rates higher than 90%. Moreover, a performance assessment on three additional datasets previously unseen by the network, which consist of different flower species and were acquired under different conditions, reveals that the proposed method highly surpasses baseline approaches in terms of generalization capability.
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Apple detection during different growth stages in orchards using the improved YOLO-V3 model
Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., & Liang, Z. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.01.012 · Cited by 1,043
Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN
Yu, Y., Zhang, K., Yang, L., & Zhang, D. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.06.001 · Cited by 630
Wu, D., Lv, S., Jiang, M., & Song, H. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105742 · Cited by 496
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review
Jiang, Y., & Li, C. (2020) · Plant Phenomics · doi:10.34133/2020/4152816 · Cited by 380
Structural crack detection using deep convolutional neural networks
Ali, R., Chuah, J. H., Talip, M. S. A., Mokhtar, N., & Shoaib, M. A. (2022) · Automation in Construction · doi:10.1016/j.autcon.2021.103989 · Cited by 369
Metadata
- Authors
- Dias, P. A., Tabb, A., & Medeiros, H.
- Venue
- Computers in Industry
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 99
- Pages
- 17-28
- ISSN
- 0166-3615 (ISSN-L)
- OpenAlex
- W2794915299
- Cited by
- 265
- References
- 53
- FWCI
- 19.42
- Type
- journal article
- Language
- en
- Open access
- green
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