Counting spikelets from infield wheat crop images using fully convolutional networks
Author:
Funder
University of Tabuk
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07392-1.pdf
Reference52 articles.
1. Alkhudaydi T, Reynolds D, Griffiths S, Zhou J, De La Iglesia B (2019) An exploration of deep-learning based phenotypic analysis to detect spike regions in field conditions for UK bread wheat. Plant Phenomics 2019:7368761
2. Alkhudaydi T, Zhou J, de la Iglesia B (2019) SpikeletFCN: counting spikelets from infield wheat crop images using fully convolutional networks. In: international conference on artificial intelligence and soft computing, pp 3–13. Springer
3. Arteta C, Lempitsky V, Noble JA, Zisserman A (2014) Interactive object counting. In: European conference on computer vision, pp 504–518. Springer
4. Arteta C, Lempitsky V, Zisserman A (2016) Counting in the wild. In: European conference on computer vision, pp 483–498. Springer
5. Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures. Neural networks: tricks of the trade. Springer, Berlin, pp 437–478
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