Deep learning combined with Balance Mixup for the detection of pine wilt disease using multispectral imagery

Author:

Rao Deshen,Zhang Derong,Lu Huanda,Yang Yong,Qiu Yi,Ding Menghan,Yu XinjieORCID

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference46 articles.

1. Akbulut, S., Yuksel, B., Baysal, İ., Vieira, P., & Mota, M. (2008). Pine Wilt Disease: A Threat to Pine Forests in Turkey? , 59-67. https://doi.org/10.1007/978-1-4020-8455-3_4.

2. Al-Amri, S. S., & Kalyankar, N. V. (2010). Image segmentation by using threshold techniques. arXiv preprint arXiv:1005.4020.

3. Baldi, P., & Sadowski, P. J. (2013). Understanding dropout. Advances in neural information processing systems, 26.

4. Beck, P. S., Zarco-Tejada, P., Strobl, P., & San Miguel, J. (2015). The feasibility of detecting trees affected by the pine wood nematode using remote sensing. EUR—Scientific and Technical Research Reports, 1831-9424.

5. 1D convolutional neural network for the discrimination of aristolochic acids and their analogues based on near-infrared spectroscopy;Chen;Anal. Methods,2019

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