A Fast Fourier Convolutional Deep Neural Network For Accurate and
  Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From
  Sentinel-2 Time-Series Data
Yue Shi Liangxiu Han Pablo González-Moreno Darren Dancey Wenjiang Huang Zhiqiang Zhang Yuanyuan Liu Mengning Huan Hong Miao Min Dai
Accurate and timely detection of plant stress is essential for yield
protection, allowing better-targeted intervention strategies. Recent advances
in remote sensing and deep learning have shown great potential for rapid
non-invasive detection of plant stress in a fully automated and reproducible
manner. However, the existing models always face several challenges: 1)
computational inefficiency and the misclassifications between the different
stresses with similar symptoms; and 2) the poor interpretability of the
host-stress interaction. In this work, we propose a novel fast Fourier
Convolutional Neural Network (FFDNN) for accurate and explainable detection of
two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen
Deficiency). Specifically, unlike the existing CNN models, the main components
of the proposed model include: 1) a fast Fourier convolutional block, a newly
fast Fourier transformation kernel as the basic perception unit, to substitute
the traditional convolutional kernel to capture both local and global responses
to plant stress in various time-scale and improve computing efficiency with
reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to
encapsulate the extracted features into a series of vector features to
represent part-to-whole relationship with the hierarchical structure of the
host-stress interactions of the specific stress. In addition, in order to
alleviate over-fitting, a photochemical vegetation indices-based filter is
placed as pre-processing operator to remove the non-photochemical noises from
the input Sentinel-2 time series.