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.

翻译：准确及时地检测植物胁迫对于作物保护至关重要，有助于制定更具针对性的干预策略。遥感与深度学习的最新进展在完全自动化、可重复的植物胁迫快速无创检测方面展现出巨大潜力。然而，现有模型始终面临若干挑战：1）计算效率低下以及具有相似症状的不同胁迫之间的误分类；2）宿主-胁迫相互作用的可解释性较差。本研究提出一种新型快速傅里叶卷积神经网络，用于准确且可解释地检测两种具有相似症状的植物胁迫（即小麦黄锈病与缺氮症）。具体而言，与现有CNN模型不同，所提模型主要组成部分包括：1）快速傅里叶卷积模块，以一种新型快速傅里叶变换核作为基本感知单元，替代传统卷积核，以捕捉不同时间尺度下植物胁迫的局部与全局响应，并通过减少傅里叶域中的学习参数提升计算效率；2）胶囊特征编码器，将提取的特征封装为一系列向量特征，以表征特定胁迫下宿主-胁迫相互作用层级结构中的整体-部分关系。此外，为缓解过拟合，引入基于光化学植被指数的滤波器作为预处理算子，以去除输入哨兵2号时间序列中的非光化学噪声。