IntroductionAccurate　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.MethodsIn　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.Results　and　discussionThe　proposed　model　has　been　evaluated　with　ground　truth　data　under　both　controlled　and　natural　conditions.　The　results　demonstrate　that　the　high-level　vector　features　interpret　the　influence　of　the　host-stress　interaction/response　and　the　proposed　model　achieves　competitive　advantages　in　the　detection　and　discrimination　of　yellow　rust　and　nitrogen　deficiency　on　Sentinel-2　time　series　in　terms　of　classification　accuracy,　robustness,　and　generalization.