BackgroundLysine　crotonylation　(Kcr)　is　a　crucial　protein　post-translational　modification　found　in　histone　and　non-histone　proteins.　It　plays　a　pivotal　role　in　regulating　diverse　biological　processes　in　both　animals　and　plants,　including　gene　transcription　and　replication,　cell　metabolism　and　differentiation,　as　well　as　photosynthesis.　Despite　the　significance　of　Kcr,　detection　of　Kcr　sites　through　biological　experiments　is　often　time-consuming,　expensive,　and　only　a　fraction　of　crotonylated　peptides　can　be　identified.　This　reality　highlights　the　need　for　efficient　and　rapid　prediction　of　Kcr　sites　through　computational　methods.　Currently,　several　machine　learning　models　exist　for　predicting　Kcr　sites　in　humans,　yet　models　tailored　for　plants　are　rare.　Furthermore,　no　downloadable　Kcr　site　predictors　or　datasets　have　been　developed　specifically　for　plants.　To　address　this　gap,　it　is　imperative　to　integrate　existing　Kcr　sites　detected　in　plant　experiments　and　establish　a　dedicated　computational　model　for　plants.ResultsMost　plant　Kcr　sites　are　located　on　non-histones.　In　this　study,　we　collected　non-histone　Kcr　sites　from　five　plants,　including　wheat,　tabacum,　rice,　peanut,　and　papaya.　We　then　conducted　a　comprehensive　analysis　of　the　amino　acid　distribution　surrounding　these　sites.　To　develop　a　predictive　model　for　plant　non-histone　Kcr　sites,　we　combined　a　convolutional　neural　network　(CNN),　a　bidirectional　long　short-term　memory　network　(BiLSTM),　and　attention　mechanism　to　build　a　deep　learning　model　called　PlantNh-Kcr.　On　both　five-fold　cross-validation　and　independent　tests,　PlantNh-Kcr　outperformed　multiple　conventional　machine　learning　models　and　other　deep　learning　models.　Furthermore,　we　conducted　an　analysis　of　species-specific　effect　on　the　PlantNh-Kcr　model　and　found　that　a　general　model　trained　using　data　from　multiple　species　outperforms　species-specific　models.ConclusionPlantNh-Kcr　represents　a　valuable　tool　for　predicting　plant　non-histone　Kcr　sites.　We　expect　that　this　model　will　aid　in　addressing　key　challenges　and　tasks　in　the　study　of　plant　crotonylation　sites.