Epigenetic　modifications　function　in　gene　transcription,　RNA　metabolism,　and　other　biological　processes.　However,　multiple　factors　currently　limit　the　scientific　utility　of　epigenomic　datasets　generated　for　plants.　Here,　using　deep-learning　approaches,　we　developed　a　Smart　Model　for　Epigenetics　in　Plants　(SMEP)　to　predict　six　types　of　epigenomic　modifications:　DNA　5-methylcytosine　(5mC)　and　N6-methyladenosine　(6mA)　methylation,　RNA　N6-methyladenosine　(m(6)A)　methylation,　and　three　types　of　histone　modification.　Using　the　datasets　from　the　japonica　rice　Nipponbare,　SMEP　achieved　95%　prediction　accuracy　for　6mA,　and　also　achieved　around　80%　for　5mC,　m(6)A,　and　the　three　types　of　histone　modification　based　on　the　10-fold　cross-validation.　Additionally,　＞　95%　of　the　6mA　peaks　detected　after　a　heat-shock　treatment　were　predicted.　We　also　successfully　applied　the　SMEP　for　examining　epigenomic　modifications　in　indica　rice　93-11　and　even　the　B73　maize　line.　Taken　together,　we　show　that　the　deep-learning-enabled　SMEP　can　reliably　mine　epigenomic　datasets　from　diverse　plants　to　yield　actionable　insights　about　epigenomic　sites.　Thus,　our　work　opens　new　avenues　for　the　application　of　predictive　tools　to　facilitate　functional　research,　and　will　almost　certainly　increase　the　efficiency　of　genome　engineering　efforts.