This　editorial　introduces　the　Special　Issue,　entitled　＂Deep　Learning　(DL)　Techniques　for　Agronomy　Applications＂,　of　Agronomy.　Topics　covered　in　this　issue　include　three　main　parts:　(I)　DL-based　image　recognition　techniques　for　agronomy　applications,　(II)　DL-based　time　series　data　analysis　techniques　for　agronomy　applications,　and　(III)　behavior　and　strategy　analysis　for　agronomy　applications.　Three　papers　on　DL-based　image　recognition　techniques　for　agronomy　applications　are　as　follows:　(1)　＂Automatic　segmentation　and　counting　of　aphid　nymphs　on　leaves　using　convolutional　neural　networks,＂　by　Chen　et　al.;　(2)　＂Estimating　body　condition　score　in　dairy　cows　from　depth　images　using　convolutional　neural　networks,　transfer　learning,　and　model　ensembling　techniques,＂　by　Alvarez　et　al.;　and　(3)　＂Development　of　a　mushroom　growth　measurement　system　applying　deep　learning　for　image　recognition,＂　by　Lu　et　al.　One　paper　on　DL-based　time　series　data　analysis　techniques　for　agronomy　applications　is　as　follows:　＂LSTM　neural　network　based　forecasting　model　for　wheat　production　in　Pakistan,＂　by　Haider　et　al.　One　paper　on　behavior　and　strategy　analysis　for　agronomy　applications　is　as　follows:　＂Research　into　the　E-learning　model　of　agriculture　technology　companies:　analysis　by　deep　learning,＂　by　Lin　et　al.