Deep　learning　is　a　field　of　Artificial　Intelligence　that　has　recently　drawn　a　lot　of　attention　with　the　desire　to　build　up　a　quick,　automatic　and　accurate　system　for　image　identification　and　classification.　Deep　learning　serves　as　a　fundamental　part　of　modern　computer　vision　solutions.　However,　as　the　architectures　become　deep　and　powerful　new　challenges　in　the　process　of　training　emerge.　This　includes　the　computational　cost　associated　with　training　deep　and　large　networks.　In　this　work,　the　focus　is　on　pruning　and　evaluation　of　state-of-the-art　deep　convolutional　neural　network　for　image-based　plant　disease　and　plants　species　classification.　Pruning　filters　allow　the　reduction　of　parameters　by　removing　unimportant　filters　and　its　feature　maps.　In　this　paper,　the　performance　of　pruned　networks　is　evaluated　across　three　datasets.　It　is　observed　that　pruned　DenseNet　with　Self-Normalization　Neural　Network　(SNN)　approach　learns　2x　faster　compared　to　the　initial　DenseNet　architecture.　Additionally,　pruning　filters　allow　the　reduction　of　the　number　of　parameters　and　FLOPs　by　approximately　14%　and　25%　respectively.　The　aim　is　to　create　a　fast　and　efficient　model　for　the　purpose　of　identification　of　plant　diseases.　Fast　methods　are　desired　for　early　identifications　of　diseases　before　damages　occur.　The　proposed　method　achieves　a　satisfactory　accuracy　performance　on　PlantVillage,　LeafSnap　and　Swedish-leaf　dataset　using　held-out　dataset.　Our　best　pruned　model　gives　an　accuracy　of　99.24%,　86.64%,　and　97.5%　on　PlantVillage,　LeafSnap,　and　Swedish-leaf　datasets　respectively.