Deep　learning　has　recently　attracted　a　lot　of　attention　with　the　aim　to　develop　a　quick,　automatic　and　accurate　system　for　image　identification　and　classification.　In　this　work,　the　focus　was　on　fine-tuning　and　evaluation　of　state-of-the-art　deep　convolutional　neural　network　for　image-based　plant　disease　classification.　An　empirical　comparison　of　the　deep　learning　architecture　is　done.　The　architectures　evaluated　include　VGG　16,　Inception　V4,　ResNet　with　50,　101　and　152　layers　and　DenseNets　with　121　layers.　The　data　used　for　the　experiment　is　38　different　classes　including　diseased　and　healthy　images　of　leafs　of　14　plants　from　plantVillage.　Fast　and　accurate　models　for　plant　disease　identification　are　desired　so　that　accurate　measures　can　be　applied　early.　Thus,　alleviating　the　problem　of　food　security.　In　our　experiment,　DenseNets　has　tendency＇s　to　consistently　improve　in　accuracy　with　growing　number　of　epochs,　with　no　signs　of　overfitting　and　performance　deterioration.　Moreover,　DenseNets　requires　a　considerably　less　number　of　parameters　and　reasonable　computing　time　to　achieve　state-of-the-art　performances.　It　achieves　a　testing　accuracy　score　of　99.75%　to　beat　the　rest　of　the　architectures.　Keras　with　Theano　backend　was　used　to　perform　the　training　of　the　architectures.