Plants　are　the　basis　of　all　living　things　on　earth,　supplying　us　with　oxygen,　food,　shelter,　medicine,　and　preserving　the　planet　from　dam-ages　that　could　face　climate　changes.　Concerning　their　medicinal　abilities,　limited　access　to　proper　medical　centers　in　many　rural　areas　and　developing　countries　made　traditional　medicine　preferable　by　the　community.　In　addition,　their　lower　side　effect　and　affordability　also　plays　a　big　role.　More　than　half　of　the　population　uses　medicinal　plants　directly　and　indirectly　for　animals　and　personal　use　in　Ethiopia.　However,　accurate　medicinal　plant　identification　has　always　been　a　challenge　for　manual　identification　and　automatic　recognition　systems　mainly　because　the　knowledge　transfer　between　the　knowledge　holders　(traditional　physicians,　elderly)　and　modern　science　have　a　huge　gap.　Several　studies　addressed　an　automatic　plant　recognition　system　using　different　feature　extraction　methods　and　classification　algorithms.　In　this　paper,　a　novel　dataset,　which　was　based　on　Ethiopian　medicinal　plants,　that　use　the　leaf　part　of　the　plant,　as　a　medicine　was　used　to　automatically　classify　the　plants　accordingly　using　their　leaf　image.　An　attempt　has　been　made　to　collect　leaf　images　of　medicinal　plants　in　Ethiopia,　to　train,　test　collected　dataset　images,　and　classify　those　images　using　convolutional　neural　network　models　like　GoogleNet　and　AlexNet.　The　proposed　convolutional　neural　networks　were　fine-tuned　with　the　adjustment　of　hyper-parameters　like　learning　rate,　the　number　of　epochs,　optimizers　to　the　models.　Image　augmentation　is　also　implemented　to　enlarge　the　dataset.　The　experimental　result　for　the　augmented　dataset　and　more　training　epoch　gave　better　performance　and　accuracy　in　the　classification　of　the　images.　From　the　two　selected　convolutional　neural　network　models,　the　best　model　is　then　determined　based　on　the　result　in　accuracy　and　loss;　from　an　experiment　conducted,　the　best　model,　which　is　GoogLeNet　with　an　accuracy　of　96.7　%　chosen　to　develop　a　web-based　automatic　medicinal　plant　classification　system.　©　2022,　Springer　Nature　Singapore　Pte　Ltd.