부산대학교 도서관

Healthy farm plant leaf classification and identification is a critical food security issue. In many places of the world, it remains tough as it needs appropriate infrastructure. Combining the rising worldwide prevalence of the smartphone with current progress in computer vision through deep learning, now it is possible to diagnose inconsistency of various farm plants. In this technology era, automation can help to replace manual prevention efforts in plants by employing image processing methods. This research deployed three pre-trained deep neural models: 3DCNN, ResNet50 and MobileNet, to classify the Matrib leaf into two categories: Good Matrib leaf and Bad Matrib leaf. We employed our own Matrib leaf customized dataset for this research. Experimental results demonstrate that MobileNet outperformed other models with an accuracy of 99.99% on test data, while ResNet50 and 3DCNN followed with an accuracy of 92.67% and 72.80%.