Comparison of Deep Learning Architectures in Identifying Types of Medicinal Plant Leaf Images
Abstract
This study focuses on the identification of 3500 images of medicinal plant leaves using Deep Learning CNN Transfer Learning models such as MobileNet, VGG16, DenseNet121, ResNet50V2, and NASNetMobile. The dataset used is the "Indonesian Herb Leaf Dataset 3500," consisting of 10 classes of medicinal plants. This research has the potential to efficiently and accurately recognize medicinal plants using machine learning workflow methods. The objective of this study is to compare the performance of these five methods in conducting plant identification. The testing phase involves various data handling schemes, dividing the data into two scenarios: 80:10:10 and 70:20:10. Performance comparison is also done between augmented and non-augmented data. The research findings indicate that MobileNet exhibits the best performance with an accuracy, precision, recall, and f1-Score of 98.86%. Accurate leaf identification supports further research on the properties and benefits of medicinal plants and can be applied in the development of decision support systems for plant recognition.
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