Implementation of CNN for Plant Leaf Classification
Many deep learning-based approaches for plant leaf stress identification have been proposed in the literature, but there are only a few partial efforts to summarize various contributions. This study aims to build a classification model to enable people or traditional medicine experts to detect medicinal plants by using a scanning camera. This Android-based application implements the Java programming language and labels using the Python programming language to build deep learning applications. The study aims to construct a deep learning model for image classification for plant leaves that can help people determine the types of medicinal plants based on android. This research can help the public recognize five types of medicinal plants, including spinach Duri, Javanese ginseng, Dadap Serep, and Moringa. In this study, the accuracy is 0.86, precision 0.22, f-1 score 0.23, while recall is 0.2375.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
IJICOM is an open-access journal. Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.