Abstract
Grapevine leaves are utilized worldwide in a vast range of traditional cuisines. As their price and flavor differ from kind to kind, recognizing various species of grapevine leaves is becoming an essential task. In addition, the differentiation between grapevine leaf types by human sense is difficult and time-consuming. Thus, building a machine learning model to automate the grapevine leaf classification is highly beneficial. Therefore, this is the primary focus of this work. This paper uses a CNN-based model to classify grape leaves by adapting DenseNet201. This study investigates the impact of layer freezing on the performance of DenseNet201 throughout the fine-tuning process. This work used a public dataset consist of 500 images with 5 different classes (100 images per class). Several data augmentation methods used to expand the training set. The proposed CNN model, named DenseNet-30, outperformed the existing grape leaf classification work that the dataset borrowed from by achieving 98% overall accuracy.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.