Plant Leaf Classification through Deep Feature Fusion with Bidirectional Long Short-Term Memory

Dudi, Bhanuprakash; Rajesh, V.; Kumar, G Prasanna

doi:10.1109/icistsd55159.2022.10010620

2022 International Conference on Innovations in Science and Technology for Sustainable Development (ICISTSD) 2022

DOI: 10.1109/icistsd55159.2022.10010620

|View full text |Cite

Plant Leaf Classification through Deep Feature Fusion with Bidirectional Long Short-Term Memory

Bhanuprakash Dudi¹,

V. Rajesh

G Prasanna Kumar³

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning

Elbasi,

Topcu,

Cina

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In botany and agriculture, classifying leaves is a crucial process that yields vital information for studies on biodiversity, ecological studies, and the identification of plant species. The Cope Leaf Dataset offers a comprehensive collection of leaf images from various plant species, enabling the development and evaluation of advanced classification algorithms. This study presents a robust methodology for classifying leaf images within the Cope Leaf Dataset by enhancing the feature extraction and selection process. Cope Leaf Dataset has 99 classes and 64 features with 1584 records. Features are extracted based on the margin, texture, and shape of the leaves. It is challenging to classify a large number of labels because of class imbalance, feature complexity, overfitting, and label noise. Our approach combines advanced feature selection techniques with robust preprocessing methods, including normalization, imputation, and noise reduction. By systematically integrating these techniques, we aim to reduce dimensionality, eliminate irrelevant or redundant features, and improve data quality. Increasing accuracy in classification, especially when dealing with large datasets and many classes, involves a combination of data preprocessing, model selection, regularization techniques, and fine-tuning. The results indicate that the Multilayer Perception algorithm gives 89.48%, the Naïve Bayes Classifier gives 89.63%, Convolutional Neural Networks has 88.72%, and the Hoeffding Tree algorithm gives 89.92% accuracy for the classification of 99 label plant leaf classification problems.

show abstract

Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning

Elbasi,

Topcu,

Cina

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Plant Leaf Classification through Deep Feature Fusion with Bidirectional Long Short-Term Memory

Cited by 1 publication

References 21 publications

Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning

Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning

Contact Info

Product

Resources

About