Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jul 2, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

A hybrid DenseNet121-random vector functional link (RVFL) approach for plant leaf classification.

Upendra Mishra^1,2, Himanshi Chaudhary³

¹Department of Computer Science and Engineering, National Institute of Technology Arunachal Pradesh, Jote, Papum Pare, Arunachal Pradesh, 791113, India. upendra.mishra13@gmail.com.

Scientific Reports

|July 1, 2026

Summary

Related Concept Videos

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

NAC062 regulates apical growth and cytokinin signaling in the shoot stem cell niche of Arabidopsis thaliana.

The Plant journal : for cell and molecular biology·2026

Same author

Common Variable Immunodeficiency Disorder: A Decade of Insights from a Cohort of 150 Patients in India and the Use of Machine Learning Algorithms to Predict Severity.

Journal of clinical immunology·2025

Same author

Enhanced medical image watermarking using hybrid DWT-HMD-SVD and Arnold scrambling.

Scientific reports·2025

Same author

Management of Steroid-Refractory Serositis and Macrophage Activation Syndrome in Systemic Juvenile Idiopathic Arthritis.

Indian journal of pediatrics·2024

Same author

The value of CT coronary angiography for a comprehensive assessment of left circumflex artery in Kawasaki disease: 9 years of experience from a tertiary center.

The Lancet regional health. Southeast Asia·2024

Same author

Novel IL2RB gene defect associated with severe eczema, recurrent infections, lymphoproliferation, and IgA nephropathy.

Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology·2024

This study introduces a hybrid DenseNet121-RVFL model for efficient leaf image classification. The combined approach achieves superior accuracy and performance compared to other methods, enhancing artificial intelligence applications.

Area of Science:

Artificial Intelligence
Machine Learning
Computer Vision

Background:

Classical neural networks suffer from slow convergence and overfitting.
DenseNet121 excels in object recognition but lacks interpretability in classification.
A need exists for improved, interpretable deep learning models in image analysis.

Purpose of the Study:

To develop a hybrid model integrating DenseNet121 and Random Vector Functional Link (RVFL) networks.
To enhance the classification accuracy and analytical flexibility of deep learning models for image recognition.
To address the interpretability limitations of DenseNet121 in classification tasks.

Main Methods:

Utilized a pre-trained DenseNet121 model to extract deep features from leaf images.

Keywords:

Binary classification DenseNet121 RVFL

Related Experiment Videos

Last Updated: Jul 2, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Applied Principal Component Analysis (PCA) to reduce feature dimensionality and remove redundancy.

Integrated the reduced feature set with an RVFL classifier for final classification.

Conducted comparative experiments against baseline classifiers like SVM, ELM, and KRR.

Main Results:

The hybrid DenseNet121-RVFL model achieved the highest accuracy (94.45%).
Achieved a high F1-Score of 0.955, Geometric Mean (G-Mean) of 0.898, and Area Under Curve (AUC) of 0.961.
Demonstrated superior performance over baseline classifiers in leaf image classification.

Conclusions:

The proposed DenseNet121-RVFL model offers an effective and efficient solution for image classification tasks.
Combining DenseNet121's feature extraction with RVFL's efficient classification improves performance and offers better analytical insights.
This hybrid approach shows significant potential for advancing artificial intelligence in object recognition and similar applications.