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

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

Evolution of pre- and post-operative balance characteristics in patients undergoing anterior cruciate ligament reconstruction: implications for rehabilitation.

Frontiers in bioengineering and biotechnology·2026

Same author

A proposed method for locating the tibial attachment for medial patellotibial ligament reconstruction: A cadaveric anatomical and imaging study.

Journal of experimental orthopaedics·2026

Same author

Immune-activated & pathology-characterized: the distinct clear cell renal cell carcinoma subtypes for 3PM strategies derived from artificial intelligence in multi-omics.

The EPMA journal·2026

Same author

Study on the dynamic failure performance of bamboo fiber/high-density polyethylene composites with different fiber content using digital image correlation.

Scientific reports·2026

Same author

Estimation of safe zones for suture anchor placement in arthroscopic hip labral repair.

Frontiers in surgery·2026

Same author

Resonant Raman spectroscopy elucidates pH-reversible chelation of blueberry anthocyanins with metal ions and carrageenan-enhanced photothermal stability.

Food chemistry: X·2026

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

DSC-DeepLabv3+: a lightweight semantic segmentation model for weed identification in maize fields.

Haitao Fu¹, Xiaoyao Li¹, Li Zhu¹

¹College of Information Technology, Jilin Agricultural University, Changchun, China.

Frontiers in Plant Science

|September 19, 2025

Summary

A new lightweight semantic segmentation model, DSC-DeepLabv3+, significantly improves weed identification in maize crops. This model enhances precision agriculture by offering high accuracy and efficiency with reduced computational costs.

Keywords:

DeepLabV3+attention mechanisms feature fusion lightweight semantic segmentation weed recognition

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Published on: October 9, 2018

Related Experiment Videos

Last Updated: Jan 17, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Published on: October 9, 2018

Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Weeds reduce crop yield and quality by competing for essential resources.
Accurate weed identification is crucial for precision agriculture.
Existing models often lack the efficiency required for real-time agricultural applications.

Purpose of the Study:

To develop a lightweight semantic segmentation model for accurate and efficient weed identification in maize.
To reduce model complexity and computational cost while maintaining high segmentation performance.

Main Methods:

Proposed DSC-DeepLabv3+ model using MobileNetV2 backbone.
Replaced standard convolutions with depthwise separable dilated convolutions (DSDConv).
Incorporated strip pooling-atrous spatial pyramid pooling (S-ASPP) and convolutional block attention module (CBAM) for enhanced feature representation and fusion (CBAM-Cascade Feature Fusion - C-CFF).

Main Results:

Reduced model parameters from 54.714M to 2.89M.
Decreased computational cost from 167.139 GFLOPs to 15.326 GFLOPs.
Achieved an inference speed of 42.89 FPS and a mean Intersection over Union (mIoU) of 85.57%.

Conclusions:

DSC-DeepLabv3+ offers an effective balance between accuracy and efficiency for weed segmentation.
The model outperforms classical lightweight models in agricultural applications.
Presents a promising solution for precise and efficient weed management in agriculture.