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.

Plant Breeding and Biotechnology

Plant Breeding and Biotechnology

Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.

You might also read

Related Articles

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

Sort by

Same author

High-Performance Weavable Piezoelectret Fibers via Scalable In Situ Poling Melt-Spinning for Real-Time Knee Joint Rehabilitation Monitoring.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

A CD36-targeted aptamer-4-butyl-polyhydroxybenzophenone conjugate with pH-responsive release for liver delivery in MASLD.

Regenerative biomaterials·2026

Same author

Ultrasensitive Hydrogen Sensor Based on a Non-Stoichiometric Tin Oxide Heterojunction for LIB Thermal Runaway Early Monitoring.

ACS sensors·2026

Same author

Scion varieties and nitrogen levels affect carbon and nitrogen assimilation in apple via modulating rhizosphere microbial structure and function.

Horticulture research·2026

Same author

Accelerating Water Dissociation by Interfacial Conductance Clamp.

ChemSusChem·2026

Same author

Nepetin suppresses HBV-associated hepatocellular carcinoma metastasis by regulating the HBx/HIF-1α-mediated autophagy axis.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026

Search research articles

Related Experiment Video

Updated: Jul 22, 2025

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

A new model based on improved VGG16 for corn weed identification.

Le Yang¹, Shuang Xu², XiaoYun Yu¹

¹School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China.

Frontiers in Plant Science

|July 24, 2023

Summary

A new SE-VGG16 model accurately identifies corn weeds using deep convolutional neural networks. This advanced model significantly improves upon existing methods for effective weed control in agriculture.

Keywords:

Leaky ReLU attention mechanism corn weed deep convolutional neural network global average pooling

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

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Related Experiment Videos

Last Updated: Jul 22, 2025

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

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Weeds significantly impact corn yield and quality in agriculture.
Accurate and efficient weed identification is crucial for effective crop management.

Purpose of the Study:

To develop a novel deep convolutional neural network model for accurate and efficient identification of weeds in corn fields.
To enhance weed detection capabilities using attention mechanisms and optimized network architecture.

Main Methods:

Proposed the SE-VGG16 model, an adaptation of VGG16 incorporating an SE attention mechanism.
Modified convolutional kernels and activation functions (ReLU to Leaky ReLU) for improved feature extraction.
Replaced the fully connected layer with a global average pooling layer and utilized softmax for output.

Main Results:

The SE-VGG16 model achieved an average accuracy of 99.67% in corn weed classification.
Demonstrated superior performance compared to classical and advanced multiscale models, outperforming the original VGG16 (97.75%).
Evaluated using precision rate, recall rate, and F1 score, showing high robustness and stability.

Conclusions:

The SE-VGG16 model offers a robust and accurate solution for identifying weeds in corn fields.
This model provides practical applications for effective weed control strategies in agriculture.
The developed model showcases the potential of attention mechanisms in deep learning for agricultural challenges.