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

Preoperative evaluation of post-hepatectomy liver failure in hepatocellular carcinoma based on gadoxetic acid-enhanced MRI.

Frontiers in oncology·2026

Same author

Association between intraoperative hypothermia and total hospitalization cost after liver transplantation: a retrospective cohort study.

Perioperative medicine (London, England)·2026

Same author

Physiology-informed LSTM framework integrating crop model and Sentinel-2 time series for rice nitrogen status estimation.

Plant phenomics (Washington, D.C.)·2026

Same author

Ciprofol versus Propofol for General Anesthesia Induction in Major Orthopedic Surgery: Efficacy and Safety Analyses Pertaining to Anesthetic Parameters.

Pharmacology·2026

Same author

Analysis of Genetic Characteristics, Biofilm Formation Ability, and Mortality Risk Factors in Patients Infected with Carbapenem-Resistant <i>Acinetobacter baumannii</i>.

Infection and drug resistance·2026

Same author

In-season estimation of aboveground biomass and yield in winter wheat with a UAV-based LUE model and machine learning.

Plant phenomics (Washington, D.C.)·2026

Search research articles

Related Experiment Video

Updated: Jul 4, 2025

Development of Targeting Induced Local Lesions IN Genomes TILLING Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis

Development of Targeting Induced Local Lesions IN Genomes TILLING Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis

Published on: July 16, 2019

A method for small-sized wheat seedlings detection: from annotation mode to model construction.

Suwan Wang¹, Jianqing Zhao^2,3, Yucheng Cai^1,3

¹National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.

|January 30, 2024

Summary

This study introduces a new method for accurately counting wheat seedlings using local annotations and improved deep learning models. The approach enhances detection accuracy, crucial for wheat yield prediction.

Keywords:

Local annotation Unmanned aerial vehicle (UAV) images Wheat seedlings detection YOLO

More Related Videos

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

Published on: May 21, 2020

Related Experiment Videos

Last Updated: Jul 4, 2025

Development of Targeting Induced Local Lesions IN Genomes TILLING Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis

Development of Targeting Induced Local Lesions IN Genomes TILLING Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis

Published on: July 16, 2019

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

Published on: May 21, 2020

Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Accurate wheat seedling counts are vital for population size assessment and yield prediction.
Deep learning methods using unmanned aerial vehicle (UAV) images are increasingly used for wheat seedling detection.
Challenges in current methods include small seedling size, diverse postures, and background soil interference due to global annotations.

Purpose of the Study:

To develop an improved wheat seedling detection method addressing limitations of global annotations.
To enhance the accuracy of wheat seedling counting from UAV imagery.

Main Methods:

Proposed a wheat seedling detection method utilizing local annotations instead of global ones.
Improved the detection model by incorporating a Space-to-depth Conv module and a micro-scale detection layer in the YOLOv5 head.
Optimized the model to better extract small-scale features and mitigate issues like leaf occlusion.

Main Results:

The proposed method achieved a detection accuracy of 90.1%.
Outperformed existing state-of-the-art wheat seedling detection methods.
Demonstrated effectiveness in reducing errors caused by seedling size and occlusion.

Conclusions:

Local annotation and model optimization significantly improve wheat seedling detection accuracy.
The developed method offers a valuable reference for future wheat seedling detection and yield prediction research.
This approach addresses key challenges in automated agricultural monitoring.