Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Light Acquisition02:16

Light Acquisition

8.6K
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.
8.6K
Methods of Classification and Identification01:28

Methods of Classification and Identification

206
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
206
Epiphytes, Parasites, and Carnivores02:40

Epiphytes, Parasites, and Carnivores

13.2K
Plants often form mutualistic relationships with soil-dwelling fungi or bacteria to enhance their roots’ nutrient uptake ability. Root-colonizing fungi (e.g., mycorrhizae) increase a plant’s root surface area, which promotes nutrient absorption. While root-colonizing, nitrogen-fixing bacteria (e.g., rhizobia) convert atmospheric nitrogen (N2) into ammonia (NH3), making nitrogen available to plants for various biological functions. For example, nitrogen is essential for the...
13.2K

You might also read

Related Articles

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

Sort by
Same author

Chiral-Ligand-Modulated Nickel-Catalyzed Stereoselective Radical Migratory C2-Arylation of Carbohydrates.

Journal of the American Chemical Society·2026
Same author

Enhanced puncture event detection for teleoperated needle insertion robotic system.

Medical & biological engineering & computing·2026
Same author

Rek-Surv: A lightweight deep survival model for plant infectious disease onset prediction.

Infectious Disease Modelling·2026
Same author

UAV-LiDAR high-throughput time-series phenotyping and genome-wide association analysis reveal the genetic basis of plant height in peanut (<i>Arachis hypogaea</i> L.).

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

An improved YOLOv8n model for in-field detection of pests and diseases in pakchoi.

Frontiers in plant science·2026
Same author

Traceability of black tea origin by synergistic application of electronic tongue and hyperspectral imaging combined with a Transformer-graph network.

Journal of the science of food and agriculture·2026

Related Experiment Video

Updated: Sep 16, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

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

Published on: February 9, 2024

1.6K

Rice Canopy Disease and Pest Identification Based on Improved YOLOv5 and UAV Images.

Gaoyuan Zhao1,2, Yubin Lan2, Yali Zhang2

  • 1College of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary

This study introduces an improved YOLOv5 deep learning model for identifying rice diseases and pests using drone imagery. The enhanced model achieves 95.6% average precision, offering a significant advancement for agricultural monitoring.

Keywords:
UAV imagesYOLOv5canopy diseases and pestsrice

More Related Videos

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.4K
Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field
10:49

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Published on: March 16, 2019

8.7K

Related Experiment Videos

Last Updated: Sep 16, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

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

Published on: February 9, 2024

1.6K
Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.4K
Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field
10:49

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Published on: March 16, 2019

8.7K

Area of Science:

  • Agricultural Science
  • Computer Science
  • Remote Sensing

Background:

  • Traditional manual surveys for rice diseases and pests are inefficient and subjective.
  • There is a need for large-scale, rapid, and accurate monitoring methods in agriculture.

Purpose of the Study:

  • To develop an improved deep learning model for accurate identification of rice canopy diseases and pests.
  • To enhance the capabilities of the YOLOv5 model for agricultural applications.

Main Methods:

  • Utilized unmanned aerial vehicles (UAVs) for high-resolution image capture of rice canopies.
  • Developed an improved YOLOv5 model (YOLOv5_DWMix) incorporating deep separable convolutions, MixConv, attention mechanisms, and optimized loss functions.
  • Employed image augmentation to train the model for recognizing four common rice diseases and pests, addressing challenges of complex environments and small datasets.

Main Results:

  • The improved YOLOv5_DWMix model achieved an average precision of 95.6% in detecting rice diseases and pests.
  • Demonstrated a 4.8% improvement in average precision compared to the original YOLOv5 model.
  • The model showed enhanced speed, feature extraction, and robustness.

Conclusions:

  • The YOLOv5_DWMix model is an effective and advanced tool for identifying rice diseases and pests.
  • This method provides a strong foundation for large-scale, regional agricultural monitoring using drone technology and deep learning.