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

Enhanced puncture event detection for teleoperated needle insertion robotic system.

Medical & biological engineering & computing·2026

Same author

Effects of ciprofol versus propofol sedation on hypoxaemia and hypotension in elderly patients undergoing bidirectional endoscopy: protocol for a randomized controlled trial.

Frontiers in medicine·2026

Same author

Research Trends and Emerging Frontiers in Racehorse Genetics: A Bibliometric Analysis.

Animals : an open access journal from MDPI·2026

Same author

Prior-Knowledge-Guided Graph Attention Network for Fault Diagnosis of Engine Valve Clearance.

Sensors (Basel, Switzerland)·2026

Same author

Achieving High-Density and Stress-Resilient Maize Breeding Via Germplasm Innovation.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Radiation-activated astrocytes promote tumor progression through CXCL12 secretion and induce resistance to anti-PD-1 immunotherapy.

Translational cancer research·2026

Search research articles

Related Experiment Video

Updated: Jan 6, 2026

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

YOLO-APLD: A Lightweight Apple Leaf Disease Detection Model Based on Multiscale Feature Fusion.

Xinlong Li¹, Haiteng Liu¹, Lening Jiao¹

¹College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.

|November 23, 2025

Summary

A new lightweight algorithm, YOLO-APLD, improves apple leaf disease detection accuracy and speed. This method enhances identification of various diseases, aiding precision pesticide application in orchards.

Keywords:

apple deep learning disease detection precision agriculture

More Related Videos

Specific and Accurate Detection of the Citrus Greening Pathogen Candidatus liberibacter spp. Using Conventional PCR on Citrus Leaf Tissue Samples

Specific and Accurate Detection of the Citrus Greening Pathogen Candidatus liberibacter spp. Using Conventional PCR on Citrus Leaf Tissue Samples

Published on: June 29, 2018

Author Spotlight: High-Throughput In Vivo Leaf Inoculation for Accelerating Disease Resistance Screening in Poplar Hybrid Breeding

Author Spotlight: High-Throughput In Vivo Leaf Inoculation for Accelerating Disease Resistance Screening in Poplar Hybrid Breeding

Published on: September 20, 2024

Related Experiment Videos

Last Updated: Jan 6, 2026

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

Specific and Accurate Detection of the Citrus Greening Pathogen Candidatus liberibacter spp. Using Conventional PCR on Citrus Leaf Tissue Samples

Specific and Accurate Detection of the Citrus Greening Pathogen Candidatus liberibacter spp. Using Conventional PCR on Citrus Leaf Tissue Samples

Published on: June 29, 2018

Author Spotlight: High-Throughput In Vivo Leaf Inoculation for Accelerating Disease Resistance Screening in Poplar Hybrid Breeding

Author Spotlight: High-Throughput In Vivo Leaf Inoculation for Accelerating Disease Resistance Screening in Poplar Hybrid Breeding

Published on: September 20, 2024

Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Accurate apple leaf disease identification is crucial for effective pesticide application.
Conventional deep learning models face challenges with large model sizes and varying disease scales.

Purpose of the Study:

To develop a lightweight and accurate algorithm for detecting apple leaf diseases.
To improve upon existing deep learning models for real-time disease monitoring in orchards.

Main Methods:

Introduced YOLO-APLD, an enhanced YOLOv8n model incorporating an EP-C2f module for feature representation.
Utilized Focal-SIoU loss for improved bounding box regression and classification, especially for challenging samples.
Implemented a bidirectional feature pyramid network (BiFPN) and Slim-neck structure for efficient multi-scale feature fusion and reduced model complexity.

Main Results:

YOLO-APLD achieved precision of 88.5%, recall of 84.3%, mAP of 88.5%, and F1-score of 86.4%.
Demonstrated significant reductions in FLOPs (22.2%), parameters (23.3%), and model size (17.5%) compared to YOLOv8n.
Achieved a high detection frame rate of 90.3 f/s on edge devices, confirming real-time capability.
Showcased generalizability on grape and tomato datasets.

Conclusions:

YOLO-APLD offers a practical and efficient solution for apple leaf disease detection.
The model provides valuable technical support for precision agriculture and on-site disease monitoring.
The enhanced detection performance and reduced computational cost make it suitable for real-world orchard applications.