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Related Experiment Video

Updated: Jun 21, 2025

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

Published on: September 22, 2023

A High-Precision Identification Method for Maize Leaf Diseases and Pests Based on LFMNet under Complex Backgrounds.

Jintao Liu¹, Chaoying He¹, Yichu Jiang²

¹College of Electronic Information & Physics, Central South University of Forestry and Technology, Changsha 410004, China.

Plants (Basel, Switzerland)

|July 13, 2024

Summary

A new maize leaf disease and pest identification model, LFMNet, effectively addresses complex backgrounds and subtle feature extraction challenges. This model achieves high accuracy, offering robust support for agricultural production.

Keywords:

FLB LFMNet LMSB MLFFA identification of maize leaf diseases and pests

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Last Updated: Jun 21, 2025

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

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Published on: September 22, 2023

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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

Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Maize is a vital global crop threatened by diseases and pests, necessitating accurate identification for agricultural security.
Current identification methods struggle with complex backgrounds and extracting subtle disease/pest features, impacting accuracy.

Purpose of the Study:

To develop an advanced maize leaf disease and pest identification model to overcome existing challenges.
To enhance the accuracy and reliability of identifying maize leaf diseases and pests.

Main Methods:

Proposed LFMNet model incorporating localized multi-scale inverted residual convolutional blocks (LMSB) for initial down-sampling and feature preservation.
Introduced feature localization bottleneck (FLB) to improve focus on disease/pest characteristics and reduce background interference.
Implemented multi-hop local-feature fusion architecture (MLFFA) for enhanced extraction and fusion of global and local features.

Main Results:

LFMNet achieved excellent performance on a dataset of 19,451 images.
Demonstrated high identification accuracy (95.68%), precision (95.91%), recall (95.78%), and F1 score (95.83%).
Outperformed existing models, showcasing significant advantages in maize disease and pest identification.

Conclusions:

LFMNet provides a robust solution for accurate maize leaf disease and pest identification.
The model's innovative architecture effectively handles complex backgrounds and subtle features.
Offers significant technical support for safeguarding agricultural production through precise pest and disease detection.