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Related Concept Videos

Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...
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Related Experiment Videos

Paddy leaf disease detection and classification using improved Gorilla Troops optimized YOLO-V8 network.

T V Chithra1, A Ahilan2, P Deepa3

  • 1Department of Computer Science and Engineering, Arunachala College of Engineering for Women, Manavilai, Nagercoil, 629203, Tamil Nadu, India. chithratvakul@gmail.com.

Scientific Reports
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

A new YOLO-LEAFNET method improves paddy leaf disease detection using deep learning and an Improved Gorilla Troops (IGT) optimization. This approach achieves 99.07% accuracy, enabling efficient early diagnosis and preventing crop yield loss.

Keywords:
B-CLAHEDeep learningImproved Gorilla Troops optimizationPaddy leaf diseaseYolov8

Related Experiment Videos

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Paddy leaf disease (PLD) detection is challenging due to complex image conditions.
  • Existing models lack accuracy and generalization for early PLD diagnosis.
  • Significant crop yield losses occur without timely disease identification.

Purpose of the Study:

  • To develop a novel deep learning method for accurate PLD detection.
  • To enhance early diagnosis capabilities for paddy crops.
  • To improve upon existing disease detection models' performance.

Main Methods:

  • Utilized Bilateral Contrast Limited Adaptive Histogram Equalization (B-CLAHE) for image pre-processing.
  • Integrated YOLOv8 with Improved Gorilla Troops (IGT) optimization for YOLO-LEAFNET.
  • Employed hyperparameter tuning via IGT to optimize YOLOv8 performance.

Main Results:

  • Achieved a high accuracy of 99.07% in PLD detection.
  • Demonstrated improved contrast and accuracy with B-CLAHE pre-processing.
  • YOLO-LEAFNET outperformed CNN, DeepRice, and Faster R-CNN by 3.21%, 5.25%, and 1.98%, respectively.

Conclusions:

  • The YOLO-LEAFNET method offers a scalable and efficient solution for early PLD diagnosis.
  • B-CLAHE and IGT-YOLO integration significantly enhance detection accuracy.
  • The proposed model addresses limitations of existing methods in challenging conditions.