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Related Concept Videos

Light Acquisition02:16

Light Acquisition

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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.
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Related Experiment Video

Updated: Mar 15, 2026

Visualizing Cellular Gibberellin Levels Using the nlsGPS1 Förster Resonance Energy Transfer (FRET) Biosensor
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Integrating EfficientNetV2 with guided filopic diffusion for enhanced rice leaf disease recognition.

V Vinoth Kumar1, P Rajesh2, N Krishnamoorthy2

  • 1School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore, 632014, India. vinothkumar.v@vit.ac.in.

Scientific Reports
|March 14, 2026
PubMed
Summary

This study introduces an advanced AI method for detecting rice leaf diseases, achieving 98.92% accuracy. The novel approach enhances image quality and disease identification for sustainable rice production.

Keywords:
Bacterial leaf blightBrown spotEfficientNetV2Guided filopic diffusionLeaf smutRice leaf disease

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Rice is a staple for over 65% of India's population, but diseases like leaf smut, brown spot, and bacterial leaf blight threaten yield and quality.
  • Accurate and timely disease identification is crucial for sustainable rice cultivation and food security.
  • Emerging technologies like Deep Learning (DL) offer potential solutions for agricultural disease challenges.

Purpose of the Study:

  • To propose and evaluate a novel approach for identifying Rice Leaf Disease using EfficientNetV2 and a Diffusion Bounded Attention method.
  • To improve the quality of input imagery for better disease classification through Preceding Noise Reduction (PNR) using Guided Filopic Diffusion (GFD).
  • To accurately segment and classify rice leaf diseases, enhancing sustainable agricultural practices.

Main Methods:

  • Utilized EfficientNetV2 with a Diffusion Bounded Attention method for disease detection.
  • Implemented Preceding Noise Reduction (PNR) via Guided Filopic Diffusion (GFD) to enhance image quality and preserve leaf texture.
  • Employed the Dice Similarity Coefficient (DSC) to evaluate the model's segmentation accuracy on a comprehensive Rice Leaf Diseases Dataset.

Main Results:

  • Achieved a high accuracy rate of 98.92% in identifying and classifying rice leaf diseases.
  • Demonstrated superior performance in recall, precision, and F1 score compared to existing methods.
  • The PNR-GFD technique effectively improved image quality, retaining critical leaf texture for classification.

Conclusions:

  • The proposed EfficientNetV2 model with Diffusion Bounded Attention and PNR-GFD is highly effective for accurate rice leaf disease identification.
  • This AI-driven approach can significantly aid in sustainable rice production by enabling early and precise disease detection.
  • The methodology provides a robust framework for agricultural disease management, improving crop yield and quality.