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Related Concept Videos

Information Processing Approach01:30

Information Processing Approach

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The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
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Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...
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Functional groups are groups of atoms with specific chemical properties that occur within organic molecules and are sometimes denoted as “R”. Functional groups can “functionalize” a compound by enabling it to adopt different physical and chemical properties.
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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All radioactive nuclides emit high-energy particles or electromagnetic waves. When this radiation encounters living cells, it can cause heating, break chemical bonds, or ionize molecules. The most serious biological damage results when these radioactive emissions fragment or ionize molecules. For example, α and β particles emitted from nuclear decay reactions possess much higher energies than ordinary chemical bond energies. When these particles strike and penetrate matter, they...
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Related Experiment Video

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An Effective Approach for Recognition of Crop Diseases Using Advanced Image Processing and YOLOv8.

Muhammad Nouman Noor1, Muhammad Masab2, Farah Haneef3

  • 1Department of AI and Data Science National University of Computer and Emerging Sciences (FAST-NUCES) Islamabad Pakistan.

Food Science & Nutrition
|February 12, 2026
PubMed
Summary

This study introduces a computer-aided approach using deep learning (YOLOv8) for early plant disease detection in crops. The AI model accurately identifies 32 diseases, improving agricultural monitoring and reducing crop loss.

Keywords:
YOLOv8artificial intelligencecrop diseasesimage processing

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Plant diseases threaten economic crops (tomatoes, coffee, wheat) and global food security, especially in Asia.
  • Traditional disease detection is slow, labor-intensive, and lacks accessible data, hindering practical application.
  • Computer-aided detection is crucial for efficient and scalable crop disease management.

Purpose of the Study:

  • To develop and evaluate a computer-aided system for detecting and classifying crop diseases using image analysis.
  • To leverage deep learning, specifically YOLOv8, for accurate segmentation and classification of plant diseases.
  • To improve early disease diagnosis and minimize crop losses through automated identification.

Main Methods:

  • Image processing techniques (local contrast enhancement, wavelet transform, median filtering) were applied.
  • The YOLOv8 deep learning model was trained using Transfer Learning on a hybrid dataset of 32 crop diseases.
  • Performance was evaluated using metrics like recall and overall accuracy.

Main Results:

  • The YOLOv8 model achieved high performance in segmenting and classifying crop diseases.
  • Achieved a recall of 0.94 and an overall accuracy of 92.567%.
  • Demonstrated dependable performance across various disease identification scenarios.

Conclusions:

  • The developed computer-aided system enhances early disease detection in key crops.
  • The AI approach reduces reliance on expert intervention for disease diagnosis.
  • This technology contributes to preventing significant crop losses and bolstering food security.