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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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LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
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IDDMSLD: An image dataset for detecting Malabar spinach leaf diseases.

Adnan Rahman Sayeem1, Jannatul Ferdous Omi1, Mehedi Hasan1

  • 1Multidisciplinary Action Research Laboratory, Department of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, Bangladesh.

Data in Brief
|February 3, 2025
PubMed
Summary

A new dataset of Malabar Spinach leaf images from Bangladesh aids in early crop disease detection. This resource supports farmers and researchers in identifying Anthracnose, Bacterial Spot, Downy Mildew, and Pest Damage.

Keywords:
AgricultureBasella albaClassificationIdentificationImage processingPlant pathology

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Plant Pathology

Background:

  • Leaf diseases significantly impact crop yield and quality in agriculture.
  • Malabar Spinach (Basella alba) is a nutritious vegetable facing challenges from disease identification.
  • A lack of specialized datasets hinders accurate disease detection in Malabar Spinach.

Purpose of the Study:

  • To develop a unique dataset of Malabar Spinach leaf images from Bangladesh.
  • To facilitate automated disease detection and improve agricultural management.
  • To address the scarcity of resources for identifying crop ailments.

Main Methods:

  • Collected 3,006 original images of Malabar Spinach leaves.
  • Captured images under natural lighting across various locations in Bangladesh.
  • Categorized samples into healthy and diseased states, including Anthracnose, Bacterial Spot, Downy Mildew, and Pest Damage.

Main Results:

  • A comprehensive dataset of Malabar Spinach leaf images was created.
  • The dataset includes diverse examples of healthy and diseased leaves.
  • The images represent common diseases affecting the crop in Bangladesh.

Conclusions:

  • The dataset provides a valuable resource for agricultural research.
  • It enables the development and testing of computational models for disease detection.
  • This initiative supports farmers in managing Malabar Spinach diseases effectively.