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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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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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AqUavplant Dataset: A High-Resolution Aquatic Plant Classification and Segmentation Image Dataset Using UAV.

Md Abrar Istiak1, Razib Hayat Khan2,3, Jahid Hasan Rony2

  • 1RIoT Research Center, Independent University, Bangladesh, Dhaka, 1229, Bangladesh. abraristiakakib@gmail.com.

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Aquatic plant decline threatens ecosystems. This study introduces the AqUavplant dataset, using drones for high-resolution mapping to aid conservation efforts and biodiversity preservation.

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Area of Science:

  • Ecology
  • Remote Sensing
  • Botany

Background:

  • Aquatic vegetation is crucial for ecosystem stability but faces gradual decline.
  • Effective conservation requires accurate monitoring and mapping of aquatic plant species.
  • Unmanned Aerial Vehicles (UAVs) offer a viable solution for large-scale aquatic environment mapping.

Purpose of the Study:

  • To develop the AqUavplant dataset for aquatic plant mapping and monitoring.
  • To facilitate the development of machine learning models for automatic plant identification.
  • To support biodiversity conservation and management of aquatic ecosystems.

Main Methods:

  • Collected 197 high-resolution (4K) images of 31 aquatic plant species across nine sites in Bangladesh.
  • Utilized a DJI Mavic 3 Pro drone with a Ground Sampling Distance (GSD) of 0.04-0.05 cm/px for detailed image acquisition.
  • Created binary and multiclass semantic segmentation masks to complement the image dataset.

Main Results:

  • The AqUavplant dataset provides comprehensive, high-resolution imagery of diverse aquatic flora.
  • The dataset includes detailed segmentation masks essential for training machine learning algorithms.
  • This resource enables precise mapping and monitoring of aquatic vegetation.

Conclusions:

  • The AqUavplant dataset is a valuable resource for ecological research and conservation.
  • It supports the detection of indigenous and invasive species, monitoring of plant health, and biodiversity assessment.
  • The dataset aids in preventing aquatic plant extinction and preserving ecosystem balance.