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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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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...
509

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Related Experiment Video

Updated: Aug 25, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

483

Wheat lodging extraction using Improved_Unet network.

Jun Yu1, Tao Cheng1, Ning Cai1

  • 1National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China.

Frontiers in Plant Science
|October 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an Improved_Unet deep learning model for accurate wheat lodging detection using Unmanned Aerial Vehicle (UAV) imagery. The model demonstrates superior performance and robustness in identifying lodging areas, crucial for crop management.

Keywords:
Improved_UnetUAV imagesdeep learninglodging extractionwheat

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Area of Science:

  • Agricultural remote sensing
  • Computer vision for crop monitoring
  • Deep learning for precision agriculture

Background:

  • Accurate wheat lodging assessment is vital for yield loss evaluation and breeding lodging-resistant varieties.
  • Current methods face challenges in balancing timeliness and accuracy, with a lack of effective extraction techniques.
  • Unmanned Aerial Vehicle (UAV) technology offers potential for high-resolution crop monitoring.

Purpose of the Study:

  • To develop and evaluate an improved deep learning model (Improved_Unet) for wheat lodging area extraction.
  • To assess the model's applicability across various UAV flight heights.
  • To verify the network's robustness using data from different years and locations.

Main Methods:

  • High-definition images of wheat canopy were captured using a quadrotor UAV at grain filling and maturity stages.
  • The Unet network was enhanced with Involution operator and Dense block modules.
  • The Improved_Unet's performance was evaluated against Segnet, Unet, and DeeplabV3+ using data from multiple flight heights and years.

Main Results:

  • Improved_Unet outperformed other networks in segmentation accuracy, with average improvements of 3-6% across indicators.
  • The model achieved highest accuracy (Precision: 0.907, Dice: 0.929, Recall: 0.884, Accuracy: 0.933) for lodging extraction at the maturity stage.
  • The network demonstrated strong robustness, achieving Precision, Dice, Recall, and Accuracy of 0.851, 0.892, 0.844, and 0.885 respectively, on external data.
  • Segmentation accuracy decreased with increasing flight height; 20m yielded the best results.

Conclusions:

  • The Improved_Unet network effectively extracts wheat lodging areas with enhanced accuracy and robustness.
  • The model's performance is influenced by UAV flight height, with lower altitudes being more effective.
  • This deep learning approach provides a valuable tool for automatic wheat lodging extraction and crop management.