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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: Apr 1, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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CTRNet: a lightweight and efficient deep learning model for field maize whorl identification.

Xiaojun Tian1, Jingkang Zhang1, Yanqiang Li2

  • 1Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, Shandong, China.

Scientific Reports
|March 30, 2026
PubMed
Summary
This summary is machine-generated.

A new Contextual and Texture-enhanced Representation Network (CTRNet) improves maize whorl detection in challenging field conditions. This AI model enhances accuracy for small targets, even with occlusion and varying light, aiding precision agriculture.

Keywords:
Attention mechanismComputer visionMaize whorlPest control

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Accurate maize whorl detection is difficult due to small target size, leaf occlusion, complex backgrounds, and variable lighting.
  • Existing methods struggle with these challenges, impacting precision agriculture applications.

Purpose of the Study:

  • To develop an efficient and robust maize whorl detection network.
  • To enhance feature representation for small targets under occlusion and varying illumination.

Main Methods:

  • Proposed the Contextual and Texture-enhanced Representation Network (CTRNet).
  • Integrated multi-scale contextual interaction (MSCIM), dual-channel fine-grained feature enhancement (DCFEM), and gated adaptive information fusion (GAIFusion).
  • Incorporated an adaptive channel-edge attention mechanism (ACEA) to handle background and illumination variations.

Main Results:

  • CTRNet improved mAP@0.5 from 81.6% to 84.7% in complex field scenarios.
  • Achieved significant enhancements in detection robustness for small targets under occlusion and varying lighting.
  • The network is efficient and lightweight, with only 2.38 million parameters.

Conclusions:

  • CTRNet offers an effective solution for maize whorl detection in challenging environments.
  • The proposed network contributes to precision monitoring and targeted pesticide application in maize.
  • This research advances automated agricultural management systems.