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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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During photosynthesis, plants acquire the necessary carbon dioxide and release the produced oxygen back into the atmosphere. Openings in the epidermis of plant leaves is the site of this exchange of gasses. A single opening is called a stoma—derived from the Greek word for “mouth.” Stomata open and close in response to a variety of environmental cues.
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Updated: Sep 16, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task Training.

Ziqi Yang1,2, Yiran Liao3, Ziao Chen2,4

  • 1College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China.

Plants (Basel, Switzerland)
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

A new AI model, StomaYOLO, accurately detects maize stomata for efficient plant phenotyping. This technology aids crop breeding and smart agriculture by improving stomatal analysis.

Keywords:
YOLOmaize stomamulti-task trainingprecision agriculture

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Area of Science:

  • Agricultural Science
  • Plant Biology
  • Computer Vision

Background:

  • Stomata are crucial for maize photosynthesis and drought response.
  • Manual stomatal detection is inefficient and subjective for high-throughput phenotyping.

Purpose of the Study:

  • To develop a novel, efficient AI model for detecting maize stomata in microscopic images.
  • To enhance stomatal recognition for high-throughput plant phenotyping research.

Main Methods:

  • Curated a dataset of over 1500 maize stomata images.
  • Developed StomaYOLO, a lightweight detection model based on YOLOv11 framework.
  • Integrated Small Object Detection layer P2, dynamic convolution, and auxiliary training.

Main Results:

  • StomaYOLO achieved 91.8% mean average precision (mAP) and 98.5% precision.
  • Outperformed mainstream detection models in detecting small stomatal targets.
  • Demonstrated significant performance gains with integrated strategies (nearly 9% mAP improvement).

Conclusions:

  • StomaYOLO offers a cost-effective and accurate solution for maize stomatal phenotyping.
  • The model supports advancements in crop breeding and smart agriculture.
  • This tool is suitable for practical implementation in plant research.