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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

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field.

Le Wang1,2, Lirong Xiang2, Lie Tang2

  • 1College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.

Sensors (Basel, Switzerland)
|January 16, 2021
PubMed
Summary
This summary is machine-generated.

Automated corn stand counting using YoloV3 and Kalman filters achieves over 98% accuracy. This robust method overcomes challenges of manual counting and UAV limitations for efficient early season crop assessment.

Keywords:
YoloV3corn stand countingdeep learningvideo tracking

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Plant Breeding

Background:

  • Manual corn stand counting is labor-intensive and error-prone.
  • Unmanned Aerial Vehicles (UAVs) offer potential but face resolution and operational limitations for early seedling detection.
  • Challenges include camera motion, wind, shadows, and complex backgrounds.

Purpose of the Study:

  • To develop an automated, robust, and high-throughput method for accurate corn stand counting.
  • To address the limitations of manual methods and UAV-based systems for early season crop assessment.

Main Methods:

  • Developed a pipeline using YoloV3 network and Kalman filter for online corn seedling counting.
  • Utilized color images extracted from video clips for analysis.
  • Tested the system on corn at V2 and V3 growth stages.

Main Results:

  • Achieved over 98% accuracy in corn stand counting at V2 and V3 stages.
  • Operated at an average frame rate of 47 frames per second (FPS).
  • Demonstrated robustness against common challenges like motion blur and complex backgrounds.

Conclusions:

  • The developed automated method is accurate and reliable for corn stand counting.
  • The pipeline can be easily integrated with various ground-based systems (carts, tractors, robots) for versatile deployment.
  • Offers a significant advancement over traditional methods for precision agriculture.