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Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms.

Eric Rodene1,2, Gayara Demini Fernando3, Ved Piyush3

  • 1Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning methods automate tassel counting in maize using drone imagery. These tools improve plant breeding by enabling accurate flowering trait estimation for crop enhancement.

Keywords:
UAV imageryconvolutional neural networkhigh-throughput phenotypingimage segmentationmachine learningmaize tassel detectionobject detection

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Area of Science:

  • Agricultural Science
  • Plant Breeding
  • Remote Sensing

Background:

  • Unmanned aerial vehicle (UAV)-based imagery is crucial for collecting time-series agronomic data in plant breeding.
  • Automated data collection and analysis are essential for efficient crop improvement programs.

Purpose of the Study:

  • To develop machine learning methods for automated tassel counts at the plot level using UAV-based imagery.
  • To assess the accuracy of object-based counting-by-detection (CBD) and density-based counting-by-regression (CBR) approaches.

Main Methods:

  • Leveraged an aerial photography dataset of 233 maize inbred lines.
  • Developed and compared CBD and CBR machine learning approaches.
  • Employed image segmentation to isolate plant tassels for improved detection.

Main Results:

  • Object-based (CBD) detection achieved a peak cross-validation prediction accuracy (r²) of 0.7033 with filtered images (90% threshold).
  • Density-based (CBR) approach showed the best accuracy with unfiltered images (MAE of 7.99).
  • Filtered images (90% threshold) using bootstrapping yielded a slightly better MAE (8.65) than unfiltered images (8.90).

Conclusions:

  • Developed accurate machine learning methods for automated tassel counting in maize.
  • These methods facilitate precise estimation of flowering-related traits.
  • The approach supports data-driven breeding decisions for enhanced crop improvement.