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Identification and estimation of lodging in bread wheat genotypes using machine learning predictive algorithms.

Ehsan Rabieyan1, Reza Darvishzadeh1, Hadi Alipour2

  • 1Department of Plant Production and Genetics, Urmia University, Urmia, Iran.

Plant Methods
|October 18, 2023
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Summary
This summary is machine-generated.

Early wheat lodging can be predicted using machine learning. The Random Forest (RF) model accurately estimates lodging, aiding in precise, non-destructive monitoring and management strategies for improved crop yields.

Keywords:
Image processingLodgingMachine learningRandom forestWheat

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Area of Science:

  • Agricultural Science
  • Plant Breeding
  • Machine Learning Applications

Background:

  • Wheat lodging (stem bending) significantly reduces crop yield and quality.
  • Early identification of lodging-resistant genotypes is crucial for crop improvement.
  • Image processing and machine learning offer potential for non-destructive trait evaluation.

Purpose of the Study:

  • To identify superior wheat genotypes for lodging resistance.
  • To compare the predictive accuracy of multiple linear regression (MLR), support vector regression (SVR), artificial neural network (ANN), and random forest regression (RF) for wheat lodging.
  • To evaluate the potential of machine learning models for early lodging prediction.

Main Methods:

  • Cultivated 228 wheat accessions under field conditions across two cropping seasons.
  • Employed an alpha-lattice experimental design with two replications.
  • Measured plant traits using image processing on 20 isolated plants per plot.

Main Results:

  • Lodging score index (LS) showed strong positive correlations with plant height, number of nodes, and internode length.
  • The Random Forest (RF) algorithm demonstrated superior accuracy (R²=0.887 training, R²=0.768 testing) in predicting wheat lodging compared to ANN and SVR.
  • RF exhibited robust performance, comparable to ANN and superior to SVR.

Conclusions:

  • The RF model serves as a valuable predictive tool for estimating wheat lodging in field conditions.
  • This approach supports precise, non-destructive monitoring of lodging.
  • Findings can inform managerial strategies for mitigating lodging impacts on wheat production.