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Identification lodging degree of wheat using point cloud data and convolutional neural network.

Yunlong Li1, Baohua Yang1, Shuaijun Zhou1

  • 1School of Information and Computer, Anhui Agricultural University, Hefei, China.

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Assessing wheat lodging is crucial for crop yield. This study introduces a method using dimensionality reduction on point cloud data from drones, achieving high accuracy in classifying wheat lodging degrees with deep learning models.

Keywords:
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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Computer Vision

Background:

  • Wheat lodging significantly impacts grain quality and yield, necessitating accurate assessment methods.
  • Unmanned aerial vehicle (UAV) point cloud data offers potential for lodging assessment but faces processing challenges due to data clutter.
  • Existing methods struggle with the complexity of 3D point cloud data for precise wheat lodging degree classification.

Purpose of the Study:

  • To develop and evaluate a novel method for classifying wheat lodging degrees using dimensionality reduction on UAV-derived point cloud data.
  • To improve the efficiency and scalability of wheat lodging assessment for agricultural applications.
  • To compare the performance of different convolutional neural network (CNN) models in classifying wheat lodging degrees from processed point cloud data.

Main Methods:

  • Generated 2D images from 3D wheat point cloud data via dimensionality reduction using Hotelling transform and point cloud interpolation.
  • Employed three CNN models—AlexNet, VGG16, and MobileNetV2—for classifying different degrees of wheat lodging.
  • Utilized a self-built wheat lodging dataset for model training and performance evaluation.

Main Results:

  • The proposed method successfully generated dimensionality-reduced 2D images from UAV point cloud data.
  • The MobileNetV2 model demonstrated superior performance in classifying wheat lodging degrees, achieving an F1-Score of 96.7% during the filling stage and 94.6% during maturity.
  • The study confirmed the effectiveness of the point cloud dimensionality reduction technique for field-scale wheat lodging identification.

Conclusions:

  • The developed point cloud dimensionality reduction method effectively addresses the challenges of processing cluttered 3D data for wheat lodging assessment.
  • The integration of dimensionality reduction with CNN models, particularly MobileNetV2, provides a scalable and accurate solution for identifying wheat lodging degrees.
  • This approach holds significant promise for improving yield estimation, harvesting strategies, and agricultural insurance processes.