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[Quantitative models between canopy hyperspectrum and its component features at apple tree prosperous fruit stage].

Ling Wang1, Geng-xing Zhao, Xi-cun Zhu

  • 1College of Resource and Environment, Shandong Agricultural University, Taian 271018, China. lingwang@sdau.edu.cn

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|December 9, 2010
PubMed
Summary

Hyperspectral imaging accurately estimates apple yield by analyzing canopy reflectance. Support vector regression models show the highest accuracy for predicting fruit-to-leaf ratios, crucial for remote sensing applications.

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Area of Science:

  • Agricultural remote sensing
  • Quantitative spectral analysis
  • Precision agriculture

Background:

  • Hyperspectral techniques are fundamental to quantitative remote sensing.
  • Apple tree canopy hyperspectra contain complex information influenced by fruits, leaves, and ground cover.
  • Ground reflecting films significantly impact spectral reflectance, necessitating separate analysis.

Purpose of the Study:

  • To investigate the relationship between apple tree canopy hyperspectral reflectance and yield-related components.
  • To develop and compare models for predicting apple yield using hyperspectral data.
  • To establish a reliable theoretical basis for apple yield estimation via remote sensing.

Main Methods:

  • Separated analysis of apple trees with and without ground reflecting films.
  • Development of nine canopy component indexes from classified digital photos.
  • Correlation analysis between canopy reflectance and component indexes.
  • Application of correlation analysis, linear regression, BP neural network, and support vector regression models.

Main Results:

  • Reflectance showed the strongest correlation with the fruit-to-leaf ratio (max coefficient 0.815).
  • BP neural network and support vector regression models outperformed linear regression models.
  • Support vector regression achieved the highest accuracy in predicting the fruit-to-leaf ratio within the 611-680 nm band.

Conclusions:

  • Hyperspectral reflectance is a feasible predictor for apple yield estimation.
  • Support vector regression offers the most accurate method for yield prediction based on spectral data.
  • This research provides a valuable reference for applying remote sensing in apple production.