Probabilistic Neural Network Southern Jujube Pest Stress Index Leaf Pigment Estimation Model Based Hyperspectral

Peng Zhou; Xin Wang Dou; Qing Zhao; Ming Deng Shi

doi:10.4028/www.scientific.net/AMM.610.362

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 610Probabilistic Neural Network Southern Jujube Pest...

Probabilistic Neural Network Southern Jujube Pest Stress Index Leaf Pigment Estimation Model Based Hyperspectral

Abstract:

Analysis jujube leaf rust pigment content and spectral reflectance correlation study comparing jujube leaf rust pigment content and differential spectral correlation. Hyperspectral characteristic parameters to achieve the southern jujube jujube leaf rust pigments PC1/PC2 and PC1+PC2 content estimation. Using a combination of linear and polynomial fitting method to construct the canopy hyperspectral disease dates Brix content estimation model and test. The probabilistic neural network PNN and SVM classifier SVC applied to hyperspectral estimation model, comparative analysis of model accuracy. The results of the quantitative estimation of disease hyperspectral information dates pigment content in leaves of jujube growing use of high spectral monitoring and impact assessment of disease have high practical value.

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Periodical:

Applied Mechanics and Materials (Volume 610)

Pages:

362-366

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.610.362

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Online since:

August 2014

Authors:

Peng Zhou*, Xin Wang Dou, Qing Zhao, Ming Deng Shi

Keywords:

Estimation Model, Jujube Pests, Pigment Content, Sensor Network, Stress Index

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* - Corresponding Author

References

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