Field Collected Plant Spectrum Denoising by Logarithm Transform and Wavelet Transform

Zhou, Guangzhu; Wang, Cui-Zhen; Yang, F. L.; Li, Yin-Ming

doi:10.3724/sp.j.1010.2009.00316

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…The results show that the wavelet transform presents the periodic components included in the variations of micrometeorological factors in and out of the plant canopy [6]. In order to discriminate and recognize the phytoplanktons from different categories, the second-order low-frequency component of Daubechies wavelet is used to extract the discrete spectrum features of ten phytoplanktons [7]. The method of using the multi-scale wavelet transform is presented to test the verge of plant root image [8].…”

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Feature Extraction of Plant Diseases and Insect Pests Signal Based on Optimal Wavelet Packet and Non-negative Matrix Factorization

Li¹,

Cai²,

Yin³

2016

Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

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Plant diseases and insect pests have similar symptoms, but it is difficult to distinguish between professional and technical personnel to identify plant diseases and insect pests. In order to accurate extraction of plant diseases and insect pests, physiological and pathological characteristics of signal, puts forward a based on lifting wavelet transform feature extraction algorithm optimization scheme, for the study of plant diseases and insect pests damage signal showing the effect of the prior farmers identify any disease, choose the correct method of governance, quickly make the right decision, improve farmers plant diseases and insect pests, harm signal feature extraction and recognition level. The simulation results show that this algorithm can be used to optimize the stability and convergence, and can be used as an ideal plant disease and insect pests signal feature extraction optimization algorithm, which can effectively identify the different plant diseases and insect pests.

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Section: Introductionmentioning

confidence: 99%

A New Algorithm for Feature Extraction of Plant Diseases and Insect Pests Signal Based on Optimal Wavelet Packet and Non-negative Matrix Factorization

Li¹,

Cai²,

Yin³

2016

Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

View full text Add to dashboard Cite

show abstract

“…The method is presented to use the Gabor wavelet for testing the verge of plant root image [2]. The spectrum noise falls into the scope of multiplicative noise, and the method of combining the logarithmic transformation and wavelet transform is put forward on the basis of the theoretical deduction [3], The wavelet soft-threshold de-noising method and the fast Fourier transform (FFT) are used to study the basic features and variation of the electrical signal of aloe leaves under different temperatures [4]. The experiment system of the virtual instrument for collecting the plant electrical signals is designed by using the technology of wavelet soft-threshold denoising method [5].…”

Section: Introductionmentioning

confidence: 99%