A New Algorithm for Feature Extraction of Plant Diseases and Insect Pests Signal Based on Optimal Wavelet Packet and Non-negative Matrix Factorization
- DOI
- 10.2991/icence-16.2016.91How to use a DOI?
- Keywords
- Plant diseases and insect pests, feature extraction, optimal wavelet packet basis, non- negative matrix factorization, algorithm optimization
- Abstract
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.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Changcheng Li AU - Shuyun Cai AU - Laiwu Yin PY - 2016/09 DA - 2016/09 TI - A New Algorithm for Feature Extraction of Plant Diseases and Insect Pests Signal Based on Optimal Wavelet Packet and Non-negative Matrix Factorization BT - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016) PB - Atlantis Press SP - 480 EP - 485 SN - 2352-538X UR - https://doi.org/10.2991/icence-16.2016.91 DO - 10.2991/icence-16.2016.91 ID - Li2016/09 ER -