According to the self similarity of plant electrical signal (fractal feature), changes of plant electrical signal amplitude a moment with the physical environment and mutation, causing plant electrical signal is not continuous. Electrical signal fractal characteristics of plant changes along with the time development, but at some point, it does not change with time change. This paper adopts the wavelet coefficient and self similarity relationship, through the index of self similarity calculation between plant electrical signal and wavelet to obtain the wavelet decomposition. Self similarity index is large, and plant electrical of the self similar degree are high. The simulation experiment results show that the self similarity index diagram after wavelet decomposition display can be found in many scales, the wavelet coefficients are very similar looking, providing a new idea for the detection of plant electrical signal characteristics of the physical environment.

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.