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In country like India, where agricultural economy plays major role, understanding and tracking the soil nutrients are essential. However, the chemical analysis, which determines the nutrient contents of soil, is expensive and time consuming. Hence, we attempt to exploit remote sensing imagery for estimating them. This paper analyzes the correlation between the level of soil nutrients and wavelet decompositions of remote sensing imagery of a particular region. Four renowned wavelet transformations such as Daubechies, Symlet, Biorthogonal and Coiflet are used to represent the image in wavelet domain. Subsequently, here exploit a neural network model to predict the soil nutrient content using the principle wavelet components. Experimental analysis on the prediction accuracy and the correlation measure reveals the suitability of each wavelet transformation of remote sensing imagery in predicting the soil nutrients.

Abstract-a modified filter strategy for harmonic elimination is introduced in this paper. The proposed approach eliminates all harmonics component from the signal (current or voltage) and it requires only knowledge of fundamental frequency of the signal. The proposed filter eliminates undesirable harmonics from periodic signal using adaptive algorithm. Adaptation process adjusts weights to exactly match amplitude and phase of fundamental frequency component and the outputs of the filter is a harmonics replica and are subtracted from the original periodic signal waveform to eliminate them. The bipolar waveforms are roughly analyzed and considered case of square wave pattern which contain all odd harmonics. The simulation results show that the method can effectively eliminate undesirable harmonics and result in low(less than two percentage) total harmonic distortion (THD).

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