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The classification and identification of cotton leaf diseases is important as it can prove detrimental to the yield. The classifier needs most discriminating features to improve the effectiveness and efficiency of analysis and classification for that reason feature extraction and representation is a decisive step for pattern recognition system. In the proposed work we present a graph cut based approach for the segmentation of images of diseased cotton leaves. The testing samples of the images are captured from the fields at Central Institute of Cotton Research Nagpur, and the cotton fields in Buldhana and Wardha district. The Gaussian filter is applied to remove the noise present in the images before segmentation. The Color layout descriptor which is a very compact and resolutioninvariant representation of color and can be used for a variety of similarity-based retrieval, content filtering and visualization are extracted along with shape parameters as features. The diseases that have been selected for experimentation are Bacterial Blight, Myrothecium and Alternaria.

The objective of this work was to develop and to evaluate adaptive neuro-fuzzy inference system as methodology to identify the leaf diseases on cotton. This paper presents automatic system for classification of three cotton leaf diseases namely Bacterial Blight, Myrothecium and Alternaria. Graph cut method is used for segmentation of images to extract color layout descriptors as features to train the adaptive fuzzy inference system. The testing samples are collected from Central Institute for Cotton Research, Nagpur and from the fields in Buldana and Wardha district.

EEG analysis is used as a tool for medical diagnosis of various diseases related to brain like epilepsy, dementia, brain disorders like stress etc. To identify such neural disorders it is essential to process these signal because of the nonstationary behavior. The time frequency analysis of EEG signals provide the correct visualization of EEG signals to extract the various rhythms of frequencies like alpha, beta, gamma waves. The Wavelet transform is used to carry out the time frequency analysis of EEG signal. Wavelets are used to analyze the EEG signals correctly. Their applications include seizure analysis, modeling of neuron potentials etc. To perform these applications it is necessary to perform the mathematical operations on EEG signal by windowing, filtering and then finally reconstruction.