This paper summarizes the outcome of the survey carried out for quality identification of a tea leaf and eventually price prediction. Quality identification can allow to categorizing leaf in different grades, which helps the buyer and seller to acquire suitable quality to their need. Price prediction is an important feature, which can bring certainty at price and farmers can be benefitted more for their reasonable good quality. Additionally, if the leaf disease is identified at the initial stage that would also allow farmers to timely resolve the concerned issues and save their corps. In the field of agriculture, this has been always a research area to identify and predict the quality of tea leaves. Various artificial intelligence techniques are hot topics in the field of recognition and their effective combination cannot only solve the problem but also enhance recognition accuracy. Therefore, there is an imminent need for a detailed survey on compiling techniques used for the identification of different varieties of tea plants. In this research, we aim to propose a review of the various techniques which can be utilized for determining the quality and price prediction. The survey is hybrid in nature with a combination of different artificial techniques, which is a suitable approach to target effective tea leaf identification. Further for the classification of tea leaf images, various algorithms can be combined as well to obtain better results and different algorithms can be used for feature extraction on the basis of texture extraction, color extraction, and shape extraction.

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In this paper BPNN performance for the gait analysis with using the different modulation techniques i.e LDA and MDA. Gait Analysis is the new technique in the biometric identification, the gait have more advantages over the field of biometric systems like face recognition, finger printing etc. Gait Analysis is a method by which individual can be recognized by the manner of walk. Less unobtrusive gait recognition system over the other biometric traits is the main advantage. i.e. it offers the identification of an individual at a particular distance, without any physical interference or contact with an individual and can be easily apply on the low resolution image frames as well. In this paper, firstly the video of an individual in captured, secondly background subtraction is applied on that so as to remove the unwanted information, thirdly feature extraction is carried out to extract the various parameters by using the Hanavan's model, and finally the recognition is performed by using BPNN+LDA and BPNN+MDA techniques, are used for the training and the testing purposes, and the matching can also be performed on the basis of CBIR. All the processes are performed on the gait database and the input video. KeywordsBack-propagation neural network(BPNN),CBIR, Feature extraction, Gait recognition system, linear discriminant Analysis (LDA) and multilinear discrimant analysis(MDA).

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