the advent of green house technology has become most important and most widely used part in daily life because green house environment protects plants from undesirable environmental conditions and provides well desired conditions for growing under controlled atmosphere. The main aim of this paper is to propose effective method for crop monitoring in agricultural which shows the path to rural farming community to replace traditional crop cultivation techniques. The green houses are precisely used for improvement in productivity, quality, quantity and profitability of vegetable, flower and fruit crops. In this paper, green house approach has been presented supporting GSM wireless technology. The proposed green house system provides impact on varieties of crop species mostly flowers, vegetable crops and fruit crops. The presented system effectively monitors and controls the green house parameters of crucial importance like temperature, humidity, soil moisture, and light intensity and Co 2 gas. The system had been tested in green house environment and observations had been recorded for crop analysis purpose. The crop analysis helps farmers for monthly future prediction to know the expenditure for growing crop. This makes effective solution for farmers to grow highly efficient and disease free crop species for commercial production.
In this paper, we describe our work in Spoken language Identification using Visual Speech Recognition (VSR) and analyze the effect of various visual speech units used to transcribe the visual speech on language recognition. We have proposed a new approach of word recognition followed by the word N-gram language model (WRWLM), which uses high-level syntactic features and the word bigram language model for language discrimination. Also, as opposed to the traditional visemic approach, we propose a holistic approach of using the signature of a whole word, referred to as a “Visual Word” as visual speech unit for transcribing visual speech. The result shows Word Recognition Rate (WRR) of 88% and Language Recognition Rate (LRR) of 94% in speaker dependent cases and 58% WRR and 77% LRR in speaker independent cases for English and Marathi digit classification task. The proposed approach is also evaluated for continuous speech input. The result shows that the Spoken Language Identification rate of 50% is possible even though the WRR using Visual Speech Recognition is below 10%, using only 1[Formula: see text]s of speech. Also, there is an improvement of about 5% in language discrimination as compared to traditional visemic approaches.
The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biometrics traits like palmprint and face. First, palmprint and face are pre-processed, extracted features and calculated matching score of each trait using correlation coefficient and combine matching scores using t-norm based score level fusion. Face database like Face 94, Face 95, Face 96, FERET, FRGC and palmprint database like IITD are operated for training and testing of algorithm. The results of experimentation show that the proposed algorithm provides the Genuine Acceptance Rate (GAR) of 99.7% at False Acceptance Rate (FAR) of 0.1% and GAR of 99.2% at FAR of 0.01% significantly improves the accuracy of a biometric recognition system. The proposed algorithm provides the 0.53% more accuracy at FAR of 0.1% and 2.77% more accuracy at FAR of 0.01%, when compared to existing works.
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