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Diseases in plants accounts to major crop losses which affects the economy of farmers and in turn the country. Recent advancements in technologies like Internet of Things and Machine learning has led to the development of automated systems for crucial applications. This paper focuses on developing such a system for agricultural application mainly for early detection of diseases in plant leaves and provides solutions to the farmers. The main contribution is the novel pixel replacement-based segmentation and double feature extraction techniques for enhancing classification process. Classification is done using Support vector machine classifier and the developed system was validated using pomegranate leaves. The system is evaluated using performance metrics such as detection accuracy and classification accuracy.

Agriculture is one of the primary sources of Indian economy as majority of the population depends on agriculture. Early detection of plant stress functions an evident role in smart agriculture. In order to improve the quality and quantity of food crop and to prevent the damage caused to the plant, it is necessary to detect the stress in plant using environmental parameters. Visual sensor along with environmental sensors helps to identify the colour change in leaves of the plant at the initial stage thereby reducing the damage. An efficient plant stress detection framework is proposed which is automated for ease of use for farmers. The framework makes use of camera for capturing the images of the leaves in the field and machine learning for classifying the leaves as healthy or unhealthy. Support vector machine algorithm makes use of the features extracted from the leaves for training the system and the input features are alone transmitted to the cloud. The agricultural consultant at the other end retrieves the features and the stress is detected with the help of classification. Gray level co-occurrence matrix textures are used for extracting the features from the healthy and unhealthy leaves. Metrics such as Accuracy in classification and stress detection is validated in the proposed system.

Digital communication plays a vital role in transmitting and receiving the information. With the advent of advancements in telecommunications, requirement of high data rate is in evident in Digital communication for the transmission of the signal. The information which has to be transmitted over a long distance needed to be error free while receiving in order to obtain the quality of the information. Emphasizing on the high data rate and also error free requirements, the role of Multiple Input Multiple Output and Wavelet as a combination makes progress towards high extent in achieving successful transmission. The proposed work emphasizes on the application of Discrete wavelet transform for filtering the Quadrature Amplitude modulated wave for the internet telephonic communication where the digital communication plays a major role. This proposed work purely concentrates on the digital communication part of the internet telephony communications. Stein’s Unbiased Risk Estimate is used to obtain the information samples with soft thresholding. The Bit to Error rate performance is measured for various modulation order and the simulated results are shown and also the values of the Bit to Error rate are tabulated.

Noise in a signal is an unwanted Distribution occurred which disrupts the original signalduring transmission, Depuration or conversion. Also noise may be random, with an unspecific mean or white noise with an independently interleaved distribution, zero mean by the processing algorithms. Performance degradation of Orthogonal Frequency Division Multiplexing system with Multiple Input and Multiple Output would have ensued due to the noise distribution in an original signal, so it is inevitable to reduce the unwanted noise distribution in an original signal to recover the original information which is sent at the transmitter. Various filtering techniques in channel estimation have been proposed in this literature to denoise a signal in Multiple Input Multiple Output – OFDM system effectively. The main intent of this survey is to provide the better insight of various denoising techniques in channel estimation to reduce unknown noise distributions in a signal with the tradeoff complexity in MIMO – OFDM systems.