<span>Machine learning methodologies are commonly used in the field of precession farming. It prospects greatly in the plant safety measure like disease detection and classification of pest attacks. It highly influences the crop production and management. The venture of our system is to produce healthy plantation. The proposed system involves Enhanced Fractal Texture Feature Analysis and Machine Learning methodology for classification. Hence more than ever there is a need for such a tool that combines image processing methodologies and the Neural network concepts and that is supported by huge cloud of structured data which makes the diagnosis part much easier and convenient. The proposed system recognizes and classifies the plant taxonomy and the infection also it measures the percentage of infection. The neural network concept followed in our proposed system is focused on Artificial Neural Network which uses Recursive Back Propagation Neural network to speed up the training process and the weights on ANN is optimized using Genetic Algorithm based Particle Swarm Optimization technique. We have used MATLAB to implement the concept and obtained better accuracy in disease detection and proved to be an efficient method.</span>

The diminishing expenditure of consumer electronic devices such as digital cameras and digital camcorders along with ease of transportation facilitated by the internet, has lead to a phenomenal rise in the quantity of multimedia data. The need to find a desired image from a collection is shared by many professional groups, including journalists, design engineers and art historians. While the requirements of image users, it can be characterize image queries into three levels. The proposed method based on primitive features such as color and shapes. These features are extracted and used as the basis for a similarity check between images. The shape and color features are extracted through Gradient Edge Detection and color histogram the combination of these features is robust. The experiment results show that the proposed image retrieval is more efficient and effective in retrieving the user interested images

Abstract-Texture is a possession that represents the facade and arrangement of an image. Image textures are intricate ocular patterns serene of entities or regions with sub-patterns with the kind of brightness, color, outline, dimension, and etc.This article proposes a new method for texture characterization by using statistical methods (TCUSM). In this proposed method (TCUSM) the features are obtained from energy, entropy, contrast and homogeneity. In an image, each one pixel is enclosed by 8 nearest pixels. The confined in turn for a pixel can be extracted from a neighbourhood of 3x3 pixels, which represents the fewest absolute unit. We used four vector angles 0, 45, 90,135 to carry out the experimentation with the query image. A total of 16 texture values are calculated per unit. Compute the feature vectors for the query image by calculating texture unit and the resultant value is compared with the image database. The retrieval result shows that the performance using Canberra distance has achieved higher performance.

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