In low level data processing functions, like FIR filtering, pattern recognition or correlation, where the parallel implementation is supported by architecture matched special purpose arithmetic; high throughput FPGA circuits easily outperform even the most advanced DSP processors. This paper investigates a high-speed, non-linear adaptive median filter implementation. Adaptive median filter solves the dual purpose of removing the impulse noise and reducing distortion in the image. It can achieve the filtering operation of an image corrupted with impulse noise of probability greater than 0.2.
<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>
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