Enhanced images have high quality and clarity than original captured images. Computer vision image enhancement (Color conversion and Histogram equalization) is used in different real time applications such as remote sensing, medical image analysis and plant leaves disease detection. Original captured images are RGB images. RGB images are combination of primary colors (Red, Green and Blue). It is difficult to implement the applications because of the range of this color is 0 to 255. Grayscale images have only the range between 0 and 1. So it is easy to implement many applications. Histogram equalization is used to increase the images clarity. Grayscale conversion and histogram equalization is used in plant leaves disease detection.
Abstract-Digital image processing is employed in numerous areas of biology to identify and analyse problems. This approach aims to use image processing techniques for citrus canker disease detection through leaf inspection. Citrus canker is a severe bacterium-based citrus plant disease. The symptoms of citrus canker disease typically occur in the leaves, branches, fruits and thorns. The leaf images show the health status of the plant and facilitate the observation and detection of the disease level at an early stage. The leaf image analysis is an essential step for the detection of numerous plant diseases.The proposed approach consists of two stages to improve the clarity and quality of leaf images. The primary stage uses Recursively Separated Weighted Histogram Equalization (RSWHE), which improves the contrast level. The second stage removes the unwanted noise using a Median filter. This proposed approach uses these methods to improve the clarity of the images and implements these methods in lemon citrus canker disease detection.
In the process of physical annealing, a solid is heated until all particles randomly arrange themselves forming the liquid state. A slow cooling process is then used to crystallize the liquid. This process is known as simulated annealing. Simulated annealing is stochastic computational technique that searches for global optimum solutions in optimization problems. The main goal here is to give the algorithm more time in the search space exploration by accepting moves, which may degrade the solution quality, with some probability depending on a parameter called temperature. In this discussion the simulated annealing algorithm is implemented in pest and weather data set for feature selection and it reduces the dimension of the attributes through specified iterations.
The forming behavior of AA6061 boron carbide composites produced by stir casting process was investigated with the cold upsetting test. The composites containing 0%, 5%, 10% and 15% of B4Cp reinforcements were investigated with the cold upsetting test in Universal Testing Machine under tri-axial stress state condition. SEM images were taken to identify the presence of B4Cp particle in aluminium matrix. From the analysis, it was found that the hardness of composite was increased due to increasing amount of boron carbide particle in the composite and the density was decreased due to the lower value of density of boron carbide. The maximum true axial stress, true hoop stress and hydrostatic stress were gradually increased in the event of increasing order of B4Cp in the composites. Finally, it was found that in the stress – strain curve, the boron carbide was the main factor in improving the compressive strength of composites because of its high hardness.
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