The geometric and color features of agricultural material along with related physical properties are critical to characterize and express its physical quality. The experiments were conducted to classify the physical characteristics like size, shape, color and texture and then workout the relationship between manual observations and using image processing techniques for weight and volume of the four wheat refractions i.e. sound, damaged, shriveled and broken grains of wheat variety PBW 725. A flatbed scanner was used to acquire the images and digital image processing method was used to process the images and output of image analysis was compared with the actual measurements data using digital vernier caliper. A linear relationship was observed between the axial dimensions of refractions between manual measurement and image processing method with R2 in the range of 0.798–0.947. The individual kernel weight and thousand grain weight of the refractions were observed to be in the range of 0.021–0.045 and 12.56–46.32 g respectively. Another linear relationship was found between individual kernel weight and projected area estimated using image processing methodology with R2 in the range of 0.841–0.920. The sphericity of the refractions varied in the range of 0.52–0.71. Analyses of the captured images suggest ellipsoid shape with convex geometry while the same observation was recorded by physical measurements also. A linear relationship was observed between the volume of refractions derived from measured dimensions and calculated from image with R2 in the range of 0.845–0.945. Various color and grey level co-variance matrix texture features were extracted from acquired images using the open-source Python programming language and OpenCV library which can exploit different machine and deep learning algorithms to properly classify these refractions.

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Image Processing is an art to get an enriched image or it can be used to retrieve information. This image processing methods are used in medical field also. Numerous modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) etc. are used to analyze and diagnose diseases.Pixel-level image fusion is a combination of several images collected from various inputs and gives more information than any other input messages. Pixel-level image fusion shows a vital role in medical imaging. In this paper, pixel-level image fusionsmethods are survived and review the fusion quality measures are being used. Finally this surveycomplete with different kinds of image fusion methods proposed and still there are so many imminent ways in image fusion applications. Hence image fusions fields are pointedly develop in the forthcoming years.

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