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Top1. Introduction
Now-a-days the present agricultural science and technology is intensely advanced. The worth of fruits and vegetables depends on their quality. It is an imperative concern how to evaluate the quality of fruits in agricultural / horticultural realm (Saxena, 2014), and (Balakrishna, et al., 2019). The orthodox method of fruits quality judgment is made by the experts in those domains that is quite effective, but is very time-consuming. It becomes incredibly imperative to examine the fruit diseases very precisely within limited time. Study reveals that, approximately 50% of fruits like apples, oranges, lemons, grapes, bananas etc. are destroyed every year due to plant diseases which cannot be detected professionally at the early stage. Few diseases can be identified by human experts, but it is always not likely to get them on time at remote areas. Some fruit diseases are so complicated that they require powerful microscopes for their identification. Hence, the expansion of computer visualization system for identifying and categorizing disease in fruits will immensely evade human intervention and will lead to impartial decision making about disease detection in fruits and this will also help in quick and absolute recovery of the disease. With the advent of image segmentation (Masood, 2016), and (Singh, et al., 2020) we are effortlessly able to identify the defected portion of the fruit.
Digital images are now regarded as a key factor of conveying information in this real world. Mining the information from images and studying them minutely in order to make the extorted information valuable for several applications is a vital quality of digital image processing. Image segmentation (Gonzales, et al., 2008) plays a key role in extorting the required features from the images. The data pixels with familiar visual characteristics are grouped into the same region and are separated from those having different characteristics. At present, image processing forms the mainstay in the research area in almost all the disciplines. For instance, after minutely analyzing the segmented images the cancerous tissue (Altarawneh, 2012) and (Kahaki, S. M. M, et al. 2017) can be effortlessly distinguished from the non-cancerous ones. From the results obtained from segmentation, it is effortlessly feasible to discover the essential area of significance.