An overview on detection, counting and categorization of silkworm eggs using image analysis approach

Image processing techniques have grown more important in the field of sericulture in the modern era, as the rapid growth of computer vision technology also provides a platform for image processing applications to obtain a better image. This review article provides an overview of the various types of algorithms used to count, classify, and detect silkworm eggs, whether the silworm eggs are fertilized (hatched) or unfertilized (unhatched), using image processing approaches. The literature review, analysis, and in-depth research explains the strengths and limits of the study and identify potential research problems. Modern tools and techniques for automatically counting, categorizing, and identifying silkworm eggs are being deployed, according to data gathered by previous researchers. A number of algorithms were used for automatic counting, categorizing, and detecting, however, the results were not accurate. As a result, in the field of sericulture, modern tools have proven essential to fully automatic counting, classifying, and detecting.