Analysis of Image Improvement and Edge Identification Methods in Watermelon Image

Abstract

The initial stage in digital image processing, known as pre-processing, plays a vital role in enhancing image quality. This essential step involves employing various techniques to prepare the image for subsequent analysis and feature extraction. Among the array of pre-processing methodologies utilized, thresholding, median averaging, median filtering, rapid Fourier transform, point operations, intensity modification, and histogram equalization stand out as prominent tools. These techniques are employed to mitigate noise, enhance contrast, and optimize the overall visual quality of the image. Once the pre-processing phase is complete, the focus often shifts to specific tasks, such as identifying objects or features within the image. In the context of analyzing watermelon images, one such task is the detection of watermelon seeds. To accomplish this, the pre-processed image undergoes further refinement through the application of edge detection techniques. Gradient edge detection, isotropic, Canny, and Sobel edge detection are among the methods commonly employed for this purpose. These techniques aim to highlight the edges and contours of objects within the image, facilitating the identification of distinct features such as watermelon seeds. However, our investigation reveals that not all edge detection methods are equally effective in this context. By employing a combination of pre-processing techniques and judiciously selecting edge detection methods, researchers can enhance the accuracy and reliability of their image processing workflows, ultimately advancing our understanding of complex biological structures such as watermelon seeds.