Multi-level Segmentation of Fruits Using Modified Firefly Algorithm

The data explosion caused by the Internet and its applications has given researchers immense scope for data analysis. A large amount of data is available in form of images. Image processing is required for better understandability of an image. Various image processing steps are available for improving the image in different application areas. Various applications like medical imaging, face recognition, biometric security, fruit quality evaluation, and traffic surveillance depend only on image and its analysis. This analysis in several applications is highly dependent on the outcome of image segmentation. This paper focuses on the good segmentation of different kinds of fruits through multi-level thresholding. In this paper, multi-level segmentation based on modified Firefly Algorithm (FA) with Kapur's, Tsallis, and fuzzy entropy is proposed. FA is used to optimize fuzzy parameters for obtaining optimal thresholds. The levy flight and local search are implemented with FA. The various experiments have been performed on apple, banana, mango, and orange images with the distinct threshold (i.e., 2, 3, 4) values. The proposed algorithm has been estimated quantitatively and qualitatively by using parameters like peak signal-to-noise ratio (PSNR) and structured similarity index metric (SSIM).

Automated summary

The Internet and its applications have led to a massive amount of data, particularly in the form of images, which has provided researchers with vast opportunities for data analysis. Image processing is crucial for improving the understanding of images, and various image processing steps can enhance images in different application areas. Many applications, such as medical imaging, face recognition, biometric security, fruit quality evaluation, and traffic surveillance, heavily rely on image analysis and segmentation. This paper focuses on multi-level thresholding for accurately segmenting different types of fruits, proposing a modified Firefly Algorithm (FA) that optimizes fuzzy parameters to obtain optimal thresholds. The algorithm utilizes levy flight and local search for improved performance. The proposed method is evaluated quantitatively and qualitatively on apple, banana, mango, and orange images using parameters like peak signal-to-noise ratio (PSNR) and structured similarity index metric (SSIM).