Abstract
Image segmentation is a significant processing stride in a large number of machine vision applications. Although there exist different techniques for segmentation, it is important to determine and find an optimal technique for a particular context. For an automated machine vision-based fruit disease recognition context, image segmentation plays a very important role for extracting features from the location and size of defective areas. In this research work, we conduct a profound analysis of four prominent segmentation techniques, namely Otsu’s method, K-means clustering, fuzzy c-means clustering, and region growing in extracting the defective regions of three common fruits of Bangladesh, namely guava, jackfruit, and papaya. For the evaluation, we have used six region-based metrics. K-means clustering segmentation technique is found outperforming all other segmentation techniques in terms of quantitative evaluation metrics by attaining an aggregate accuracy of 81.65%.
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Habib, M.T., Khatun, A., Aziz, M.A., Uddin, M.S., Ahmed, F. (2021). An In-Depth Analysis of Different Segmentation Techniques in Automated Local Fruit Disease Recognition. In: Uddin, M.S., Bansal, J.C. (eds) Computer Vision and Machine Learning in Agriculture. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6424-0_9
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