Advancements in nutty quality: Segmentation for enhanced monitoring and determination

P.  Saranya; R.  Durga

doi:10.59400/cai1577

Advancements in nutty quality: Segmentation for enhanced monitoring and determination

P. Saranya Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, Chennai 600043, India
R. Durga Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, Chennai 600043, India

Article ID: 1577

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DOI: https://doi.org/10.59400/cai1577

Keywords: nut segmentation; image analysis; region growing; k-means clustering; cuckoo search algorithm (CSA); mean squared error (MSE); intersection over union (IoU); dice coefficient (DC); computer vision; agricultural applications

Abstract

Segmentation of nut images plays a vital role in computer vision and agricultural applications. Precise segmentation enables the extraction and analysis of essential information about the nuts, supporting quality evaluation, yield estimation, and automated sorting processes. This study explores nuts image segmentation utilizing the cuckoo search algorithm. The cuckoo search algorithm, a nature-inspired optimization technique, is introduced to enhance the segmentation process, potentially optimizing parameters or guiding the segmentation algorithms. Performance evaluation emphasizes metrics such as MSE, IoU, and dice coefficient. CSA (cuckoo search algorithm) demonstrates superior results, showcasing its effectiveness in automated nuts segmentation. This research contributes to the advancement of nut image analysis, providing insights into segmentation methodologies that can enhance automated processes in agriculture and food industry applications. The findings underscore the significance of employing advanced algorithms like CSA for accurate and efficient segmentation of nuts in images.

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Published

2024-12-18

How to Cite

Saranya, P., & Durga, R. (2024). Advancements in nutty quality: Segmentation for enhanced monitoring and determination. Computing and Artificial Intelligence, 3(1), 1577. https://doi.org/10.59400/cai1577

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