In Sri Lanka, sea cucumbers are a highly sought-after marine product that is traded abroad. It has gained significant attention in recent years due to its high demand in the seafood market and potential medicinal properties. For the seafood industry and researchers, it is still difficult to analyze sea cucumbers accurately and effectively. This study offers an innovative solution which is of high economic value “Sea Cense” that automates the evaluation and classification of sea cucumbers utilizing advanced computer vision and machine learning approaches. The proposed analyzer incorporates a multi-step approach, including image preprocessing, feature extraction, and classification. A set of preprocessed images are used to extract morphological, textural, and color-based distinguishing characteristics. These characteristics are then used to accurately classify sea cucumber species and grade them according to quality criteria using a machine learning algorithm. This project introduces a novel approach to analyze sea cucumber length to calculate the price using a mobile application, making the task more efficient and accessible. The proposed solution “Sea Cense” comprises of 2 models that utilizes CNN algorithm.
In Sri Lanka, sea cucumbers are a highly sought-after marine product that is traded abroad. It has gained significant attention in recent years due to its high demand in the seafood market and potential medicinal properties. For the seafood industry and researchers, it is still difficult to analyze sea cucumbers accurately and effectively. This study offers an innovative solution which is of high economic value “Sea Cense” that automates the evaluation and classification of Sea cucumbers utilizing advanced computer vision and machine learning approaches. The proposed analyzer incorporates a multi-step approach, including image preprocessing, feature extraction, and classification. A set of preprocessed images are used to extract morphological, textural, and color-based distinguishing characteristics. These characteristics are then used to accurately classify sea cucumber species and grade them according to quality criteria using a machine learning algorithm. This project introduces a novel approach to analyze sea cucumber length to calculate the price using a mobile application, making the task more efficient and accessible. The proposed solution “Sea Cense” comprises of 2 models that utilizes CNN algorithm.
