Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration

• URL: http://arxiv.org/abs/2501.04873v2
• Date: Thu, 06 Mar 2025 17:35:19 GMT
• Title: Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration
• Authors: Alexander Valverde, Luis Solano,
• Abstract summary: In Costa Rica, an average of 5 tons of seashells are extracted from ecosystems annually. Confiscated seashells, cannot be returned to their ecosystems due to the lack of origin recognition.<n>We developed a convolutional neural network (CNN) specifically for seashell identification.<n>We built a dataset from scratch, consisting of approximately 19000 images from the Pacific and Caribbean coasts.<n>The model has been integrated into a user-friendly application, which has classified over 36,000 seashells to date, delivering real-time results within 3 seconds per image.
• Score: 49.1574468325115
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In Costa Rica, an average of 5 tons of seashells are extracted from ecosystems annually. Confiscated seashells, cannot be returned to their ecosystems due to the lack of origin recognition. To address this issue, we developed a convolutional neural network (CNN) specifically for seashell identification. We built a dataset from scratch, consisting of approximately 19000 images from the Pacific and Caribbean coasts. Using this dataset, the model achieved a classification accuracy exceeding 85%. The model has been integrated into a user-friendly application, which has classified over 36,000 seashells to date, delivering real-time results within 3 seconds per image. To further enhance the system's accuracy, an anomaly detection mechanism was incorporated to filter out irrelevant or anomalous inputs, ensuring only valid seashell images are processed.

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