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.

本研究解决了哥斯达黎加海洋生态系统中每年提取的海贝容易无法返回的问题，缺乏起源识别技术。通过开发特定的卷积神经网络（CNN）进行海贝识别，并建立了包含约19000张图片的数据集，模型准确率超过85%。该系统不仅高效分类海贝，还具有异常检测机制，提高了分类的准确性，对生态恢复实践具有重要影响。

回归家园：机器学习在海贝分类和生态系统恢复中的应用