Recognition of Spice Types Using Web-Based Convolutional Neural Network. Methods Collected and processed a dataset of 6,050 images covering turmeric, sand ginger, curcuma, galangal, ginger, pepper, coriander, cumin, anise, star anise, secang wood, candle nut, nutmeg Achieved 97% accuracy and loss 0.0682
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This project develops a web-based convolutional neural network (CNN) to accurately identify 13 different types of spices from a dataset of 6,050 images, achieving 97% accuracy. It is designed for developers and researchers looking to deploy effective image classification models directly in a web browser for practical applications like spice recognition.