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What is Predicting GlutoPeak Parameters with CNN about?

This study presents a machine learning methodology using AutoML and transfer learning to predict GlutoPeak test parameters from image data, aimed at improving gluten quality assessment in wheat. The research utilized convolutional neural networks (CNN) with AutoKeras, achieving the highest accuracy of 0.5765 for 2-class predictions and 0.4362 for 4-class predictions. This approach offers a faster and more cost-effective alternative for evaluating gluten properties in the baking industry.

Author

Vamsi Bandi

Language

EN