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In real-time monitoring systems, strawberry freshness is a crucial metric for guaranteeing the quality of the product. This work investigates the application of transfer learning methods to effectively assess the freshness of strawberries, offering a less expensive option for creating deep neural networks (DNNs) from the ground up. We implemented multiple cutting-edge transfer learning models in a suggested real-time system architecture using a brand-new strawberry dataset. The Xception model outperformed the others in terms of accuracy, precision, recall, and F1-score, indicating its promise for a reliable and effective assessment of strawberry freshness. According to the research, transfer learning models provide a workable method for determining the freshness of fruit, saving time and money in real-time applications.

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Transfer Learning Techniques for Efficient Deep Neural Networks in Real-Time Strawberry Freshness Evaluation

  • 会议论文

  • 发表日期：

    2025年

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  • Alaya Parven Alo,
  • Rita Faria Richi,
  • S. M. Shaqib,
  • Kazi Rezwana Alam,
  • Sharun Akter Khushbu,
  • Md. Sadekur Rahman

摘要

In real-time monitoring systems, strawberry freshness is a crucial metric for guaranteeing the quality of the product. This work investigates the application of transfer learning methods to effectively assess the freshness of strawberries, offering a less expensive option for creating deep neural networks (DNNs) from the ground up. We implemented multiple cutting-edge transfer learning models in a suggested real-time system architecture using a brand-new strawberry dataset. The Xception model outperformed the others in terms of accuracy, precision, recall, and F1-score, indicating its promise for a reliable and effective assessment of strawberry freshness. According to the research, transfer learning models provide a workable method for determining the freshness of fruit, saving time and money in real-time applications.