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<p>Livestock and agriculture are considered as a major part in dealing with the stability of social and economic policies. The safety of food and transparency of the food supply chain are the main constraints for several people. The alliance of blockchain and the Internet of Things (IoT) are acquiring huge focus because of their success in versatile applications. There exists huge data which are optimized by deep learning models. This paper presents an approach for food quality evaluation for dairy products with blockchain-driven IoT. The system model with Radio Frequency Identification (RFID) tags collects the dairy product data and provides blockchain data management. Here, the food traceability blockchain architecture contains entities, like supplier, manufacturer, verification, certification, distribution, and retailer, consumer, and blockchain nodes. Here, food traceability blockchain are developed based on the supplier total milk quantity. Here, a deep learning-based food quality evaluation module is considered as input data which is further pre-processed with min and max normalization. Subsequently, the feature weighting is executed with the Gannet Vulture optimization Algorithm (GVOA). The food quality is examined with Deep Maxout Network (DMN). The BC_GVOA + DMN achieved highest TPR of 93%, TNR of 92.6% and small validation time of 52.252&#xa0;s than the existing methods, such as, ADL, 3DP, Fuzzy ANP + VIKOR, and ML + Fuzzy.</p>

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Dairy product quality evaluation using optimization-based deep maxout network with blockchain-driven internet of things

  • 期刊论文

  • 发表日期：

    2025年3月15日

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  • Maheshwari Biradar,
  • Nandini Sidnal,
  • Rajeshree Rokade,
  • Bahubali Shiragapur

摘要

Livestock and agriculture are considered as a major part in dealing with the stability of social and economic policies. The safety of food and transparency of the food supply chain are the main constraints for several people. The alliance of blockchain and the Internet of Things (IoT) are acquiring huge focus because of their success in versatile applications. There exists huge data which are optimized by deep learning models. This paper presents an approach for food quality evaluation for dairy products with blockchain-driven IoT. The system model with Radio Frequency Identification (RFID) tags collects the dairy product data and provides blockchain data management. Here, the food traceability blockchain architecture contains entities, like supplier, manufacturer, verification, certification, distribution, and retailer, consumer, and blockchain nodes. Here, food traceability blockchain are developed based on the supplier total milk quantity. Here, a deep learning-based food quality evaluation module is considered as input data which is further pre-processed with min and max normalization. Subsequently, the feature weighting is executed with the Gannet Vulture optimization Algorithm (GVOA). The food quality is examined with Deep Maxout Network (DMN). The BC_GVOA + DMN achieved highest TPR of 93%, TNR of 92.6% and small validation time of 52.252 s than the existing methods, such as, ADL, 3DP, Fuzzy ANP + VIKOR, and ML + Fuzzy.