{"title":"Advanced agricultural supply chain management: integrating blockchain and young’s double-slit experiment for enhanced security","authors":"Esakki Muthu Santhanam, Kartheeban kamatchi","doi":"10.1007/s41870-024-02180-7","DOIUrl":null,"url":null,"abstract":"<p>In the agricultural supply and food, chain Ensuring product safety is important which includes monitoring the effective logistics management and advancements of agricultural products. An effective model that guarantees sufficient safety of the product is required because many issues have been raised regarding contamination risks and food safety. Thus, an efficacious model is introduced in this article referred to as Blockchain Based-Crossover Young’s double-slit (BC-CYD) algorithm, which enables securing agriculture-based data in supply chain management. The developed approach successfully executes the transactions in the traceability and tracking of products with high-level security for the agricultural supply chain. The developed method utilizes an authentication process in provenance tracking and product information storage. The developed BC-CYD method improves safety and efficiency by obtaining higher security, reliability, and integrity. Here, product transactions are stored in the blockchain ledger, thereby, the developed model offers high-level traceability and transparency in a capable manner in the supply chain management. The effectiveness of the proposed BC-CYD method is assayed, where evaluation parameters quantify the efficiency of the developed BC-CYD method. Based on the performance rates of Precision, ROC, accuracy, and Processing time, the developed BC-CYD method’s effectiveness is ascertained as higher. The suggested BC-CYD method yields a greater precision of 97.4% and its accuracy is 98.8% with lower processing time and training time.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"18 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s41870-024-02180-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In the agricultural supply and food, chain Ensuring product safety is important which includes monitoring the effective logistics management and advancements of agricultural products. An effective model that guarantees sufficient safety of the product is required because many issues have been raised regarding contamination risks and food safety. Thus, an efficacious model is introduced in this article referred to as Blockchain Based-Crossover Young’s double-slit (BC-CYD) algorithm, which enables securing agriculture-based data in supply chain management. The developed approach successfully executes the transactions in the traceability and tracking of products with high-level security for the agricultural supply chain. The developed method utilizes an authentication process in provenance tracking and product information storage. The developed BC-CYD method improves safety and efficiency by obtaining higher security, reliability, and integrity. Here, product transactions are stored in the blockchain ledger, thereby, the developed model offers high-level traceability and transparency in a capable manner in the supply chain management. The effectiveness of the proposed BC-CYD method is assayed, where evaluation parameters quantify the efficiency of the developed BC-CYD method. Based on the performance rates of Precision, ROC, accuracy, and Processing time, the developed BC-CYD method’s effectiveness is ascertained as higher. The suggested BC-CYD method yields a greater precision of 97.4% and its accuracy is 98.8% with lower processing time and training time.