Deep Learning based Robust Food Supply Chain Enabled Effective Management with Blockchain
Article Sidebar
Main Article Content
Abstract
Agriculture supply chain plays a predominant role in everyday life. The safety of the products is more significantly considered while the farmers targeted in the improvising of their profit. Moreover, the productions of agri products are dynamic and tracing the production and distribution are challenging and time-consuming process. In concern with these, we propose a blockchain based secured framework for tracing and managing the supply of pulses incorporated with the storage capacity of factories. The cost of production, transportation, penalty, and storage are analyzed. For the enhancement of profitability, and managing the production to improve the storage AlexNet framework is proposed. This effectively classifies the need of the retailers and aids in supplying the products. Simulation is conducted and analyzed the robustness of the proposed work with the comparison of state-of-art works. The outcomes show that the profitability and production increases with the proposed work.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Wang, L.; Xu, L.; Zheng, Z.; Liu, S.; Li, X.; Cao, L.; Li, J.; Sun, C. Smart contract-based agricultural food supply chain traceability. IEEE Access 2021, Volume 9, pp. 9296-9307.
Dong, Y.; Fu, Z.; Stankovski, S.; Wang, S.; Li, X. Nutritional quality and safety traceability system for China’s leafy vegetable supply chain based on fault tree analysis and QR code. IEEE Access 2020, Volume 8, pp. 161261-161275.
Zhao, G.; Liu, S.; Lopez, C.; Lu, H.; Elgueta, S.; Chen, H.; Boshkoska, B.M. Blockchain technology in agri-food value chain management: A synthesis of applications, challenges and future research directions. Computers in industry 2019, Volume 109, pp. 83-99.
Putro, P.A.W.; Purwaningsih, E.K.; Sensuse, D.I.; Suryono, R.R. Model and implementation of rice supply chain management: A literature review. Procedia Computer Science 2022, Volume 197, pp. 453-460.
Bhutta, M.N.M.; Ahmad, M. Secure identification, traceability and real-time tracking of agricultural food supply during transportation using Internet of things. IEEE Access 2021, Volume 9, pp. 65660-65675.
Zawish, M.; Ashraf, N.; Ansari, R.I.; Davy, S.; Qureshi, H.K.; Aslam, N.; Hassan, S.A. Toward On-Device AI and Blockchain for 6G-Enabled Agricultural Supply Chain Management. IEEE Internet of Things Magazine 2022, Volume 5, No 2, pp. 160-166.
Hassija, V.; Batra, S.; Chamola, V.; Anand, T.; Goyal, P.; Goyal, N.; Guizani, M. A blockchain and deep neural networks-based secure framework for enhanced crop protection. Ad Hoc Networks 2021, Volume 119, pp. 102537.
Chen, H.; Chen, Z.; Lin, F.; Zhuang, P. Effective management for blockchain-based agri-food supply chains using deep reinforcement learning. IEEE Access 2021, Volume 9, pp. 36008-36018.
Dayana, D.S.; Kalpana, G.; Vigneswaran, T. Implementation of circular blockchain-based approach for food crops supply chain with bitcoin prediction using deep learning. Soft Computing 2023, pp. 1-14.
Wang, Y.; Lv, Y.; Guo, D.; Zhang, S.; Jiao, S. A novel multi-input alexnet prediction model for oil and gas production. Mathematical Problems in Engineering 2018, pp.1-9.
Chen, H.; Chen, Z.; Lin, F.; Zhuang, P. Effective management for blockchain-based agri-food supply chains using deep reinforcement learning. Ieee Access 2021, Volume 9, pp. 36008-36018.