AdPO-TECBTM: Triple Elliptic Curve Algorithm Enabled Deep Learning Model for Agricultural Supply Chain Management

Agriculture Supply Chain (ASC) is a vital component of developing economies, promoting rural economic growth, social progress, and environmental sustainability. However, the existing ASC Management Systems face several challenges including security concerns, high latency, and difficulty in integrating diverse systems. Therefore, this research proposes the Adaptive parrot optimization-enabled Triple Elliptic curve algorithm based Bi-directional Long Short Term Memory (AdPOTECBTM) framework, which integrates confidentiality enabled blockchain network with the deep learning model and offers superior performance. The proposed Triple Elliptic curve algorithm improves the data security and integrity of the data stored in the Interplanetary File System (IPFS). Specifically, the origin tractability performance of the proposed model is enhanced via the AdPO algorithm by effectively tuning the model's hyperparameters. The experimental results show that the AdPOTECBTM framework achieves superior performance in terms of metrics obtaining a high accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 4. 9 3 \%}$</tex>, and sensitivity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 4. 9 2 \%}$</tex> outperforming the other existing techniques. Besides, the proposed AdPO-TECBTM framework offers highly efficient performance obtaining the Gas transection ratio of 75.07 KB, Genuine user rate of 0.67, and an encryption time of 1.02 secs for 250 users compared to other existing techniques.