Enhancing The Accuracy of Block chain Based Agriculture Product Supply Chain Management System Using Random Forest Algorithm and Decision Tree Algorithm

The purpose of this work is to improve the Blockchain-based Agriculture Product Supply Chain Management System, by using the Decision tree Algorithm[DTA] and the Random Forest Algorithm[RFA]. To start improving the accuracy of a blockchain-based system for managing the supply chain of agricultural products, DTA and RFA with differential training and testing splits are incorporated. There are 20 samples total, 10 for each algorithm. (parameters: α= 0.05 and power= 0.80) There is a significant difference between theRFA , that has an accuracy of 77.0 %, and the DTA, which has an accuracy of 92.0 %, according to our findings. Statistically significant value of p=0.000, (p<0.05), which indicates that the mean, standard deviation, and standard error are all consequential. In this paper, based on the results that have obtained, that DTA (92.0%) has more accuracy than the RFA(77.0%).