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In order to improve the utilization rate of agricultural big data and solve the security issues problem of multisource and heterogeneous agricultural big data, an improved agricultural big data ant colony optimization algorithm (BigDataACO) is proposed to complete the multisource agricultural big data information in the feature layer and decision-making, and the problem of multisource data fusion was solved. The swarm intelligence algorithm is a process of simulating the complex problem of populations in nature through the mutual cooperation between individuals. The algorithm has potential parallelism and strong robustness, and the algorithm does not depend on specific problems. The definition, principle, and implementation method of agricultural big data fusion problem are studied. Then, the insufficiency of big data fusion modeling algorithm is analyzed. Finally, the source and core steps of the ant colony big data fusion algorithm are studied. The experimental results show that the improved BigDataACO algorithm is verified by the measured data. Compared with K-means, D-S evidence theory, and Bayesian algorithm, the uncertainty of data fusion is greatly reduced by the improved algorithm proposed in this paper.

A Big Data Mining and Blockchain-Enabled Security Approach for Agricultural Based on Internet of Things