Abstract
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.
Highlights
Agricultural big data is a collection of data that has a wide range of sources, diverse types, complex structures, and potential value and is difficult to apply common methods of processing and analysis, after integrating its own characteristics such as regional, seasonal, diversity, and periodicity of agriculture [1, 2]
In order to continuously promote the optimization of the agricultural economy, to realize the sustainable industrial development and regional industrial structure optimization, and further promote the construction of smart agriculture, it is necessary to comprehensively and Wireless Communications and Mobile Computing timely grasp the development of agriculture, which needs to rely on agricultural big data and related big data fusion processing technology
An improved BigDataACO algorithm based on ACO is proposed, that is, taking agricultural large data fusion as the research object; this paper studies the construction and prediction methods of classification models for different data sets by using an improved ant colony algorithm
Summary
Agricultural big data is a collection of data that has a wide range of sources, diverse types, complex structures, and potential value and is difficult to apply common methods of processing and analysis, after integrating its own characteristics such as regional, seasonal, diversity, and periodicity of agriculture [1, 2]. In order to continuously promote the optimization of the agricultural economy, to realize the sustainable industrial development and regional industrial structure optimization, and further promote the construction of smart agriculture, it is necessary to comprehensively and Wireless Communications and Mobile Computing timely grasp the development of agriculture, which needs to rely on agricultural big data and related big data fusion processing technology. It faces enormous challenges for prediction accuracy in traditional big data modeling algorithms. The construction of classification learning model based on the swarm intelligence algorithm has become a research hotspot in the field of data mining in recent years [7, 8]
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