Agricultural industrial clusters are the basis for the formation of regional brands of agricultural products and also an effective way to enhance the regional brand value and competitiveness of agricultural products. Based on the agricultural industry cluster, carry out regional brand building of agricultural products, emphasizing that the production of agricultural products from raw material bases to product sales in specific administrative areas, and even the collaborative production services of relevant agricultural technology research and development institutions are connected to the local network, establishing a regional first-class agricultural product brand. The brand grows with the development of regional production bases. Therefore, this paper is aimed at exploring the research on the promotion of green brand competitiveness of agricultural products based on the theory of agricultural industrial clusters, analyzing the internal connection between agricultural product agglomeration effect and brand competitiveness, and exploring its advantages and disadvantages. Using this strategy, this paper will use the research method of specific analysis of specific problems, design an analysis model construction experiment based on the influencing factors of agricultural industrial clusters, and draw experimental conclusions through the analysis and comparison of experimental data. Through theoretical innovation and exploration, a model for the rapid development of green agricultural product brands can be found. The results show that the use of questionnaires to explore the differences in the planting characteristics of green agricultural products in industrial clusters can account for 35% of the brand competitiveness, promote the rapid development of the brand, and enhance the technological innovation ability by 19%. Therefore, combined with the characteristics of the current era, with the help of the national agricultural aid policy, fully absorb the transformation, actively innovate, and improve the level of agricultural cultivation. Analyze different application difficulties, explore development prospects, give full play to the competitiveness of agricultural products, realize high-yield green crops, use these theories as a guide to innovate and integrate, provide valuable experience clusters for the wide application of agricultural industrialization, and achieve sustainable regional economic structure and ecological environment.
With the rapid development of information technology, decision support systems that can assist business managers in making scientific decisions have become the focus of research. At present, there are not many related studies, but from the brand marketing level, there are not many studies combining smart technology. Based on computer vision technology and parallel computing algorithms, this paper launches an in-depth study of brand marketing decision support systems. First, use computer vision technology and Viola-Jones face detection framework to detect consumers’ faces, and use the classic convolutional neural network model AlexNet for gender judgment and age prediction to analyze consumer groups. Then, use parallel computing to optimize the genetic algorithm to improve the running speed of the algorithm. Design the brand marketing decision support system based on the above technology and algorithm, analyze the relevant data of the L brand, and divide the functional structure of the system into three parts: customer market analysis, performance evaluation, and demand forecasting. The ROC curve of the Viola-Jones face detection framework shows its superior performance. After 500 iterations of the AlexNet model, the verification set loss of the network is stable at 1.8, and the accuracy of the verification set is stable at 38%. Parallel genetic algorithms run 1.8 times faster than serial genetic algorithms at the lowest and 9 times faster at the highest. The minimum prediction error is 0.17%, and the maximum is 2%, which shows that the system can make accurate predictions based on previous years’ data. Computer vision is a technique that converts still image or video data into a decision or a new representation. All such transformations are done to accomplish a specific purpose. Therefore, a brand marketing decision support system based on computer vision and parallel computing can help managers make scientific decisions, save production costs, reduce inventory pressure, and enhance the brand’s competitive advantage.
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