Agricultural Product Price Prediction Technology Based on Backpropagation Neural Network and Support Vector Machine Fusion Model
In the rapid development of agricultural technology today,the types and planning methods of agricultural product planting are becoming increasingly diverse.Different agricultural product planning can bring different crop plant-ing benefits.In order to improve the quality of agricultural product planting planning,a fusion model-based method has been proposed.During the process,a backpropagation neural network with a three-layer structure is established,and particle swarm optimization algorithm is used to gradually approximate the data for optimization.Support vector machine regression technology is used to predict short-term agricultural product prices.The experimental results show that the calculation time for predicting vegetables using the research method is 153ms when the input data is 200 pieces;In the prediction of product unit price,the research method maintains an error of within 0.003 yuan per kilogram when predic-ting fruits.The research method can effectively predict the unit price of agricultural products and has good efficiency.
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