智慧农业（中英文）2025，Vol.7Issue(1)：57-69,13.DOI:10.12133/j.smartag.SA202411004
农产品市场监测预警深度学习智能预测方法
Agricultural Market Monitoring and Early Warning:An Integrated Forecasting Approach Based on Deep Learning
摘要
Abstract
[Significance]The fluctuations in the supply,consumption,and prices of agricultural products directly affect market monitoring and early warning systems.With the ongoing transformation of China's agricultural production methods and market system,advancements in data acquisition technologies have led to an explosive growth in agricultural data.However,the complexity of the data,the narrow applicability of existing models,and their limited adaptability still present significant challenges in monitoring and forecasting the in-terlinked dynamics of multiple agricultural products.The efficient and accurate forecasting of agricultural market trends is critical for timely policy interventions and disaster management,particularly in a country with a rapidly changing agricultural landscape like Chi-na.Consequently,there is a pressing need to develop deep learning models that are tailored to the unique characteristics of Chinese ag-ricultural data.These models should enhance the monitoring and early warning capabilities of agricultural markets,thus enabling pre-cise decision-making and effective emergency responses. [Methods]An integrated forecasting methodology was proposed based on deep learning techniques,leveraging multi-dimensional agri-cultural data resources from China.The research introduced several models tailored to different aspects of agricultural market forecast-ing.For production prediction,a generative adversarial network and residual network collaborative model(GAN-ResNet)was em-ployed.For consumption forecasting,a variational autoencoder and ridge regression(VAE-Ridge)model was used,while price predic-tion was handled by an Adaptive-Transformer model.A key feature of the study was the adoption of an"offline computing and visual-ization separation"strategy within the Chinese agricultural monitoring and early warning system(CAMES).This strategy ensures that model training and inference are performed offline,with the results transmitted to the front-end system for visualization using light-weight tools such as ECharts.This approach balances computational complexity with the need for real-time early warnings,allowing for more efficient resource allocation and faster response times.The corn,tomato,and live pig market data used in this study covered production,consumption and price data from 1980 to 2023,providing comprehensive data support for model training. [Results and Discussions]The deep learning models proposed in this study significantly enhanced the forecasting accuracy for various agricultural products.For instance,the GAN-ResNet model,when used to predict maize yield at the county level,achieved a mean ab-solute percentage error(MAPE)of 6.58%.The VAE-Ridge model,applied to pig consumption forecasting,achieved a MAPE of 6.28%,while the Adaptive-Transformer model,used for tomato price prediction,results in a MAPE of 2.25%.These results highlight-ed the effectiveness of deep learning models in handling complex,nonlinear relationships inherent in agricultural data.Additionally,the models demonstrate notable robustness and adaptability when confronted with challenges such as sparse data,seasonal market fluctuations,and heterogeneous data sources.The GAN-ResNet model excels in capturing the nonlinear fluctuations in production da-ta,particularly in response to external factors such as climate conditions.Its capacity to integrate data from diverse sources—includ-ing weather data and historical yield data—made it highly effective for production forecasting,especially in regions with varying cli-matic conditions.The VAE-Ridge model addressed the issue of data sparsity,particularly in the context of consumption data,and pro-vided valuable insights into the underlying relationships between market demand,macroeconomic factors,and seasonal fluctuations.Finally,the Adaptive-Transformer model stand out in price prediction,with its ability to capture both short-term price fluctuations and long-term price trends,even under extreme market conditions. [Conclusions]This study presents a comprehensive deep learning-based forecasting approach for agricultural market monitoring and early warning.The integration of multiple models for production,consumption,and price prediction provides a systematic,effective,and scalable tool for supporting agricultural decision-making.The proposed models demonstrate excellent performance in handling the nonlinearities and seasonal fluctuations characteristic of agricultural markets.Furthermore,the models'ability to process and inte-grate heterogeneous data sources enhances their predictive power and makes them highly suitable for application in real-world agricul-tural monitoring systems.Future research will focus on optimizing model parameters,enhancing model adaptability,and expanding the system to incorporate additional agricultural products and more complex market conditions.These improvements will help in-crease the stability and practical applicability of the system,thus further enhancing its potential for real-time market monitoring and early warning capabilities.关键词
监测预警/深度学习/生产量预测/消费量预测/价格预测/生成对抗与残差网络协同生产量模型/变分自编码器岭回归消费预测模型/自适应变换器价格预测模型Key words
monitoring and early warning/deep learning/production forecasting/consumption forecasting/price forecasting/GAN-ResNet/VAE-Ridge/Adaptive-Transformer分类
信息技术与安全科学引用本文复制引用
许世卫,李乾川,栾汝朋,庄家煜,刘佳佳,熊露..农产品市场监测预警深度学习智能预测方法[J].智慧农业（中英文）,2025,7(1):57-69,13.基金项目
"十四五"国家重点研发计划项目(2022YFD1600603) （2022YFD1600603）
农业农村部农业监测预警技术重点实验室开放课题基金(KLAM-EWT202403) The"14th Five-Year Plan"National Key R&D Program(2022YFD1600603) （KLAM-EWT202403）
Key Laboratory of Agricultural Moni-toring and Early-Warning Technology,Ministry of Agriculture and Rural Affairs,Open Project Fund(KLAMEWT202403) （KLAMEWT202403）
