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Smart Agriculture ›› 2023, Vol. 5 ›› Issue (1): 82-98.doi: 10.12133/j.smartag.SA202211004

• 综合研究 • 上一篇    下一篇

人工智能在农业风险管理中的应用研究综述

桂泽春(), 赵思健()   

  1. 中国农业科学院农业信息研究所,北京 100081
  • 收稿日期:2022-11-11 出版日期:2023-03-30
  • 基金资助:
    中国农业科学院农业信息研究所科技创新工程项目(CAAS-ASTIP-2016-AII);教育部人文社会科学重点研究基地重大项目(17JJD910002);国家自然科学基金面上项目(41471426)
  • 作者简介:桂泽春,硕士研究生,研究方向为农业风险管理。E-mail:guizechun2022@163.com
  • 通信作者: 赵思健,博士,研究员,研究方向为农业风险管理及保险。E-mail:zhaosijian@caas.cn

Research Application of Artificial Intelligence in Agricultural Risk Management: A Review

GUI Zechun(), ZHAO Sijian()   

  1. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • Received:2022-11-11 Online:2023-03-30
  • corresponding author: ZHAO Sijian, E-mail:zhaosijian@caas.cn
  • About author:GUI Zechun, E-mail:guizechun2022@163.com
  • Supported by:
    The Science and Technology Innovation Engineering Project of the Institute of Agricultural Information, Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2016-AII); Major Project of the Key Research Base for Humanities and Social Sciences of the Ministry of Education (17JJD910002); National Natural Science Foundation General Project (41471426)

摘要:

农业是关系国计民生的基础产业,但同时又是弱质产业,传统农业风险管理研究方法中存在非线性信息挖掘不足、精确度不高和鲁棒性差等问题。人工智能(Artificial Intelligence,AI)拥有基于大数据的强非线性拟合、端到端建模和特征自学习等强大功能可很好地解决上述问题。本文首先分析了AI在农业脆弱性评估、农业风险预测,以及农业损害评估三大方面的研究进展,得出如下结论:1. AI在农业脆弱性评估中的特征重要性评估缺乏科学有效的验证指标,且应用方式导致无法比较多个模型之间的优劣,建议采用主客观法进行评价;2. 在风险预测中,发现随着预测时间的增加,机器学习模型的预测能力往往会下降,过拟合问题是风险预测中的常见问题,且目前研究针对图数据空间信息的挖掘还较少;3. 农业生产环境复杂,应用场景多变是影响损害评估准确性的重要因素,提升深度学习模型的特征提取能力和鲁棒性是未来技术发展需要克服的重点和难点问题。然后,针对AI应用过程中存在的性能提升问题和小样本问题提出了相应的解决方案。对于性能提升问题,根据使用者对人工智能的熟悉程度,可分别采用多种模型比较法、模型组合法和神经网络结构优化法以提升模型的性能表现;对于小样本的问题,往往可以将数据增强、生成对抗网络和迁移学习相结合,以增强模型的鲁棒性和提高模型识别的准确性。最后,对AI在农业风险管理中的应用进行了展望。未来可以考虑将人工智能引入农业脆弱性曲线的构建;针对农业产业链的上下游关系和与农业相关的行业关系,更多地应用图神经网络对农业价格风险预测进一步深入研究;损害评估建模过程中可以更多地引入评估目标相关领域的专业知识以增强对目标的特征学习,对小样本数据进行增广也是未来研究的重点内容。

关键词: 农业风险管理, 人工智能, 脆弱性评估, 风险预测, 损害评估

Abstract:

Agriculture is a basic industry deeply related to the national economy and people's livelihood, while it is also a weak industry. There are some problems with traditional agricultural risk management research methods, such as insufficient mining of nonlinear information, low accuracy and poor robustness. Artificial intelligence(AI) has powerful functions such as strong nonlinear fitting, end-to-end modeling, feature self-learning based on big data, which can solve the above problems well. The research progress of artificial intelligence technology in agricultural vulnerability assessment, agricultural risk prediction and agricultural damage assessment were first analyzed in this paper, and the following conclusions were obtained: 1. The feature importance assessment of AI in agricultural vulnerability assessment lacks scientific and effective verification indicators, and the application method makes it impossible to compare the advantages and disadvantages of multiple AI models. Therefore, it is suggested to use subjective and objective methods for evaluation; 2. In risk prediction, it is found that with the increase of prediction time, the prediction ability of machine learning model tends to decline. Overfitting is a common problem in risk prediction, and there are few researches on the mining of spatial information of graph data; 3. Complex agricultural production environment and varied application scenarios are important factors affecting the accuracy of damage assessment. Improving the feature extraction ability and robustness of deep learning models is a key and difficult issue to be overcome in future technological development. Then, in view of the performance improvement problem and small sample problem existing in the application process of AI technology, corresponding solutions were put forward. For the performance improvement problem, according to the user's familiarity with artificial intelligence, a variety of model comparison method, model group method and neural network structure optimization method can be used respectively to improve the performance of the model; For the problem of small samples, data augmentation, GAN (Generative Adversarial Network) and transfer learning can often be combined to increase the amount of input data of the model, enhance the robustness of the model, accelerate the training speed of the model and improve the accuracy of model recognition. Finally, the applications of AI in agricultural risk management were prospected: In the future, AI algorithm could be considered in the construction of agricultural vulnerability curve; In view of the relationship between upstream and downstream of agricultural industry chain and agriculture-related industries, the graph neural network can be used more in the future to further study the agricultural price risk prediction; In the modeling process of future damage assessment, more professional knowledge related to the assessment target can be introduced to enhance the feature learning of the target, and expanding the small sample data is also the key subject of future research.

Key words: agricultural risk management, artificial intelligence, vulnerability assessment, risk prediction, damage assessment

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