Smart Agriculture ›› 2023, Vol. 5 ›› Issue (1): 82-98.doi: 10.12133/j.smartag.SA202211004
收稿日期:
2022-11-11
出版日期:
2023-03-30
基金项目:
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.cnReceived:
2022-11-11
Online:
2023-03-30
Foundation items:
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)About author:
GUI Zechun, E-mail:guizechun2022@163.com
Corresponding author:
ZHAO Sijian, E-mail:zhaosijian@caas.cn
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。
中图分类号:
桂泽春, 赵思健. 人工智能在农业风险管理中的应用研究综述[J]. 智慧农业(中英文), 2023, 5(1): 82-98.
GUI Zechun, ZHAO Sijian. Research Application of Artificial Intelligence in Agricultural Risk Management: A Review[J]. Smart Agriculture, 2023, 5(1): 82-98.
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