A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
Gedi Liu , Keyang Zhong , Huilin Li , Tao Chen , Yang Wang
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引用次数: 0

Abstract

Agricultural greenhouse production has to require a stable and acceptable environment, it is therefore essential for future greenhouse production to obtain full and precisely internal dynamic environment parameters. Dynamic modeling based on machine learning methods, e.g., intelligent time series prediction modeling, is a popular and suitable way to solve the above issue. In this article, a systematic literature review on applying advanced time series models has been systematically conducted via a detailed analysis and evaluation of 61 pieces selected from 221 articles. The historical process of time series model application from the use of data and information strategies was first discussed. Subsequently, the accuracy and generalization of the model from the selection of model parameters and time steps, providing a new perspective for model development in this field, were compared and analyzed. Finally, the systematic review results demonstrate that, compared with traditional models, deep neural networks could increase data structure mining capabilities and overall information simulation capabilities through innovative and effective structures, thereby it could also broaden the selection range of environmental parameters for agricultural facilities and achieve environmental prediction end-to-end optimization via intelligent time series model based on deep neural networks.

农业温室环境参数的机器学习时间序列预测研究进展
农业温室大棚生产需要一个稳定且可接受的环境,因此,全面、精确地获取内部动态环境参数对未来的温室大棚生产至关重要。基于机器学习方法的动态建模,如智能时间序列预测建模,是解决上述问题的常用且合适的方法。本文通过对从 221 篇文章中选取的 61 篇文章进行详细分析和评价,对先进时间序列模型的应用进行了系统的文献综述。首先从数据使用和信息策略两个方面探讨了时间序列模型应用的历史进程。随后,从模型参数和时间步长的选择出发,对模型的准确性和普适性进行了比较和分析,为该领域的模型开发提供了新的视角。最后,系统综述结果表明,与传统模型相比,深度神经网络可以通过创新有效的结构提高数据结构挖掘能力和整体信息模拟能力,从而也可以拓宽农业设施环境参数的选择范围,并通过基于深度神经网络的智能时间序列模型实现环境预测的端到端优化。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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