Frost Monitoring Cyber-Physical System: A Survey on Prediction and Active Protection Methods

被引:18
作者
Zhou, Ian [1 ]
Lipman, Justin [1 ]
Abolhasan, Mehran [1 ]
Shariati, Negin [1 ]
Lamb, David W. [2 ]
机构
[1] Univ Technol Sydney, Sch Elect & Data Engn, Ultimo, NSW 2007, Australia
[2] Univ New England, Sch Sci & Technol, Armidale, NSW 2350, Australia
关键词
Machine learning; Agriculture; Intelligent sensors; Actuators; Protocols; Internet of Things; Cyber-physical systems (CPSs); frost prediction; frost protection; machine learning; ARTIFICIAL NEURAL-NETWORKS; WIRELESS SENSOR NETWORKS; LONG-WAVE-RADIATION; INTERNET; THINGS; TEMPERATURE; DAMAGE; FORECAST; ORCHARDS; MODELS;
D O I
10.1109/JIOT.2020.2972936
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Frost damage in broadacre cropping and horticulture (including viticulture) results in substantial economic losses to producers and may also disrupt associated product value chains. Frost risk windows are changing in timing, frequency, and duration. Faced with the increasing cost of mitigation infrastructure and competition for resources (e.g., water and energy), multiperil insurance, and the need for supply chain certainty, producers are under pressure to innovate in order to manage and mitigate risk. Frost protection systems are cyber-physical systems (CPSs) consisting of sensors (event detection), intelligence (prediction), and actuators (active protection methods). The Internet-of-Things communication protocols joining the CPS components are also evaluated. In this context, this article introduces and reviews existing methods of frost management. This article focuses on active protection methods because of their potential for real-time deployment during frost events. For integrated frost prediction and active protection systems, prediction method, sensor types, and integration architecture are assessed, research gaps are identified and future research directions proposed.
引用
收藏
页码:6514 / 6527
页数:14
相关论文
共 95 条
  • [1] Wireless sensor networks: a survey
    Akyildiz, IF
    Su, W
    Sankarasubramaniam, Y
    Cayirci, E
    [J]. COMPUTER NETWORKS, 2002, 38 (04) : 393 - 422
  • [2] Al-Sarawi S., 2017, 2017 8 INT C INF TEC, P685, DOI DOI 10.1109/ICITECH.2017.8079928
  • [3] Alboon Shadi A., 2012, International Journal of Artificial Intelligence and Soft Computing, V3, P165, DOI 10.1504/IJAISC.2012.049023
  • [4] Direct and Indirect Costs of Frost in the Australian Wheatbelt
    An-Vo, Duc-Anh
    Mushtaq, Shahbaz
    Zheng, Bangyou
    Christopher, Jack T.
    Chapman, Scott C.
    Chenu, Karine
    [J]. ECOLOGICAL ECONOMICS, 2018, 150 : 122 - 136
  • [5] Micrometeorological test of microsprinklers for frost protection of fruit orchards in Northern Italy
    Anconelli, S
    Facini, O
    Marletto, V
    Pitacco, A
    Rossi, F
    Zinoni, F
    [J]. PHYSICS AND CHEMISTRY OF THE EARTH, 2002, 27 (23-24) : 1103 - 1107
  • [6] IoT-based System to Forecast Crop Frost
    Angel Guillen-Navarro, M.
    Pereniguez-Garcia, Fernando
    Martinez-Espana, Raquel
    [J]. 2017 13TH INTERNATIONAL CONFERENCE ON INTELLIGENT ENVIRONMENTS (IE 2017), 2017, : 28 - 35
  • [7] Angstrom AndersKnutsson., 1915, STUDY RAD ATMOSPHERE, V65
  • [8] Reconstructing patterns of temperature, phenology, and frost damage over 124 years: Spring damage risk is increasing
    Augspurger, Carol K.
    [J]. ECOLOGY, 2013, 94 (01) : 41 - 50
  • [9] Australian Bureau of Statistics, 2018, AGR COMM AUSTR 2015
  • [10] VALUE OF FROST FORECASTING - BAYESIAN APPRAISAL
    BAQUET, AE
    HALTER, AN
    CONKLIN, FS
    [J]. AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS, 1976, 58 (03) : 511 - 520