IoT and ML-based automatic irrigation system for smart agriculture system

被引:5
作者
Anoop, E. G. [1 ,2 ]
Bala, G. Josemin [1 ]
机构
[1] Karunya Inst Technol & Sci, Dept ECE, Coimbatore, India
[2] Fed Inst Sci & Technol FISAT, Dept ECE, Angamaly, Kerala, India
关键词
PRECISION AGRICULTURE;
D O I
10.1002/agj2.21344
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
The development of the Internet of Things (IoT) and machine learning (ML) technologies has triggered smart agricultural systems. In smart agriculture, irrigation management plays a major role to reduce water waste. The monitoring settings, hardware modules, communication technology, and storage systems used in smart irrigation systems were analyzed to determine the optimal nature of water flow. This assessment aims to give an overview of the current state of the irrigation system by taking into account weather and soil moisture. This paper provides a comprehensive review of the utilization of various hardware modules in smart irrigation systems. Moreover, various communication technologies that aid in data transfer for efficient smart irrigation are reviewed. The ML method used for prediction is also evaluated, as well as based storage technologies like the cloud and databases used to store data for predictive irrigation systems. As a result, the paper provides an overview of all the factors that contribute to irrigation systems' smart operation as well as potential future paths for improving agricultural systems through IoT.
引用
收藏
页码:1187 / 1203
页数:17
相关论文
共 60 条
[1]  
Abayomi-Alli O., 2018, INT C APPL INF
[2]  
Abhinaya E. V., 2021, ANN ROMANIAN SOC CEL, V25, P2836
[3]   Precision Irrigation Management Using Machine Learning and Digital Farming Solutions [J].
Abioye, Emmanuel Abiodun ;
Hensel, Oliver ;
Esau, Travis J. ;
Elijah, Olakunle ;
Abidin, Mohamad Shukri Zainal ;
Ayobami, Ajibade Sylvester ;
Yerima, Omosun ;
Nasirahmadi, Abozar .
AGRIENGINEERING, 2022, 4 (01) :70-103
[4]   Smart irrigation system for environmental sustainability in Africa: An Internet of Everything (IoE) approach [J].
Adenugba, Favour ;
Misra, Sanjay ;
Maskeliunas, Rytis ;
Damasevicius, Robertas ;
Kazanavicius, Egidijus .
MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2019, 16 (05) :5490-5503
[5]  
Akshay S., 2020, 2020 INT C COMMUNICA, P867, DOI [10.1109/ICCSP48568.2020.9182215, DOI 10.1109/ICCSP48568.2020.9182215]
[6]  
Aminuddin R., 2021, J PHYS C SERIES, V2129
[7]   IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms [J].
Bakthavatchalam, Kalaiselvi ;
Karthik, Balaguru ;
Thiruvengadam, Vijayan ;
Muthal, Sriram ;
Jose, Deepa ;
Kotecha, Ketan ;
Varadarajan, Vijayakumar .
TECHNOLOGIES, 2022, 10 (01)
[8]  
Chikankar P.B., 2015, 2015 INT C PERV COMP, P1, DOI [DOI 10.1109/PERVA-SIVE.2015.7086997, DOI 10.1109/PERVASIVE.2015.7086997]
[9]   Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review [J].
Chlingaryan, Anna ;
Sukkarieh, Salah ;
Whelan, Brett .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2018, 151 :61-69
[10]  
Darshna S., 2015, IOSR J. Electron. Commun. Eng., V10, P32, DOI https://doi.org/10.9790/2834-10323236