IoT-digital twin-inspired smart irrigation approach for optimal water utilization

被引:20
作者
Manocha, Ankush [1 ,2 ]
Sood, Sandeep Kumar [1 ]
Bhatia, Munish [1 ]
机构
[1] Natl Inst Technol, Kurukshetra 136119, Haryana, India
[2] Lovely Profess Univ, Jalandhar 144001, Punjab, India
关键词
Digital twin; Internet of Things; Smart irrigation; Machine learning; Water conservation; AGRICULTURE; SUPPORT; CLOUD;
D O I
10.1016/j.suscom.2023.100947
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Agriculture industry faces the challenge of increasing productivity by 50% from 2012 to 2050 while reducing water usage, given that it currently consumes 69% of the world's freshwater. To achieve this goal, smart technologies such as Artificial Intelligence (AI), Digital Twins (DT), and Internet of Things (IoT) are being increasingly utilized. However, the use of DT in agriculture is still in its early stages. This study proposes a smart irrigation framework inspired by digital twins in an application domain. The irrigation framework's sensors and actuators are linked to their virtual counterparts to create a digital twin. The IoT platform collects, aggregates, and processes data to determine daily irrigation requirements, and the behavior of the irrigation system is simulated. The proposed framework has two main advantages: evaluating the behavior of the digital twin and IoT platform in the context of agriculture before integrating them into the field and comparing various irrigation methods with current farming methods. By providing farmers with information about soil, weather, and crops, the system has the potential to improve farm operations and reduce water consumption.
引用
收藏
页数:11
相关论文
共 34 条
[1]  
Allen R. G., 1998, FAO Irrigation and Drainage Paper
[2]  
[Anonymous], 2017, API Reference
[3]  
Burapattanasiri Bancha, 2023, 2023 9th International Conference on Engineering, Applied Sciences, and Technology (ICEAST), P29, DOI 10.1109/ICEAST58324.2023.10157426
[4]   Smart cities in the 21st century [J].
Chang, Victor ;
Sharma, Sugam ;
Li, Chung-Sheng .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2020, 153
[5]   CLUeFARM: Integrated web-service platform for smart farms [J].
Colezea, Madalin ;
Musat, George ;
Pop, Florin ;
Negru, Catalin ;
Dumitrascu, Alexandru ;
Mocanu, Mariana .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2018, 154 :134-154
[6]   Landscape irrigation by evapotranspiration-based irrigation controllers under dry conditions in Southwest Florida [J].
Davis, S. L. ;
Dukes, M. D. ;
Miller, G. L. .
AGRICULTURAL WATER MANAGEMENT, 2009, 96 (12) :1828-1836
[7]   Large-Scale Cross-Modality Search via Collective Matrix Factorization Hashing [J].
Ding, Guiguang ;
Guo, Yuchen ;
Zhou, Jile ;
Gao, Yue .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2016, 25 (11) :5427-5440
[8]  
Food and Agriculture Organization of the United Nations, 2017, The State of Food and Agriculture: Leveraging Food Systems for Inclusive Rural Transformation, V2
[9]   Soil moisture prediction using support vector machines [J].
Gill, M. Kashif ;
Asefa, Tirusew ;
Kemblowski, Mariush W. ;
McKee, Mae .
JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 2006, 42 (04) :1033-1046
[10]   Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist's tacit knowledge [J].
Goldstein, Anat ;
Fink, Lior ;
Meitin, Amit ;
Bohadana, Shiran ;
Lutenberg, Oscar ;
Ravid, Gilad .
PRECISION AGRICULTURE, 2018, 19 (03) :421-444