A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields

被引:4
|
作者
Lopez, Emiliano [1 ]
Vionnet, Carlos [1 ,2 ]
Ferrer-Cid, Pau [3 ]
Barcelo-Ordinas, Jose M. [3 ]
Garcia-Vidal, Jorge [3 ]
Contini, Guillermo [1 ]
Prodolliet, Jorge [1 ]
Maiztegui, Jose [4 ]
机构
[1] Univ Nacl Litoral, Engn & Water Sci Dept, RA-3000 Santa Fe, Argentina
[2] Natl Council Sci & Tech Res, RA-3000 Santa Fe, Argentina
[3] Univ Politecn Cataluna, Comp Architecture Dept, Barcelona 08034, Spain
[4] Univ Nacl Litoral, Agr Sci Dept, RA-3080 Esperanza S, Argentina
关键词
open-source hardware; crop productivity; hydro-environmental monitoring; machine-learning calibration; NETWORK; DYNAMICS; CLIMATE; SENSOR; FLOOD;
D O I
10.3390/s22186840
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The simultaneous measurement of soil water content and water table levels is of great agronomic and hydrological interest. Not only does soil moisture represent the water available for plant growth but also water table levels can affect crop productivity. Furthermore, monitoring soil saturation and water table levels is essential for an early warning of extreme rainfall situations. However, the measurement of these parameters employing commercial instruments has certain disadvantages, with a high cost of purchase and maintenance. In addition, the handling of commercial devices makes it difficult to adapt them to the specific requirements of farmers or decision-makers. Open-source IoT hardware platforms are emerging as an attractive alternative to developing flexible and low-cost devices. This paper describes the design of a datalogger device based on open-source hardware platforms to register water table levels and soil moisture data for agronomic applications. The paper begins by describing energy-saving and wireless transmission techniques. Then, it summarizes the linear calibration of the phreatimeter sensor obtained with laboratory and field data. Finally, it shows how non-linear machine-learning techniques improve predictions over classical tools for the moisture sensor (SKU: SEN0193).
引用
收藏
页数:23
相关论文
共 2 条
  • [1] Cloud based Low-Power Long-Range IoT Network for Soil Moisture monitoring in Agriculture
    Bhattacherjee, Subhra Shankha
    Shreeshan, S.
    Priyanka, Gattu
    Jadhav, Akshay Ramesh
    Rajalakshmi, P.
    Kholova, Jana
    2020 IEEE SENSORS APPLICATIONS SYMPOSIUM (SAS 2020), 2020,
  • [2] IoT solution for smart water distribution networks based on a low-power wireless network, combined at the device-level: A case study
    Garcia-Martin, Juan Pablo
    Torralba, Antonio
    Hidalgo-Fort, Eduardo
    Daza, David
    Gonzalez-Carvajal, Ramon
    INTERNET OF THINGS, 2023, 22