Testing Soil Moisture Sensors in Florida; Five Years of Results

2010 ◽  
Author(s):  
Bernard Cardenas-Lailhacar ◽  
Michael D Dukes ◽  
Mary Shedd McCready ◽  
Melissa B Haley
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Sensors

Performance of soil moisture sensors in gypsiferous and salt-affected soils

2021 ◽  
Vol 209 ◽  
pp. 200-209
Author(s):  
Adil K. Salman ◽  
Saad E. Aldulaimy ◽  
Huthaifa J. Mohammed ◽  
Yaareb M. Abed
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Sensors ◽  
Salt Affected Soils

scholarly journals Design a Monitoring and Control in Irrigation Systems using Arduino Wemos with the Internet of Things

2021 ◽  
Vol 1 (1) ◽  
pp. 53-64
Author(s):  
Lukman Medriavin Silalahi ◽  
Setiyo Budiyanto ◽  
Freddy Artadima Silaban ◽  
Arif Rahman Hakim
Keyword(s):  
Soil Moisture ◽  
Water Level ◽  
Irrigation System ◽  
Ground Level ◽  
Prototype System ◽  
Monitoring And Control ◽  
Moisture Sensor ◽  
Turn On ◽  
Soil Moisture Sensors ◽  
Level Sensor

Irrigation door is a big issue for farmers. The factor that became a hot issue at the irrigation gate was the irresponsible attitude of the irrigation staff regarding the schedule of opening/closing the irrigation door so that it caused the rice fields to becoming dry or submerged. In this research, an automatic prototype system for irrigation system will be designed based on integrating several sensors, including water level sensors, soil moisture sensors, acidity sensors. This sensor output will be displayed on Android-based applications. The integration of communication between devices (Arduino Nano, Arduino Wemos and sensors supporting the irrigation system) is the working principle of this prototype. This device will control via an Android-based application to turn on / off the water pump, to open/close the irrigation door, check soil moisture, soil acidity in real time. The pump will automatically turn on based on the water level. This condition will be active if the water level is below 3cm above ground level. The output value will be displayed on the Android-based application screen and LCD screen. Based on the results of testing and analysis of the prototype that has been done in this research, the irrigation door will open automatically when the soil is dry. This condition occurs if the water level is less than 3 cm. The calibrated Output value, including acidity sensor, soil moisture sensor and water level sensor, will be sent to the server every 5 seconds and forwarded to an Android-based application as an output display.

scholarly journals Interpolation and 3D visualization of soil moisture

2017 ◽  
Vol 11 (1) ◽  
pp. 23-34
Author(s):  
András Hervai ◽  
Ervin Pirkhoffer ◽  
Szabolcs Ákos Fábián ◽  
Ákos Halmai ◽  
Gábor Nagy ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Cross Sections ◽  
3D Visualization ◽  
Temporal Changes ◽  
Adaptation To Climate Change ◽  
Soil Moisture Sensors ◽  
Average Maximum ◽  
Moisture Conditions ◽  
2D And 3D ◽  
Soil Moisture Measurement

Adaptation to climate change demands the optimal and sustainable water management in agriculture, with an inevitable focus on soil moisture conditions. In the current study we developed an ArcGIS 10.4. platform-based application (software) to model spatial and temporal changes in soil moisture in a soy field. Six SENTEK Drill & Drop soil moisture sensors were deployed in an experimental field of 4.3 hectares by the contribution of Elcom Ltd. Soil moisture measurement at each location were taken at six depths (5, 15, 25, 35, 45 and 55 cm) in 60-minute intervals. The model is capable to spatially interpolate monitored soil moisture using the technique. The time sequence change of soil moistures can be tracked by a Time Slider for both the 2D and 3D visualization. Soil moisture temporal changes can be visualized in either daily or hourly time intervals, and can be shown as a motion figure. Horizon average, maximum and minimum values of soil moisture data can be identified with the builtin tool of ArcGIS. Soil moisture spatial distribution can be obtained and plotted at any cross sections, whereas an alarm function has also been developed for tension values of 250, 1,000 and 1,500 kPa.

Calibration and Assessment of Capacitance-Based Soil Moisture Sensors

2020 ◽  
Author(s):  
Mohammad Zahidul I. Bhuiyan ◽  
Shanyong Wang ◽  
John Carter ◽  
Tabassum Mahzabeen Raka
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Sensors

scholarly journals Smart & Green: An Internet-of-Things Framework for Smart Irrigation

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 190 ◽  
Author(s):  
Nidia G. S. Campos ◽  
Atslands R. Rocha ◽  
Rubens Gondim ◽  
Ticiana L. Coelho da Silva ◽  
Danielo G. Gomes
Keyword(s):  
Soil Moisture ◽  
Irrigation Management ◽  
Management Plan ◽  
Matric Potential ◽  
Outlier Removal ◽  
Temporal Window ◽  
Soil Moisture Sensors ◽  
Management Scheme ◽  
Smart Agriculture ◽  
Different Sources

Irrigation is one of the most water-intensive agricultural activities in the world, which has been increasing over time. Choosing an optimal irrigation management plan depends on having available data in the monitoring field. A smart agriculture system gathers data from several sources; however, the data are not guaranteed to be free of discrepant values (i.e., outliers), which can damage the precision of irrigation management. Furthermore, data from different sources must fit into the same temporal window required for irrigation management and the data preprocessing must be dynamic and automatic to benefit users of the irrigation management plan. In this paper, we propose the Smart&Green framework to offer services for smart irrigation, such as data monitoring, preprocessing, fusion, synchronization, storage, and irrigation management enriched by the prediction of soil moisture. Outlier removal techniques allow for more precise irrigation management. For fields without soil moisture sensors, the prediction model estimates the matric potential using weather, crop, and irrigation information. We apply the predicted matric potential approach to the Van Genutchen model to determine the moisture used in an irrigation management scheme. We can save, on average, between 56.4% and 90% of the irrigation water needed by applying the Zscore, MZscore and Chauvenet outlier removal techniques to the predicted data.

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