scholarly journals On-Farm Assessment of AquaSpy Soil Moisture Sensors for Irrigation Scheduling

2021 ◽  
Vol 7 (5) ◽  
Author(s):  
P. Rossini ◽  
A. Patrignani
Keyword(s):  
Soil Moisture ◽  
Irrigation Scheduling ◽  
Soil Moisture Sensors ◽  
On Farm

scholarly journals Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors

2020 ◽  
Vol 228 ◽  
pp. 105880 ◽  
Author(s):  
Jesús María Domínguez-Niño ◽  
Jordi Oliver-Manera ◽  
Joan Girona ◽  
Jaume Casadesús
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Irrigation Scheduling ◽  
Soil Moisture Sensors ◽  
Automated Algorithm

scholarly journals Performance Assessment of Five Different Soil Moisture Sensors under Irrigated Field Conditions in Oklahoma

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3786 ◽  
Author(s):  
Sumon Datta ◽  
Saleh Taghvaeian ◽  
Tyson Ochsner ◽  
Daniel Moriasi ◽  
Prasanna Gowda ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Cropping Systems ◽  
Irrigation Management ◽  
Clay Content ◽  
Field Capacity ◽  
Irrigation Scheduling ◽  
Irrigated Agriculture ◽  
Undisturbed Soil ◽  
Soil Moisture Sensors ◽  
Rosetta Model

Meeting the ever-increasing global food, feed, and fiber demands while conserving the quantity and quality of limited agricultural water resources and maintaining the sustainability of irrigated agriculture requires optimizing irrigation management using advanced technologies such as soil moisture sensors. In this study, the performance of five different soil moisture sensors was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma, with variable levels of soil salinity and clay content. With factory calibrations, three of the sensors had sufficient accuracies at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. The study also investigated the performance of different approaches (laboratory, sensor-based, and the Rosetta model) to determine soil moisture thresholds required for irrigation scheduling, i.e., field capacity (FC) and wilting point (WP). The estimated FC and WP by the Rosetta model were closest to the laboratory-measured data using undisturbed soil cores, regardless of the type and number of input parameters used in the Rosetta model. The sensor-based method of ranking the readings resulted in overestimation of FC and WP. Finally, soil moisture depletion, a critical parameter in effective irrigation scheduling, was calculated by combining sensor readings and FC estimates. Ranking-based FC resulted in overestimation of soil moisture depletion, even for accurate sensors at the site with lower levels of salinity and clay.

Comparison of Sensor-Based and Weather-Based Irrigation Scheduling

2020 ◽  
Vol 36 (3) ◽  
pp. 375-386
Author(s):  
Ruixiu Sui ◽  
Earl D. Vories
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Texture ◽  
Irrigation Scheduling ◽  
Cotton Field ◽  
List Type ◽  
Soil Moisture Sensors

HighlightsSensor-based irrigation scheduling methods (SBISM) were compared with computerized water balance scheduling.Number and time of irrigation events scheduled using the SBISM were often different from those predicted by the computerized method.The highly variable soils at the Missouri site complicated interpretation of the sensor values.Both SBISM and computerized water balance scheduling could be used for irrigation scheduling with close attention to soil texture and effective rainfall or irrigation.Abstract. Sensor-based irrigation scheduling methods (SBISM) measure soil moisture to allow scheduling of irrigation events based on the soil-water status. With rapid development of soil moisture sensors, more producers have become interested in SBISM, but interpretation of the sensor data is often difficult. Computer-based methods attempt to estimate soil water content and the Arkansas Irrigation Scheduler (AIS) is one example of a weather-based irrigation scheduling tool that has been used in the Mid-South for many years. To aid producers and consultants interested in learning more about irrigation scheduling, field studies were conducted for two years in Mississippi and a year in Missouri to compare SBISM with the AIS. Soil moisture sensors (Decagon GS-1, Acclima TDR-315, Watermark 200SS) were installed in multiple locations of a soybean field (Mississippi) and cotton field (Missouri). Soil water contents of the fields were measured hourly at multiple depths during the growing seasons. The AIS was installed on a computer to estimate soil water content and the required data were obtained from nearby weather stations at both locations and manually entered in the program. In Mississippi, numbers and times of the irrigation events triggered by the SBISM were compared with those that would have been scheduled by the AIS. Results showed the number and time of irrigation events scheduled using the SBISM were often different from those predicted by the AIS, especially during the 2018 growing season. The highly variable soils at the Missouri site complicated the interpretation of the sensor values. While all of the sites were within the Tiptonville silt loam map unit, some of the measurements appeared to come from sandier soils. The AIS assumed more water entered the soil than the sensors indicated from both irrigations and rainfalls less than 25 mm. While the irrigation amounts were based on the pivot sprinkler chart, previous testing had confirmed the accuracy of the charts. Furthermore, the difference varied among sites, especially for rainfall large enough to cause runoff. The recommendations based on the Watermark sensors agreed fairly well with the AIS in July after the data from the sandiest site was omitted; however, the later irrigations called for by the AIS were not indicated by the sensors. Both the sensor-based irrigation scheduling method and the AIS could be used as tools for irrigation management in the Mid-South region, but with careful attention to soil texture and the effective portion of rainfall or irrigation. Keywords: Irrigation scheduling, Soil moisture sensor, Soil water content, Water management.

Irrigation Scheduling for Green Bell Peppers Using Capacitance Soil Moisture Sensors

2011 ◽  
Vol 137 (2) ◽  
pp. 73-81 ◽  
Author(s):  
L. Zotarelli ◽  
M. D. Dukes ◽  
J. M. S. Scholberg ◽  
K. Femminella ◽  
R. Muñoz-Carpena
Keyword(s):  
Soil Moisture ◽  
Irrigation Scheduling ◽  
Soil Moisture Sensors ◽  
Bell Peppers

scholarly journals Implementation of Wireless Sensor Networks for Irrigation Control in Three Container Nurseries

2013 ◽  
Vol 23 (6) ◽  
pp. 747-753 ◽  
Author(s):  
Matthew Chappell ◽  
Sue K. Dove ◽  
Marc W. van Iersel ◽  
Paul A. Thomas ◽  
John Ruter
Keyword(s):  
Wireless Sensor Networks ◽  
Soil Moisture ◽  
Sensor Networks ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Wireless Sensor ◽  
And Control ◽  
On Farm ◽  
Control Irrigation ◽  
Monitor And Control

Water quality and quantity are increasingly important concerns for agricultural producers and have been recognized by governmental and nongovernmental agencies as focus areas for future regulatory efforts. In horticultural systems, and especially container production of ornamentals, irrigation management is challenging. This is primarily due to the limited volume of water available to container-grown plants after an irrigation event and the resultant need to frequently irrigate to maintain adequate soil moisture levels without causing excessive leaching. To prevent moisture stress, irrigation of container plants is often excessive, resulting in leaching and runoff of water and nutrients applied to the container substrate. For this reason, improving the application efficiency of irrigation is necessary and critical to the long-term sustainability of the commercial nursery industry. The use of soil moisture sensing technology is one method of increasing irrigation efficiency, with the on-farm studies described in this article focusing on the use of capacitance-based soil moisture sensors to both monitor and control irrigation events. Since on-farm testing of these wireless sensor networks (WSNs) to monitor and control irrigation scheduling began in 2010, WSNs have been deployed in a diverse assortment of commercial horticulture operations. In deploying these WSNs, a variety of challenges and successes have been observed. Overcoming specific challenges has fostered improved software and hardware development as well as improved grower confidence in WSNs. Additionally, growers are using WSNs in a variety of ways to fit specific needs, resulting in multiple commercial applications. Some growers use WSNs as fully functional irrigation controllers. Other growers use components of WSNs, specifically the web-based graphical user interface (GUI), to monitor grower-controlled irrigation schedules.

scholarly journals Irrigation Scheduling Using Soil Moisture Sensors

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Ruixiu Sui
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Management Practices ◽  
Root Zone ◽  
Weighted Average ◽  
Irrigation Scheduling ◽  
Time Interval ◽  
Soil Moisture Sensors

Irrigation is required to ensure crop production. Practical methods of use sensors to determine soil water status are needed in irrigation scheduling. Soil moisture sensors were evaluated and used for irrigation scheduling in humid region of the Mid-South US. Soil moisture sensors were installed in soil at depths of 15 cm, 30 cm, and 61 cm belowground. Soil volumetric water content was automatically measured by the sensors in a time interval of an hour during the crop growing season. Soil moisture data were wirelessly transferred onto internet through a wireless sensor network (WSN) so that the data could be remotely accessed online. Soil water content measured at the three depths were interpreted using a weighted average method to reflect the status of soil water in plant root zone. A threshold to trigger an irrigation event was determined with sensor-measured soil water content. An antenna mounting device was developed for operation of the WSN. Using the antenna mounting device, the soil moisture measurement was not be interrupted by crop field management practices.

scholarly journals Performance of Soil Moisture Sensors in Florida Sandy Soils

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 358 ◽  
Author(s):  
Rhuanito Soranz Ferrarezi ◽  
Thiago Assis Rodrigues Nogueira ◽  
Sara Gabriela Cornejo Zepeda
Keyword(s):  
Water Management ◽  
Soil Moisture ◽  
Water Content ◽  
Sandy Soils ◽  
Irrigation Scheduling ◽  
Management Efficiency ◽  
Soil Moisture Sensors ◽  
Citrus Production ◽  
Gravimetric Water Content ◽  
Calibration Equations

Soil moisture sensors can improve water management efficiency by measuring soil volumetric water content (θv) in real time. Soil-specific calibration equations used to calculate θv can increase sensor accuracy. A laboratory study was conducted to evaluate the performance of several commercial sensors and to establish soil-specific calibration equations for different soil types. We tested five Florida sandy soils used for citrus production (Pineda, Riviera, Astatula, Candler, and Immokalee) divided into two depths (0.0–0.3 and 0.3–0.6 m). Readings were taken using twelve commercial sensors (CS650, CS616, CS655 (Campbell Scientific), GS3, 10HS, 5TE, GS1 (Meter), TDT-ACC-SEN-SDI, TDR315, TDR315S, TDR135L (Acclima), and Hydra Probe (Stevens)) connected to a datalogger (CR1000X; Campbell Scientific). Known amounts of water were added incrementally to obtain a broad range of θv. Small 450 cm3 samples were taken to determine the gravimetric water content and calculate the θv used to obtain the soil-specific calibration equations. Results indicated that factory-supplied calibration equations performed well for some sensors in sandy soils, especially 5TE, TDR315L, and GS1 (R2 = 0.92) but not for others (10HS, GS3, and Hydra Probe). Soil-specific calibrations from this study resulted in accuracy expressed as root mean square error (RMSE) ranging from 0.018 to 0.030 m3 m−3 for 5TE, CS616, CS650, CS655, GS1, Hydra Probe, TDR310S, TDR315, TDR315L, and TDT-ACC-SEN-SDI, while lower accuracies were found for 10HS (0.129 m3 m−3) and GS3 (0.054 m3 m−3). This study provided soil-specific calibration equations to increase the accuracy of commercial soil moisture sensors to facilitate irrigation scheduling and water management in Florida sandy soils used for citrus production.

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