scholarly journals Sensitivity of PR2 Capacitance Soil Moisture Meter for Irrigation Scheduling

2021 ◽  
Vol 1 (3) ◽  
pp. 1-5
Author(s):  
Olotu Y.* ◽  
◽  
Omoakhalen A.I. ◽  
Ososomi A.S. ◽  
Gbalaja Mayowa ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Organic Soil ◽  
Integrated Water Resources Management ◽  
Irrigation Scheduling ◽  
Computer Algorithms ◽  
Resources Management ◽  
Moisture Content Measurement ◽  
Complex Computer

Accurate soil moisture content measurement is essential for designing a robust irrigation scheduling and integrated water resources management (I.W.R.M.). Capacitance-based sensors have widely been used to monitor soil moisture at different measuring depths coupled with continuous and instantaneous outputs. This study's objective was to evaluate the PR2 capacitance moisture meter's performance on mineral and organic soil water content. The outputs of PR2 in m3 /m3 and vol.% were compared with gravimetrically measured soil moisture. The R.M.S.E. measurement at Site A at the first and second replicates increased from 0.49% to 0.67%. In contrast, the r2 value of 0.99 was obtained for the two replications when comparing the soil moisture content observed from gravimetric measurement and the automated outputs from the PR2 Probe soil monitor. The R.M.S.E. values were 0.48%, and 1.32% were estimated for the first and second replications at Site B. The result indicates that the PR2 Profile Probe could be a reliable alternative to other time-consuming, complex computer algorithms for accurate point measurement of soil moisture.

scholarly journals Sensitivity of PR2 Capacitance Soil Moisture Meter for Irrigation Scheduling

2021 ◽  
pp. 1-5
Author(s):  
Olotu Y. ◽  
◽  
Omoakhalen A.I. ◽  
Ososomi A.S. ◽  
Gbalaja Mayowa ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Organic Soil ◽  
Integrated Water Resources Management ◽  
Irrigation Scheduling ◽  
Computer Algorithms ◽  
Resources Management ◽  
Moisture Content Measurement ◽  
Complex Computer

Accurate soil moisture content measurement is essential for designing a robust irrigation scheduling and integrated water resources management (I.W.R.M.). Capacitance-based sensors have widely been used to monitor soil moisture at different measuring depths coupled with continuous and instantaneous outputs. This study’s objective was to evaluate the PR2 capacitance moisture meter’s performance on mineral and organic soil water content. The outputs of PR2 in m3/m3 and vol.% were compared with gravimetrically measured soil moisture. The R.M.S.E. measurement at Site A at the first and second replicates increased from 0.49% to 0.67%. In contrast, the r2 value of 0.99 was obtained for the two replications when comparing the soil moisture content observed from gravimetric measurement and the automated outputs from the PR2 Probe soil monitor. The R.M.S.E. values were 0.48%, and 1.32% were estimated for the first and second replications at Site B. The result indicates that the PR2 Profile Probe could be a reliable alternative to other time-consuming, complex computer algorithms for accurate point measurement of soil moisture.

Development of calibration algorithms for selected water content reflectometry probes for burned and non-burned organic soils of Alaska

2010 ◽  
Vol 19 (7) ◽  
pp. 961 ◽  
Author(s):  
Laura L. Bourgeau-Chavez ◽  
Gordon C. Garwood ◽  
Kevin Riordan ◽  
Benjamin W. Koziol ◽  
James Slawski
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Water Content ◽  
Soil Profile ◽  
Soil Moisture Content ◽  
Mineral Soil ◽  
Organic Soil ◽  
Organic Content ◽  
Organic Soils ◽  
Probe Type

Water content reflectometry is a method used by many commercial manufacturers of affordable sensors to electronically estimate soil moisture content. Field‐deployable and handheld water content reflectometry probes were used in a variety of organic soil‐profile types in Alaska. These probes were calibrated using 65 organic soil samples harvested from these burned and unburned, primarily moss‐dominated sites in the boreal forest. Probe output was compared with gravimetrically measured volumetric moisture content, to produce calibration algorithms for surface‐down‐inserted handheld probes in specific soil‐profile types, as well as field‐deployable horizontally inserted probes in specific organic soil horizons. General organic algorithms for each probe type were also developed. Calibrations are statistically compared to determine their suitability. The resulting calibrations showed good agreement with in situ validation and varied from the default mineral‐soil‐based calibrations by 20% or more. These results are of particular interest to researchers measuring soil moisture content with water content reflectometry probes in soils with high organic content.

scholarly journals Soil moisture content measurement using optical fiber long period gratings

2017 ◽  
Author(s):  
J. S. Hallett ◽  
M. Partridge ◽  
S. W. James ◽  
D. Tiwari ◽  
T. Farewell ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Optical Fiber ◽  
Soil Moisture Content ◽  
Long Period ◽  
Content Measurement ◽  
Long Period Gratings ◽  
Moisture Content Measurement

scholarly journals Development and integration of soil moisture sensor with drip system for precise irrigation scheduling through mobile phone

2016 ◽  
Vol 8 (4) ◽  
pp. 1959-1965 ◽  
Author(s):  
Jitendra Kumar ◽  
Neelam Patel ◽  
T. B. S. Rajput
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Mobile Phone ◽  
Real Time ◽  
Soil Moisture Content ◽  
Irrigation System ◽  
Irrigation Scheduling ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
Level Sensor

Soil moisture sensor is an instrument for quick measurements of soil moisture content in the crop root zone on real time basis. The main objective of this research was development and evaluation of an indigenous sensor for precise irrigation scheduling. The various parts of sensor developed were ceramic cup, acrylic pipe, level sensor, tee, reducer, gland, cork, and end cap. The designed system was successfully tested on okra crop and calibrated with frequency domain reflectometry (FDR) by three methods of irrigation, i.e. check basin, furrow and drip, respectively. The average depth of water depletion in modified tensiometer by these methods was 27 to 35 cm at 50% management allowable depletion (MAD) of field capacity. This depth was useful for the level sensor to be installed inside modified tensiometer for real time irrigation scheduling. The correlation coefficient (R2) between soil moisture content obtained from the developed sensor and FDR was 0.963. Sensor network was integrated with global system for mobile communication (GSM), short message service (SMS) and drip head work to develop an automated irrigation system. This would enable farmers to effectively monitor and control water application in the field by sending command through SMS and receiving pumping status through the mobile phone.

scholarly journals Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3408 ◽  
Author(s):  
Olutobi Adeyemi ◽  
Ivan Grove ◽  
Sven Peets ◽  
Yuvraj Domun ◽  
Tomas Norton
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Irrigation Scheduling ◽  
Neural Network Models ◽  
Dynamic Neural Network ◽  
Rule Based ◽  
Scheduling System ◽  
Rule Based System

Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a R 2 value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. The predictive irrigation scheduling system was evaluated against a rule-based system that applies irrigation based on predefined thresholds. Results indicate that the predictive system achieves a water saving ranging between 20 and 46% while realizing a yield and water use efficiency similar to that of the rule-based system.

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