Review of research progress on soil moisture sensor technology

Soil moisture is directly related to the amount of irrigation in agriculture and influences the yield of crops.Accordingly,a soil moisture sensor is an important tool for measuring soil moisture content.In this study,the previous research conducted in recent 2-3 decades on soil moisture sensors was reviewed and the principles of commonly used soil moisture sensor and their various applications were summarized.Furthermore,the advantages,disadvantages,and influencing factors of various measurement methods employed were compared and analyzed.The improvements were presented by several scholars have established the major applications and performance levels of soil moisture sensors,thereby setting the course for future development.These studies indicated that soil moisture sensors in the future should be developed to achieve high-precision,low-cost,non-destructive,automated,and highly integrated systems.Also,it was indicated that future studies should involve the development of specialized sensors for different applications and scenarios.This review research aimed to provide a certain reference for application departments and scientific researchers in the process of selecting soil moisture sensor products and measuring soil moisture.

Field Comparison of Soil Moisture Sensing Using Neutron Thermalization, Frequency Domain, Tensiometer, and Granular Matrix Sensor Devices: Relevance to Precision Irrigation

The efficient use of irrigation water requires several kinds of information. One element of efficient irrigation scheduling is monitoring the soil moisture to assure that the crop irrigation goals are being met. Various soil moisture sensing devices were tested for irrigation scheduling in silt loam at the Malheur Agricultural Experiment Station, Oregon State University between 2001 and 2004. Neutron probes, frequency domain probes, tensiometers, granular matrix sensors, and Irrigas were compared as to their performance under field conditions at Ontario, Oregon, USA. Granular matrix sensors were tested as read automatically by a datalogger and read manually with a hand-held meter. Practical suggestions are provided to use soil moisture sensors to the benefit of crop production and water conservation.

Polymer optimization for the development of low-cost moisture sensor based on nanoporous alumina thin film

Sol-gel processed alumina(Al_(2)O_(3))thin film has been investigated for sensing moisture.The sensor was based on ordered nanoporous Al_(2)O_(3)thin film,which consisted of gold electrodes on both sides of the film forming parallel plate capacitor.Alumina substrate was used for supporting thin film moisture sensor.Hydrophilicity was achieved by controlling the surface energy of the substrate and polymer(polyvinyl alcohol(PVA))optimization was done for developing rigid thin film over it.A high change in capacitance was observed as the moisture level increased from 5 ppmV to 500 ppmV.Scanning electron microscopy(SEM)results revealed that pores were distributed uniformly throughout the sample,which enhanced the adsorption of water molecule over the film.X-ray diffraction(XRD)study clearly confirmed the gamma(γ)phase of alumina thin film.It was found that the sensitivity of our sensor was suitable for commercial application.

Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model

Soil moisture content （SMC） is a key hydrological parameter in agriculture,meteorology and climate change,and understanding of spatio-temporal distributions of SMC in farmlands is important to address the precise irrigation scheduling.However,the hybrid interaction of static and dynamic environmental parameters makes it particularly difficult to accurately and reliably model the distribution of SMC.At present,deep learning wins numerous contests in machine learning and hence deep belief network （DBN） ,a breakthrough in deep learning is trained to extract the transition functions for the simulation of the cell state changes.In this study,we used a novel macroscopic cellular automata （MCA） model by combining DBN to predict the SMC over an irrigated corn field （an area of 22 km^2） in the Zhangye oasis,Northwest China.Static and dynamic environmental variables were prepared with regard to the complex hydrological processes.The widely used neural network,multi-layer perceptron （MLP） ,was utilized for comparison to DBN.The hybrid models （MLP-MCA and DBN-MCA） were calibrated and validated on SMC data within four months,i.e.June to September 2012,which were automatically observed by a wireless sensor network （WSN） .Compared with MLP-MCA,the DBN-MCA model led to a decrease in root mean squared error （RMSE） by 18%.Thus,the differences of prediction errors increased due to the propagating errors of variables,difficulties of knowing soil properties and recording irrigation amount in practice.The sequential Gaussian simulation （s Gs） was performed to assess the uncertainty of soil moisture estimations.Calculated with a threshold of SMC for each grid cell,the local uncertainty of simulated results in the post processing suggested that the probability of SMC less than 25% will be difference in different areas at different time periods.The current results showed that the DBN-MCA model performs better than the MLP-MCA model,and the DBN-MCA model provides a powerful tool for predicting SMC in highly non-linear forms.Moreover,because modeling soil moisture by using environmental variables is gaining increasing popularity,DBN techniques could contribute a lot to enhancing the calibration of MCA-based SMC estimations and hence provide an alternative approach for SMC monitoring in irrigation systems on the basis of canals.

Internet of Things Based Smart Irrigation System Using ESP WROOM32

Farming has been the most prominent and fundamental activity for generations.As the population has been mul-tiplying exponentially,the demand for agricultural yield is growing relentlessly.Such high demand in production through traditional farming methodologies often falls short in terms of efficiency due to the limitations of manual labour.In the era of digitization,smart agricultural solutions have been emerging through the windows of Internet of Things and Artificial Intelligence to improve resource management,optimize the process of farming and enhance the yield of crops,hence,ensuring sustainable growth of the increasing production.By implementing modern technologies in the field of farming we can enable telemetry through which farmers can remotely monitor and gather real time data on the desired parameters.It also gives accurate and precise measurements when compared to traditional measurement techniques.This research paper focuses on an IoT based approach for smart monitoring using ESP WROOM 32 microcontroller that helps farmers identify real-time parameters of temperature,moisture and humidity of their field.Real-time data on temperature,moisture,and humidity enables farmers to make informed decisions about irrigation and crop protection.Furthermore,the use of smart monitoring ensures accurate and precise measurements,surpassing the limitations of traditional techniques.

Design of soil moisture distribution sensor based on high-frequency capacitance

In order to obtain accurate real-time soil moisture data and the spatial distribution of soil moisture,the soil moisture measurement methods based on high-frequency capacitance edge field effect were analyzed,the structure of probe was studied,and a multi-channel soil moisture sensor was designed.Moreover,with the established two-dimensional trace planar capacitance probe model and the method of the finite element analysis,relationship between the structure of sensor probe and electric field intensity was studied and capacitance of the probe trace amount planar capacitance model was analyzed,the most optimal structure of sensor probe was determined.Design parameters of the probe which can achieve optimal sensitivity and detection range are:outer diameter 40 mm and inner diameter 38.4 mm for the probe copper ring electrode,axial length 20 mm and axial spacing 10 mm.The sensor is suitable for measuring the moisture of different type of soil.Moreover,the features of the profile soil moisture sensor were experimentally explored.The measurement accuracy reached±1.31%with better stability and consistency.Sensor probes can be assembled according to the measurement depth and used to measure soil moisture of different crop root zone.

Field instrumentation for real-time measurement of soil-water characteristic curve

Suction measurement has a vital role in unsaturated soil analysis.However,measuring soil suction remains a challenging task due to a number of issues such as the limited range of suction measurement,cavitation,or long equilibrium time.It is even more challenging when the suction measurement is to be carried on the field.Hence,the development of a new suction measurement device which is able to measure high suction range for a long duration without tedious maintenance and yet portable enough for site measurement is required.In this study,a new sensor which is referred to as NTU Osmotic Tensiometer was developed along with the method to correct for decay and temperature.The NTU osmotic tensiometer is based on polymer swelling capacity in order to measure in-situ soil suction in real time for a very long duration.It requires minimum maintenance as the polymer-based sensor is not affected by the cavitation phenomenon.However,correction for decay and temperature is of paramount importance and therefore explained in this paper.Verification of the NTU osmotic tensiometer was carried out by comparing the field measurement results from the NTU osmotic tensiometer and the small tip tensiometer.The performance of the NTU osmotic tensiometer was found to be comparable with that of the small tip tensiometer,but the NTU osmotic tensiometer is able to measure more than 100 kPa soil suction.Therefore,it is possible to obtain the field soil-water characteristic curve by combining the measured in-situ soil suction from the NTU osmotic tensiometer with the measured in-situ water content from the moisture sensor as illustrated in the paper.