Performance Evaluation of Soil Moisture Sensors in Coarse- and Fine-Textured Michigan Agricultural Soils

Soil moisture content is a critical parameter in understanding the water movement in soil. A soil moisture sensor is a tool that has been widely used for many years to measure soil moisture levels for their ability to provide nondestructive continuous data from multiple depths. The calibration of the sensor is important in the accuracy of the measurement. The factory-based calibration of the soil moisture sensors is generally developed under limited laboratory conditions, which are not always appropriate for field conditions. Thus, calibration and field validation of the soil moisture sensors for specific soils are needed. The laboratory experiment was conducted to evaluate the performance of factory-based calibrated soil moisture sensors. The performance of the soil moisture sensors was evaluated using Root Mean Squared Error (RMSE), Index of Agreement (IA), and Mean Bias Error (MBE). The result shows that the performance of the factory-based calibrated CS616 and EC5 did not meet all the statistical criteria except the CS616 sensor for sand. The correction equations are developed using the laboratory experiment. The validation of correction equations was evaluated in agricultural farmlands. Overall, the correction equations for CS616 and EC5 improved the accuracy in field conditions.

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Energy models for cost-optimal analysis: Development and calibration of residential reference building models for Northern Cyprus

Indoor and Built Environment ◽

10.1177/1420326x211013076 ◽

2021 ◽

pp. 1420326X2110130

Author(s):

Manta Marcelinus Dakyen ◽

Mustafa Dagbasi ◽

Murat Özdenefe

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Mean Squared Error ◽

Residential Buildings ◽

Mean Bias Error ◽

Bias Error ◽

Sampled Data ◽

Low Carbon ◽

Building Stock ◽

Northern Cyprus

Ambitious energy efficiency goals constitute an important roadmap towards attaining a low-carbon society. Thus, various building-related stakeholders have introduced regulations targeting the energy efficiency of buildings. However, some countries still lack such policies. This paper is an effort to help bridge this gap for Northern Cyprus, a country devoid of building energy regulations that still experiences electrical energy production and distribution challenges, principally by establishing reference residential buildings which can be the cornerstone for prospective building regulations. Statistical analysis of available building stock data was performed to determine existing residential reference buildings. Five residential reference buildings with distinct configurations that constituted over 75% floor area share of the sampled data emerged, with floor areas varying from 191 to 1006 m2. EnergyPlus models were developed and calibrated for five residential reference buildings against yearly measured electricity consumption. Values of Mean Bias Error (MBE) and Cumulative Variation of Root Mean Squared Error CV(RMSE) between the models’ energy consumption and real energy consumption on monthly based analysis varied within the following ranges: (MBE)monthly from –0.12% to 2.01% and CV(RMSE)monthly from 1.35% to 2.96%. Thermal energy required to maintain the models' setpoint temperatures for cooling and heating varied from 6,134 to 11,451 kWh/year.

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

Journal of Integrated and Advanced Engineering (JIAE) ◽

10.51662/jiae.v1i1.13 ◽

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.

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Using digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradiance

Atmospheric Measurement Techniques ◽

10.5194/amt-8-183-2015 ◽

2015 ◽

Vol 8 (1) ◽

pp. 183-194 ◽

Cited By ~ 13

Author(s):

A. Sanchez-Romero ◽

J. A. González ◽

J. Calbó ◽

A. Sanchez-Lorenzo

Keyword(s):

Image Processing ◽

Temporal Resolution ◽

Solar Irradiance ◽

Atmospheric Aerosol ◽

Mean Squared Error ◽

Sunshine Duration ◽

Strong Relationship ◽

Mean Bias Error ◽

Bias Error ◽

High Temporal Resolution

Abstract. The Campbell–Stokes sunshine recorder (CSSR) has been one of the most commonly used instruments for measuring sunshine duration (SD) through the burn length of a given CSSR card. Many authors have used SD to obtain information about cloudiness and solar radiation (by using Ångström–Prescott type formulas), but the burn width has not been used systematically. In principle, the burn width increases for increasing direct beam irradiance. The aim of this research is to show the relationship between burn width and direct solar irradiance (DSI) and to prove whether this relationship depends on the type of CSSR and burning card. A method of analysis based on image processing of digital scanned images of burned cards is used. With this method, the temporal evolution of the burn width with 1 min resolution can be obtained. From this, SD is easily calculated and compared with the traditional (i.e., visual) determination. The method tends to slightly overestimate SD, but the thresholds that are used in the image processing could be adjusted to obtain an improved estimation. Regarding the burn width, experimental results show that there is a high correlation between two different models of CSSRs, as well as a strong relationship between burn widths and DSI at a high-temporal resolution. Thus, for example, hourly DSI may be estimated from the burn width with higher accuracy than based on burn length (for one of the CSSR, relative root mean squared error is 24 and 30%, respectively; mean bias error is −0.6 and −30.0 W m−2, respectively). The method offers a practical way to exploit long-term sets of CSSR cards to create long time series of DSI. Since DSI is affected by atmospheric aerosol content, CSSR records may also become a proxy measurement for turbidity and atmospheric aerosol loading.

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Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

Remote Sensing ◽

10.3390/rs11091052 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1052 ◽

Cited By ~ 3

Author(s):

Reto Stöckli ◽

Jędrzej S. Bojanowski ◽

Viju O. John ◽

Anke Duguay-Tetzlaff ◽

Quentin Bourgeois ◽

...

Keyword(s):

Mean Squared Error ◽

Sensor Data ◽

Mean Bias Error ◽

Retrieval Algorithm ◽

Bias Error ◽

Cloud Detection ◽

Climate Data ◽

Detection Algorithms ◽

Fractional Cover ◽

Spatio Temporal

Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991–2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data.

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Using digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradiance

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-9537-2014 ◽

2014 ◽

Vol 7 (9) ◽

pp. 9537-9571

Author(s):

A. Sanchez-Romero ◽

J. A. González ◽

J. Calbó ◽

A. Sanchez-Lorenzo

Keyword(s):

Image Processing ◽

Temporal Resolution ◽

Solar Irradiance ◽

Atmospheric Aerosol ◽

Mean Squared Error ◽

Sunshine Duration ◽

Mean Bias Error ◽

Unbiased Estimation ◽

Bias Error ◽

High Temporal Resolution

Abstract. The Campbell–Stokes sunshine recorder (CSSR) has been one of the most commonly used instruments for measuring sunshine duration (SD) through the burn length of a given CSSR card. Many authors have used SD to obtain information about cloudiness and solar radiation (by using Ångström–Prescott type formulas). Contrarily, the burn width has not been used systematically. In principle, the burn width increases for increasing direct beam irradiance. The aim of this research is to show the relationship between burn width and direct solar irradiance (DSI), and to prove whether this relationship depends on the type of CSSR and burning card. A semi-automatic method based on image processing of digital scanned images of burnt cards is presented. With this method, the temporal evolution of the burn width with 1 min resolution can be obtained. From this, SD is easily calculated and compared with the traditional (i.e. visual) determination. The method tends to slightly overestimate SD but the thresholds that are used in the image processing could be adjusted to obtain an unbiased estimation. Regarding the burn width, results show that there is a high correlation between two different models of CSSRs, as well as a strong relationship between burn widths and DSI at a high-temporal resolution. Thus, for example, hourly DSI may be estimated from the burn width with higher accuracy than based on burn length (for one of the CSSR, relative root mean squared error 24 and 30% respectively; mean bias error −0.6 and −30.0 W m−2 respectively). The method offers a practical way to exploit long-term sets of CSSR cards to create long time series of DSI. Since DSI is affected by atmospheric aerosol content, CSSR records may also become a proxy measurement for turbidity and atmospheric aerosol loading.

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RANCANG BANGUN PENYIRAM TANAMAN OTOMATIS MENGGUNAKAN SESOR KELEMBABAN TANAH BERBASIS ARDUINO

Jurnal Teknologi Informasi MURA ◽

10.32767/jti.v11i02.626 ◽

2019 ◽

Vol 11 (02) ◽

pp. 76-83

Author(s):

Armanto Armanto

Keyword(s):

Soil Moisture ◽

Rainy Season ◽

Dry Season ◽

Food Plants ◽

Crop Failure ◽

Moisture Sensor ◽

Soil Moisture Sensors ◽

Self Sufficiency ◽

Information And Communication ◽

Moisture Conditions

Food self-sufficiency is a government program that is currently being actively promoted, so that Indonesia can be independent in providing food by the end of 2019. Indonesia besides being a maritime country is also an agricultural country with fertile land with 2 seasons, namely the rainy season and the dry season. In the rainy season food plants usually do not need to be watered because they have enough rain water. Whereas in the dry season the plants must be watered regularly in accordance with soil moisture conditions. Farmers usually do not grow food in the dry season for fear that it will not grow well and crop failure. Dependence of farmers with the season causes farmer production to decline and becomes an obstacle in the success of the food self-sufficiency program. To overcome the constraints of the dry season and so that farmers can still plant crops in the dry season, we need an information and communication technology-based agricultural tool product in the form of a programmed chip mircrocontroller so that it can control watering plants automatically based on soil moisture that is detected using domestic soil moisture sensors . This tool will detect whether the soil where the planting is dry so that the tool can control watering automatically when the soil lacks the element of water. So farmers do not need to do watering manually. So that plants can continue to flourish even though it is the dry season. In addition to helping farmers this tool can also be installed on plantations, seedbed nurseries, urban parks, hotels, offices, and in homes that have parks or plants that need regular watering. Keywords— Soil Moisture Sensor, Microcontroller, Arduino

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Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model

Processes ◽

10.3390/pr9112045 ◽

2021 ◽

Vol 9 (11) ◽

pp. 2045

Author(s):

Nehal Elshaboury ◽

Eslam Mohammed Abdelkader ◽

Ghasan Alfalah ◽

Abobakr Al-Sakkaf

Keyword(s):

Neural Network ◽

Waste Management ◽

Municipal Solid Waste ◽

Solid Waste ◽

Mean Squared Error ◽

Pearson Correlation ◽

Mean Bias Error ◽

Bias Error ◽

P Value ◽

Waste Generation

Developing successful municipal waste management planning strategies is crucial for implementing sustainable development. The research proposed the application of an optimized artificial neural network (ANN) to forecast quantities of waste in Poland. The neural network coupled with particle swarm optimization (PSO) algorithm is compared to the conventional neural network using five assessment metrics. The metrics are coefficient of efficiency (CE), Pearson correlation coefficient (R), Willmott’s index of agreement (WI), root mean squared error (RMSE), and mean bias error (MBE). Selected explanatory factors are incorporated in the developed models to reflect the influence of economic, demographic, and social aspects on the rate of waste generation. These factors are population, employment to population ratio, revenue per capita, number of entities by type of business activity, and number of entities enlisted in REGON per 10,000 population. According to the findings, the ANN–PSO model (CE = 0.92, R = 0.96, WI = 0.98, RMSE = 11,342.74, and MBE = 6548.55) significantly outperforms the traditional ANN model (CE = 0.11, R = 0.68, WI = 0.78, RMSE = 38,571.68, and MBE = 30,652.04). The significant level of the reported outputs is evaluated using the Wilcoxon–Mann–Whitney U-test, with a significance level of 0.05. The p-values of the pairings (ANN, observed) and (ANN, ANN–PSO) are all less than 0.05, suggesting that the models are statistically different. On the other hand, the P-value of (ANN–PSO, observed) is more than 0.05, suggesting that the difference between the models is statistically insignificant. Therefore, the proposed ANN–PSO model proves its efficiency at estimating municipal solid waste quantities and may be regarded as a cost-efficient method of developing integrated waste management systems.

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Soil Moisture Sensor-based Systems are Suitable for Monitoring and Controlling Irrigation of Greenhouse Crops

HortScience ◽

10.21273/hortsci12676-17 ◽

2018 ◽

Vol 53 (4) ◽

pp. 552-559 ◽

Cited By ~ 2

Author(s):

Scott Henderson ◽

David Gholami ◽

Youbin Zheng

Keyword(s):

Soil Moisture ◽

Crop Production ◽

Growth Index ◽

Dry Weight ◽

Irrigation Systems ◽

Crop Species ◽

Moisture Sensor ◽

Soil Moisture Sensor ◽

Soil Moisture Sensors ◽

Microclimate Variation

Sensor-based feedback control irrigation systems have been increasingly explored for greenhouse applications. However, the relationships between microclimate variation, plant water usage, and growth are not well understood. A series of trials were conducted to investigate the microclimate variations in different greenhouses and whether a soil moisture sensor-based system can be used in monitoring and controlling irrigation in greenhouse crop productions. Ocimum basilicum ‘Genovese Gigante’ basil and Campanula portenschlagiana ‘Get Mee’ bellflowers were monitored using soil moisture sensors for an entire crop cycle at two commercial greenhouses. Significant variations in greenhouse microclimates were observed within the two commercial greenhouses and within an older research greenhouse. Evaporation rates were measured and used as an integrated indicator of greenhouse microclimate conditions. Evaporation rates varied within all three greenhouses and were almost double the lowest rates within one of the greenhouses, suggesting microclimates within a range of greenhouses. Although these microclimate variations caused large variations in the growing substrate water contents of containers within the greenhouses, the growth and quality of the plants were unaffected. For example, no significant correlations were observed between the growth of bellflower plants and the average volumetric water content (VWC), minimum VWC, or maximum VWC of the growing substrate. The change in VWC at each irrigation (ΔVWC), however, was positively correlated with the fresh weight, dry weight, and growth index (GI) of the bellflowers. For basil, no significant correlations were observed between plant growth and ΔVWC. This suggests that sensor-based feedback irrigation systems can be used for greenhouse crop production when considerations are given to factors such as the magnitude of microclimate variation, crop species and its sensitivity to water stress, and growing substrate.

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Soil Moisture Sensor Network Design for Hydrological Applications

10.5194/hess-2020-24 ◽

2020 ◽

Author(s):

Lu Zhuo ◽

Qiang Dai ◽

Binru Zhao ◽

Dawei Han

Keyword(s):

Soil Moisture ◽

Network Design ◽

Wrf Model ◽

Principal Component ◽

Optimal Number ◽

In Situ Observation ◽

Catchment Scale ◽

Moisture Sensor ◽

Soil Moisture Sensors ◽

Vital State

Abstract. Soil moisture plays an important role in the partitioning of rainfall into evapotranspiration, infiltration and runoff, hence a vital state variable in the hydrological modelling. However, due to the heterogeneity of soil moisture in space most existing in-situ observation networks rarely provide sufficient coverage to capture the catchment-scale soil moisture variations. Clearly, there is a need to develop a systematic approach for soil moisture network design, so that with the minimal number of sensors the catchment spatial soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed. It is based on the Principal Component Analysis (PCA) and Elbow curve for the determination of the optimal number of soil moisture sensors; and K-means Cluster Analysis (CA) and a selection of statistical criteria for the identification of the sensor placements. Furthermore, the long-term (10-year) soil moisture datasets estimated through the advanced Weather Research and Forecasting (WRF) model are used as the network design inputs. In the case of the Emilia Romagna catchment, the results show the proposed network is very efficient in estimating the catchment-scale soil moisture (i.e., with NSE and r at 0.995 and 0.999, respectively for the areal mean estimation; and 0.973 and 0.990, respectively for the areal standard deviation estimation). To retain 90 % variance, a total of 50 sensors in a 22,124 km2 catchment is needed, which in comparison with the original number of WRF grids (828 grids), the designed network requires significantly fewer sensors. However, refinements and investigations are needed to further improve the design scheme which are also discussed in the paper.

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The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin: An Evaluation of 15 Year WRF Simulations

Applied Sciences ◽

10.3390/app10051765 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1765 ◽

Cited By ~ 2

Author(s):

Ghulam Hussain Dars ◽

Courtenay Strong ◽

Adam K. Kochanski ◽

Kamran Ansari ◽

Syed Hammad Ali

Keyword(s):

Mean Squared Error ◽

Pearson Correlation ◽

Wrf Model ◽

Horizontal Resolution ◽

Indus Basin ◽

Spatiotemporal Variability ◽

Mean Bias Error ◽

Bias Error ◽

Upper Indus Basin ◽

Precipitation And Temperature

Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01–d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.

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