Soil moisture sensor for agricultural applications inspired from state of art study of surfaces scattering models &amp; semi-empirical soil moisture models

Shakya, Amit Kumar; Ramola, Ayushman; Kandwal, Akhilesh; Vidyarthi, Anurag

doi:10.1016/j.jssas.2021.06.006

Cited by 19 publications

(8 citation statements)

References 57 publications

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“…In addition to irrigation efficiency, the emission of irrigation from research that has been carried out produces the right recommendations in the irrigation or fertilization process so as not to have a negative impact on the environment [32]. The sensors used to determine the results of the irrigation process are soil moisture sensors, one of which uses semi-empirical soil moisture [33]. A multimodal neural network to estimate plant water stress can increase the accuracy of plant water stress estimation by 21% [34].…”

Section: Related Workmentioning

confidence: 99%

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

Setyawan,

Marjunus

et al. 2024

ijacsa

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The integration of IoT systems in agriculture has become a very important need amid the high population and increasingly limited farmland, which demands researchers to be more innovative in addressing these issues. Using IoT systems for automatic irrigation, fertilization, and cooling based on sensor values through internet networks. Poor internet connection leads to the failure of automation and sustainability in online conditions, which can be very dangerous for plants. This paper presents a new IoT-based control system divided into two parts: an automation system and an IoT system, which can maintain sustainability in online conditions to ensure that plants in the planting area are always controlled. In addition, the sensors used have undergone calibration processes to determine the increase in precision of the sensor values produced. The research results show that the system can maintain sustainability under online conditions. Mobile apps are available for control when the system is online, but if it goes offline and is unable to reconnect, the Arduino Mega will fully manage control using soil moisture sensor values for irrigation processes if the values fall below a certain threshold. This demonstrates the sustainability of the system in online conditions, allowing continuous control and reducing the risk of plant death in the planting area. The calibration result shows an increase in precision for the air temperature and humidity (DHT 11 sensor) by 7.14 and 6.15, respectively. Additionally, the precision improvement for the soil pH sensor is 1.81, while for the soil moisture sensor and the water flow sensor, it is 0.13 and 0.008, respectively.

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Section: Related Workmentioning

confidence: 99%

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

Setyawan,

Marjunus

et al. 2024

ijacsa

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“…These models were among the earliest approaches to analyzing scattering from rough surfaces, while more modern approaches like Integral Equation Model (IEM) [ 9 ] and Small Slope Approximation Method (SSAM) [ 10 ] are considered to have a larger validity range for monitoring scattering from rough surfaces. Shakya et al [ 11 ] introduced a prototype of an IoT-based resistive soil moisture sensor, which offers voltage readings associated with varying levels of moisture in the soil. The main disadvantage of these models is that they follow a complex methodology and are quite challenging to implement.…”

Section: Introductionmentioning

confidence: 99%

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

Zhang,

Hou,

Huang

2023

Sensors

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This research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN) data (WATERNET), the method captured and integrated local soil moisture characteristics, thereby improving regional model state estimations. The use of varying observation search radii with the Local Error-subspace Transform Kalman Filter (LESTKF) resulted in improved spatial and temporal assimilation performance, while also considering the impact of observation data uncertainties. The best performance (improvement of 0.006 m3/m3) of LESTKF was achieved with a 20 km observation search radii and 0.01 m3/m3 observation standard error. This study assimilated wireless sensor network data into a distributed model, presenting a departure from traditional methods. The high accuracy and resolution capabilities of WATERNET’s regional soil moisture observations were crucial, and its provision of multi-layered soil temperature and moisture observations presented new opportunities for integration into the data assimilation framework, further enhancing hydrological state estimations. This study’s implications are broad and relevant to regional-scale water resource research and management, particularly for freshwater resource scheduling at small basin scales.

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“…For different regions, the applicability of empirical model is uncertain when the surface and radar parameters are different (Chen et al 2003;Baghdadi et al 2012). The semi-empirical model considers the relationship between the backscattering coefficient, surface parameters, and radar parameters, which has the advantage of a wide application range (Shakya et al 2021;Stuurop et al 2021). Due to the complexity of the interaction between electromagnetic wave and surface, the radar backscattering coefficient is affected by the surface dielectric constant and soil roughness and radar parameters (incidence angle, frequency, and polarization), and vegetation cover (Balenzano et al 2013;Qiu et al 2019).…”

Section: Introductionmentioning

confidence: 99%

An improved method for estimating soil moisture over cropland using SAR and optical data

Luo

Wen

Li³

2023

Earth Sci Inform

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The paper aims to construct simple soil moisture(SM) retrieval model using Sentinel-1 synthetic aperture radar (SAR) data. The water cloud model (WCM) removed the contribution of vegetation to the radar backscattering coefficient, and the backscattering coefficient of soil was estimated. Based on the established SM retrieval model without soil roughness parameters, the SM in farmland and forest land was retrieved using radar VV-VH dual-polarization data. The accuracy of the results showed the Pearson correlation coefficient (R) of 0.65973. The SM retrieval model for removing soil roughness parameters can estimate soil moisture with reasonable accuracy. The influence of topographic factors (elevation, slope and aspect) on the retrieval results of the model was analyzed. It was found that the area with the steep slope and blocked radar signal is not conducive to estimate SM. After removing the SM data in this area, the R between the estimated and measured SM was 0.74623. We considered the interference of uneven surfaces on the radar signal, added the radar local incidence angle parameter to improve the model, and constructed a semi-empirical SM retrieval model. The SM estimated by the improved model had a higher accuracy, and the R between the estimated and measured SM was 0.81532. The SM retrieval method constructed in this paper provides many advantages for some research and practical applications, and its application in other SAR data remains to be further studied.

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Soil moisture sensor for agricultural applications inspired from state of art study of surfaces scattering models & semi-empirical soil moisture models

Cited by 19 publications

References 57 publications

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

An improved method for estimating soil moisture over cropland using SAR and optical data

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