Estimation Of Moisture Content Research Articles

Dual-Frequency Soil Moisture Meter Method for Simultaneous Estimation of Soil Moisture and Conductivity.

The measurement of soil water content is a very important factor in plant cultivation, both from an economic and ecological point of view. Proper estimation of moisture content not only allows for proper yields but can also contribute to ecologically appropriate use of fresh water, of which the world's resources are limited. It is important, for example, that the moisture content in the root area of plants is optimal for their growth, while over-watering can result in losses in the form of water, which seeps below the root layer and is lost. The novel, inexpensive electronic meter for measuring soil moisture is presented in the article. The meter, based on a capacitive method, uses an optimization algorithm to calculate soil electrical permeability and a simplified new formula between soil electrical permeability and volumetric moisture content. Moreover, by using two high-frequency signals for measurements, it is possible not only to estimate moisture content but also soil conductivity. Both readings obtained from the meter not only allow for rational management of crop optimization for economic reasons but are also important for environmental protection. In addition, the inexpensive meter, based on the principle of operation presented, can be made as an IoT module, which allows for its wide application.

Non-destructive estimation of leaf moisture content of Epipremnum aureum based on electrical impedance spectroscopy

Non-destructive estimation of leaf moisture content of Epipremnum aureum based on electrical impedance spectroscopy

Investigating the estimation of optimum moisture content through support vector regression in individual and hybrid approaches

Investigating the estimation of optimum moisture content through support vector regression in individual and hybrid approaches

Process-based and geostationary meteorological satellite-enhanced dead fuel moisture content estimation

Process-based and geostationary meteorological satellite-enhanced dead fuel moisture content estimation

Comparison of CWSI and Ts-Ta-VIs in moisture monitoring of dryland crops (sorghum, maize) based on UAV remote sensing

Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture. Utilizing unmanned aerial vehicle (UAV) remote sensing, this study explored the applicability of an empirical crop water stress index (CWSI) based on canopy temperature and three-dimensional drought indices (TDDI) constructed from surface temperature (Ts), air temperature (Ta) and five vegetation indices (VIs) for monitoring the moisture status of dryland crops. Three machine learning algorithms (random forest regression [RFR], support vector regression, and partial least squares regression) were used to compare the performance of the drought indices for vegetation moisture content (VMC) estimation in sorghum and maize. The main results of the study were as follows: (1) Comparative analysis of the drought indices revealed that Ts-Ta-Normalized Difference Vegetation Index (TDDIn) and Ts-Ta-Enhanced Vegetation Index (TDDIe) were more strongly correlated with VMC compared with the other indices. The indices exhibited varying sensitivities to VMC under different irrigation regimes; the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment (r=−0.93); (2) Regarding spatial and temporal characteristics, the TDDIn, TDDIe and CWSI indices showed minimal differences over the experimental period, with coefficients of variation were 0.25, 0.18 and 0.24, respectively. All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops, but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event. (3) For prediction of the moisture content of single crops, RFR models based on TDDIn and TDDIe estimated VMC most accurately (R20.7), and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples, with R2 and RMSE of 0.62 and 14.26%, respectively. Thus, TDDI proved more effective than the CWSI in estimating crop water content.

Optimization of multi-dimensional indices for kiwifruit orchard soil moisture content estimation using UAV and ground multi-sensors

Accurately estimating of soil moisture content (SMC) is essential for effective irrigation water management and optimizing plant water productivity. Recent advancements in multi-sensor platforms and ensemble learning (EL) algorithms such as the use of UAV-based multispectral (MS) and thermal infrared (TIR) images, have enabled precise mapping of SMC at the field level. The purpose of this study is to optimize the multi-dimensional (n-D) index set and EL model to achieve accurate inversion mapping of SMC in the 0–60 cm root zone at a kiwifruit orchard during the fruit growth period (May-Sep 2022). The n-D index set was computed using kiwifruit canopy MS and TIR data from UAV along with in-field temperature data (air temperature [Ta], kiwifruit canopy temperature [Tc], soil temperature [Ts], and Ta-Tc, Ta-Ts, Tc-Ts). The primary findings of this study are as follows: (1) The Three-Dimensional Drought Index (TDDI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) demonstrated superior performance in capturing temporal variations of root zone SMC compared to Normalized Difference Vegetation Index (NDVI) and Enhance Vegetation Index (EVI). (2) The optimal Categorical Boosting (Catboost) model with two optimal TDDIs (TDDI22: (Tc-Ts)-EVI10,6,2-CWSI space and TDDI7: Tc-NDVI7,3-CWSI space), exhibited exceptional performance for SMC estimation, with determination coefficient (R2) and root-mean-square error (RMSE) of 0.966 ± 0.010 and 0.046 ± 0.003%, respectively. (3) The planted-by-planted mapping performed well in estimating root zone SMC at different growth stages and under different irrigation treatments, with a correlation coefficient (R) of 0.936 ± 0.074 (p < 0.001). This study demonstrated that the integration of multi-sensor indicators and EL models can improve the accuracy of SMC estimation due to its robust adaptability to various field conditions. Furthermore, the planted-grid-based SMC mapping framework can be considered as a valuable approach for monitoring dynamic SMC, facilitating informed irrigation decisions at the individual planting grid.

Evaluating pulse-reserve characteristics of Soil-Plant continuum in India using remote sensing

The pulse-reserve paradigm associated with the plant's increased water uptake following surface soil moisture pulses is not well understood in India. India has a very high spatio-temporal climate and ecological variability. Such ecological and climatological diversity makes the country-scale analysis of the pulse-reserve paradigm challenging. Here, we present the first analysis of this phenomenon using 9 years of space based passive microwave estimates of surface soil moisture and vegetation water content from both X-band and L-band. We observed from both datasets that the pulse-reserve paradigm is widespread in India across seasons and biomes. The seasonal climate in India drives the precipitation characteristics, thus uphold the pulse-reserve paradigm. The estimated Soil Moisture Threshold (SMT), at which the vegetation water content drops, has substantial spatio-temporal variations, unlike other regions worldwide. The comparisons between X-band and L-band products show significant differences in the magnitudes of soil moisture and vegetation optical depths due to inherent properties. However, most of the findings related to the pulse-reserve paradigm, specifically the sensitivity of processes on different factors, remain similar. We found a strong dependence of SMT on seasonal climate, vegetation greenness, and pulse soil moisture. The association of SMT with soil characteristics, such as clay fraction, is weak in India, contrary to global traits. Our analysis provides new insights into the plant water uptake mechanisms in India to improve the parameterization schemes for better representation of the soil–plant continuum structural and functional behavior.

Estimation of Soil Moisture Content Based on Fractional Differential and Optimal Spectral Index

Applying hyperspectral remote sensing technology to the prediction of soil moisture content (SMC) during the growth stage of soybean emerges as an effective approach, imperative for advancing the development of modern precision agriculture. This investigation focuses on SMC during the flowering stage under varying nitrogen application levels and film mulching treatments. The soybean canopy’s original hyperspectral data, acquired at the flowering stage, underwent 0–2-order differential transformation (with a step size of 0.5). Five spectral indices exhibiting the highest correlation with SMC were identified as optimal inputs. Three machine learning methods, namely support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to formulate the SMC prediction model. The results indicate the following: (1) The correlation between the optimal spectral index of each order, obtained after fractional differential transformation, and SMC significantly improved compared to the original hyperspectral reflectance data. The average correlation coefficient between each spectral index and SMC under the 1.5-order treatment was 0.380% higher than that of the original spectral index, with mNDI showing the highest correlation coefficient at 0.766. (2) In instances of utilizing the same modeling method with different input variables, the SMC prediction model’s accuracy follows the order: 1.5 order &gt; 2.0 order &gt; 1.0 order &gt; 0.5 order &gt; original order. Conversely, with consistent input variables and a change in the modeling method, the accuracy order becomes RF &gt; SVM &gt; BPNN. When comprehensively assessing model evaluation indicators, the 1.5-order differential method and RF method emerge as the preferred order differential method and model construction method, respectively. The R2 for the optimal SMC estimation model in the modeling set and validation set were 0.912 and 0.792, RMSEs were 0.005 and 0.004, and MREs were 2.390% and 2.380%, respectively. This study lays the groundwork for future applications of hyperspectral remote sensing technology in developing soil moisture content estimation models for various crop growth stages and sparks discussions on enhancing the accuracy of these different soil moisture content estimation models.

Soil Moisture Modelling Using Remote Sensing and Artificial Neural Networks: A Study of Devbhumi Dwarka Region, Gujarat, India

For the arid and semi-arid region of Devbhumi Dwarka in Gujarat, where soil moisture issue is prominent, a GIS-based approach is needed to develop models for estimation of soil moisture. In this study, Landsat and Sentinel data were used to develop multiple soil moisture indices. Using these spectral indices, artificial neural network (ANN) models were developed using actual recorded soil moisture data. Total of 174 samples were collected in the study area of 3,77,731 ha. Values of soil moisture ranged from 3.40 % to 12.50 % with an average value of 8.42 %. The maps of soil moisture indices i.e. LST, NDVI, NDWI, NSDSI3, MI and VSWI were generated on 1:550000 scale using ArcMap software. Moisture index NSDSI3 was highest correlating index. Best ANN model for soil moisture content (SMC) estimation was developed using Sentinel data (8-14-1) with RMSE, R2 and NRMSE values of 0.85 %, 0.73 and 0.10 for training and 1.11 %, 0.54 and 0.13 for testing respectively.

Estimation of moisture content in dry-cured beef ham by measuring the impedance of the surface.

Dry-cured beef ham quality is determined by moisture content; however, it is hard to do non-destructive monitoring. We investigated whether the internal moisture content of dry-cured beef ham could be determined from the impedance of the surface, which is removed at the time of eating. Supraspinatus muscle samples from Japanese Shorthorn steers were dried for different periods. The samples were then divided into the parts to be removed (surface) and edible parts. Their respective impedance and moisture content were measured, and the correlation between these two parameters was investigated. A negative correlation was observed between the impedance and moisture content of the surface and the shallow and deep sections of the edible part. There was also a negative correlation between the impedance of the surface and the moisture content of the shallow and deep sections of the edible part. Therefore, the results of this study indicate that during drying, the moisture content in the shallow and deep sections of dry-cured hams can be estimated by measuring the impedance of the surface. This non-destructive method of measuring the moisture content of dry-cured hams can help in regulating the moisture content during drying to produce high-quality dry-cured hams.

Estimation of Soil Moisture Content of Farmlands Based on AEA Method of GPR

We used ground penetrating radar (GPR) to estimate the soil moisture content and its spatial distribution of farmland under different tillage modes in this paper. GPR data are collected on the farmlands with no-tillage (NT) and deep ploughing (DP), respectively. The average envelope amplitude (AEA) of GPR signal is used to estimate the soil moisture content which is constrained by time domain reflectometry (TDR) measurement. The findings of the field test indicate that the error of the moisture content obtained by AEA method is in a reasonable range. In the shallow layer of farmland, the AEA method of GPR for detecting the soil moisture content is feasible and different tillage modes can affect the change of soil moisture content.

Research on Changes in the Phase Shift Angle and Admittance of the Cellulose–Bio-Oil Composite under the Influence of Increasing Moisture during the Long-Term Operation of Power Transformers

In this study, the temperature–frequency dependencies of the pressboard–bio-oil–water nanoparticle composite’s fundamental parameters—phase shift angle and admittance—were examined using the Frequency Domain Spectroscopy method. Measurements were conducted in a frequency range of 10−4 Hz to 5 × 103 Hz at temperatures ranging from 293.15 K to 343.15 K, with a step of 10 K. The temperature stabilization accuracy was less than ±0.05 K. A total of 15 Arrhenius plots were determined for various phase shift angle values, from which 15 values and the average activation energy of relaxation time were determined. Similarly, the values of the activation energy of admittance relaxation time and the activation energy of admittance were determined. It was established that all three average values are identical within the bounds of uncertainty. Based on 45 values, a generalized activation energy was determined, with a value of ΔW ≈ (1.032 ± 0.0196) eV. Using the generalized activation energy value, the phase shift angle curves determined for all temperatures were recalculated to a temperature of 293.15 K. It was found that after conversion, all curves perfectly overlap. A similar operation was carried out for the frequency dependencies of admittance. In this case, too, the recalculated dependencies perfectly match. This means that the shape of the frequency dependencies of the phase shift angle and admittance depends solely on the moisture content in the pressboard–bio-oil–water nanodrop composite. The position of the curves in doubly logarithmic coordinates depends only on temperature and is determined by the generalized activation energy Using the generalized activation energy determined in this study will allow for the development of accurate methods for estimating moisture content in cellulose insulation of power transformers containing bio-oil. This will contribute to the detection of critical moisture content, which is about 5% by weight, and to the elimination of the risk, associated with such a high moisture content, of catastrophic failure of power transformers.

A comparative analysis of phytochemical and biochemical activities of two varieties of mango seed kernels

The present research work embraces the study of moisture content, yield percentage, organoleptic properties, and phytochemical and biochemical estimation of two varieties of mango seed kernel using different solvents with various polarity such as n-hexane, chloroform, ethyl acetate, acetone, methanol, and aqueous. Moisture content and percentage of yield were also calculated which were more in Banganpalle variety. The qualitative phytochemical and biochemical screening indicated the presence and amount of phenol, flavonoid, tannin, quinone, oxalate, saponin, sterol, triterpene, carbohydrate, protein, and lipid. The quantitative analysis result showed that phytochemicals like phenol, flavonoids, and tannin were present in higher content in the seed kernel of Neelam as compared to Banganpalle. The results from biochemical tests showed a high content of carbohydrates, proteins, and lipids in Neelam. The present study therefore conclusively points out that the mango seed kernel is a good source of various pharmacologically active substances that make a potential antioxidant, anti-inflammatory, and other therapeutic uses.

Investigation on the Integration of Low-Cost NIR Spectrometers in Mill Flour Industries for Protein, Moisture and Ash Content Estimation.

The flour milling industry-a vital component of global food production-is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality and sustainability in flour production. The accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient and cost-effective method for measuring the aforementioned essential parameters in wheat and flour by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate the protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance the parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the studied parameters.

Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers

AbstractDrying plays a crucial role in preserving the quality of agricultural products. Nevertheless, suboptimal conditions in drying systems have an adverse effect on drying characteristics and energy efficiency. Machine learning approaches are innovative and reliable that have been successfully used to solve such challenges and achieve optimization in drying processes. In this study, five machine learning approaches (multilayer perceptron [MLP], gaussian processes [GP], support vector regression [SVR], k‐nearest neighbors [kN], and random forest [RF]) were used to estimate moisture content and moisture ratio of apricot in five various dryers (convective [CV], microwave [MW], infrared [IR], microwave‐convective [MW‐CV], and infrared‐convective [IR‐CV]). Also, the values of specific energy consumption (SEC) and effective moisture diffusivity (Deff) were calculated in these dryers. Accordingly, the best result of the Deff (3.14 × 10−10 m2/s) and the minimum value of the drying time (130 min) and SEC (18.67 MJ/kg) were obtained using MW‐CV hybrid dryer. While the lowest values of Deff (2.09 × 10−11 m2/s) and highest drying time (18.5 h) and SEC (209.34 MJ/kg) were detected in CV dryer at 50°C. The best correlation coefficients (R) for the estimation of moisture content were gained using RF technique for k‐fold cross validation and train‐test split with the values of 0.9908 and 0.9912, respectively. Moreover, moisture ratio results showed that the MLP achieved the highest R value over 0.9985 for both validation methodologies. In the discrimination of the drying methods, the MLP had the greatest accuracy as 82.00% and 86.00% for k‐fold cross validation and train‐test split, respectively. The results showed that the RF and ML models could potentially be used for estimation and discrimination for drying applications.Practical ApplicationsRecently, there has been an increased interest in healthy food choices such as foodstuffs, snacks, and dried products. This trend has captured the attention of both dietitians and conscious consumers. Apricots are a prime example of a valuable dried product that can be dry in various conditions. Machine learning techniques can be used for rapid and non‐destructive determination of drying characteristics and such techniques yield objective and accurate results. Present findings revealed that texture machine learning models could be used as an effective and reliable discrimination tool for dried products.

Research on robust inversion model of soil moisture content based on GF-1 satellite remote sensing

This study aims to improve the accuracy and applicability of traditional linear regression and machine learning algorithms for monitoring soil moisture content by satellite remote sensing. Shahaoqu Experimental Station within Jiefangzha Irrigation District of Hetao Irrigation Area is selected as the study area. The dataset includes GF-1 satellite remote sensing images and measured soil moisture content. The study employs the best subset selection to determine the combination of sensitive variables for soil moisture content at different soil depths. Additionally, robust regression theory is introduced to traditional linear regression and machine learning algorithms to construct soil moisture content inversion models at different soil depths. The results show that all three improved robust algorithms effectively reduce the influence of outliers, improve the accuracy and stability of these inversion models. The improved robust nonlinear algorithm outperforms the improved robust linear algorithm. Compared with the unprocessed algorithm, at the soil depth of 0–20 cm, Robust Extreme Learning Machine (RELM) algorithm is the most optimal, with the validation set's R2adj increasing from 0.555 to 0.696 (approximately 25.33%) and the RMSE decreasing from 0.025 to 0.018 (approximately 26.54%). Robust Least Squares Support Vector Machine (RLSSVM) algorithm is the second best, with the validation set's R2adj increasing from 0.502 to 0.641 (approximately 27.75%) and the RMSE decreasing from 0.026 to 0.020 (approximately 24.59%). Robust Linear Regression (LMS-RLS) algorithm is relatively the worst, with the validation set's R2adj increasing from 0.491 to 0.659 (approximately 34.05%) and the RMSE decreasing from 0.026 to 0.019 (approximately 27.21%). The sensitivity of the five soil depths to soil moisture content follows the order of 0–20 cm, 20–40 cm, 0–60 cm, 0–40 cm, and 40–60 cm. The improved robust regression algorithms can improve the accuracy and applicability of soil moisture content estimation and provide a reference for monitoring soil moisture content using GF-1 satellite.

Moisture content estimation of green softwood logs of three species based on measurements of flexural vibration

The moisture contents of sugi (Cryptomeria japonica), todomatsu (Abies sachalinensis) and hinoki (Chamaecyparis obtusa) logs were estimated using a method of moisture content estimation proposed in our previous study. In the course of estimation, it was revealed that the regression line of the correlation between specific dynamic Young’s modulus (E/ρ) and tangent loss (tan δ) of green wood was different from that of moisture-conditioned wood and showed species dependency, both of which are not previously reported. Regression lines at the fiber saturation point (FSP) were constructed for each species by measuring E/ρ and tan δ from the flexural vibration of green small specimens and correcting the E/ρ values at their own moisture contents into E/ρ values at the FSP. The correlation of green wood in this study was different from that reported in previous studies of moisture-conditioned wood near the FSP. The correlations of sugi and hinoki were similar, whereas those of sugi and todomatsu were different despite no previous report of species dependency in air-dried wood. The moisture contents 86 logs (not those used to prepare small specimens) were estimated using regression lines of each species. The standard deviation of the difference between the estimated moisture content and the measured moisture content was 15.7%. A systematic error of 25.9% in moisture content was attributed to the different methods of specimen support used for small specimens and logs.

Inosine 5′- monophosphate derived umami taste intensity of beef determination by electrochemistry and chromatography

The umami sensation contributes to beef taste and acceptability. Inosine 5′- monophosphate (IMP), the most abundant 5′-ribonucleotide in meat, is known to impart an umami taste without the undesired side effects commonly associated with glutamate. Nevertheless, the investigation of IMP's role in beef flavor has thus far been overlooked. Traditional methods for detecting IMP have relied on liquid chromatography coupled with ultraviolet spectroscopy or mass spectrometry techniques. However, these methods are not practical for production settings due to the complexity and resource demands of sophisticated laboratory techniques. Alternative methods like cyclic voltammetry might offer more practical solutions for rapidly detecting IMP. The objectives of this study were to evaluate the efficiency of using electrochemistry and chromatography on differentiating beef strips spiked with different IMP contents. The IMP threshold was 0.30 mM determined by a trained panel using the Best Estimates Threshold method. Beef strip steaks of USDA Prime, Choice, and Select were spiked at 0.30 and 0.60 mM of IMP, based on green weight and an estimated moisture content of 65%. In this study, differences in the IMP content of steaks were not detected by liquid chromatography-mass spectrometry. However, the cyclic voltammetry approach differentiated IMP concentrations at 0.50 mM or above in aqueous solutions and subsequentially meat extracts from the buffered blank solutions. In conclusion, cyclic voltammetry holds potential as a rapid and effective approach for detecting IMP in beef and other meat products, offering promising applications for future research.

A new long short-term memory based approach for soil moisture prediction

Water scarcity is becoming more severe around the world as a result of suboptimal irrigation practices. Effective irrigation scheduling necessitates an estimation of future soil moisture content. This study presents deep learning models such as CNN-LSTM, a hybrid Deep Learning model that predicts future soil moisture using climate and soil information, including past soil moisture content. The study also investigates the appropriate number of observations and data sampling rate required to predict the next day’s soil moisture value. In terms of MSE, MAE, RMSE, and R 2 , the hybrid CNN-LSTM model is compared to standalone LSTM and Bi-LSTM models. The LSTM model achieved an MSE of 0.2471, MAE of 0.1978, RMSE of 0.4971, and R 2 of 0.9714. The LSTM model outperformed the Bi-LSTM model, which had an MSE of 0.3036, MAE of 0.3248, RMSE of 0.5510, and R 2 of 0.9614. With an MSE of 0.1348, MAE of 0.1868, RMSE of 0.3672, and R 2 of 0.9838, the hybrid CNN-LSTM model outperformed the LSTM. Our findings suggest that deep learning models, particularly the Convolutional LSTM, hold great potential for predicting soil moisture accurately. The Convolutional LSTM model’s superior performance can be attributed to its ability to capture spatial dependencies in soil moisture data. Furthermore, the results show that for better prediction, sub-hourly data samples from the previous three days should be considered.

Utilization of Remote Sensing Data in the Simulation of the Water and Heat Regime of Land Areas: A Review of Publications

The article presents a revue of the estimates of soil surface moisture, soil water content, and evapotranspiration as elements of water and heat regimes of land surface areas at various spatial scales, made with the use of remote sensing data for Earth in various spectral ranges. In most cases considered in the study, such estimates were obtained with the use of land surface models. A special section is focused on the results of estimating soil surface moisture and water content using satellite data from microwave range, including radar data. Estimates of soil surface moisture content obtained with the use of neural networks are presented. A brief description is given to international hydrological–atmosphere experiments carried out under world research projects aimed to obtain data on the processes of moisture and heat exchange between the land surface and the surface atmosphere layer. Land surface, satellite, and model databases that have been formed using the results of studies in the field under consideration since the mid-1980s are reviewed. Prospects of further studies based on the development of new multispectral instrumentation, the creation of new databases, and the use of a new generation of satellites—global-coverage microsatellites with high-resolution sensors are presented.