Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves

An increasing global aridification due to climate change has made the health monitoring of vegetation indispensable to maintaining the food supply chain. Cost-effective and smart irrigation systems are required not only to ensure the efficient distribution of water, but also to track the moisture of plant leaves, which is an important marker of the overall health of the plant. This paper presents a novel electromagnetic method to monitor the water content (WC) and characterisation in plant leaves using the absorption spectra of water molecules in the terahertz (THz) frequency for four consecutive days. We extracted the material properties of leaves of eight types of pot herbs from the scattering parameters, measured using a material characterisation kit in the frequency range of 0.75 to 1.1 THz. From the computed permittivity, it is deduced that the leaf specimens increasingly become transparent to the THz waves as they dry out with the passage of days. Moreover, the loss in weight and thickness of leaves were observed due to the natural evaporation of leaf moisture cells and change occurred in the morphology of fresh and water-stressed leaves. It is also illustrated that loss observed in WC on day 1 was in the range of 5% to 22%, and increased from 83.12% to 99.33% on day 4. Furthermore, we observed an exponential decaying trend in the peaks of the real part of the permittivity from day 1 to 4, which was reminiscent of the trend observed in the weight of all leaves. Thus, results in paper demonstrated that timely detection of water stress in leaves can help to take proactive action in relation to plants health monitoring, and for precision agriculture applications, which is of high importance to improve the overall productivity.

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Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves

10.20944/preprints201907.0125.v1 ◽

2019 ◽

Author(s):

Adnan Zahid ◽

Hasan T. Abbas ◽

Muhammad A. Imran ◽

Khalid A. Qaraqe ◽

Akram Alomainy ◽

...

Keyword(s):

Water Content ◽

Health Monitoring ◽

Precision Agriculture ◽

Estimation Method ◽

Cost Effective ◽

Plant Leaves ◽

Terahertz Waves ◽

Living Plant ◽

Dry Out ◽

Important Marker

An increasing global aridification due to climate change has made the health monitoring of vegetation indispensable to maintaining the food supply chain. Cost-effective and smart irrigation systems are required not only to ensure the efficient distribution of water, but also to track the moisture of plant leaves, which is an important marker of the overall health of the plant. This paper presents a novel electromagnetic method to monitor the water content (WC) and characterization in plant leaves utilizing the absorption spectra of water molecules in the terahertz (THz) frequency for four consecutive days. We extracted the material properties of leaves of eight types of pot herbs from the scattering parameters, measured using a material characterization kit in the frequency range of 0.75 to 1.1 THz. From the computed permittivity, it is deduced that the leaf specimens increasingly become transparent to the THz waves as they dry out with the passage of days. Moreover, the loss in weight and thickness of leaves were observed due to the natural evaporation of leaf moisture cells and change occurred in the morphology of fresh and water-stressed leaves. It is also illustrated that loss observed in WC on day 1 was in the range of 5\% to 22\%, and increased from 83.12\% to 99.33\% on day 4. Furthermore, we observed an exponential decaying trend in the peaks of the real part of the permittivity from day 1 to 4, which was reminiscent of the trend observed in the weight of all leaves. Thus, results in paper demonstrated that timely detection of water stress in leaves can help to take proactive action in relation to plants health monitoring, and for precision agriculture applications, which is of high importance to improve the overall productivity.

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Machine learning driven non-invasive approach of water content estimation in living plant leaves using terahertz waves

Plant Methods ◽

10.1186/s13007-019-0522-9 ◽

2019 ◽

Vol 15 (1) ◽

Cited By ~ 7

Author(s):

Adnan Zahid ◽

Hasan T. Abbas ◽

Aifeng Ren ◽

Ahmed Zoha ◽

Hadi Heidari ◽

...

Keyword(s):

Machine Learning ◽

Water Content ◽

Operating Time ◽

Computational Time ◽

Support Vector ◽

Terahertz Waves ◽

Living Plant ◽

Invasive Approach ◽

Non Invasive ◽

Precise Estimation

Abstract Background The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time–frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). Results The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves. Conclusion Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.

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Temporal stability of electrical conductivity in a sandy soil

International Agrophysics ◽

10.1515/intag-2016-0005 ◽

2016 ◽

Vol 30 (3) ◽

pp. 349-357 ◽

Cited By ~ 12

Author(s):

Aura Pedrera-Parrilla ◽

Eric C. Brevik ◽

Juan V. Giráldez ◽

Karl Vanderlinden

Keyword(s):

Electrical Conductivity ◽

Spatial Variability ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Precision Agriculture ◽

Temporal Stability ◽

Principal Component ◽

Model Parameters ◽

Soil Spatial Variability

Abstract Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

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An adaptive learning damage estimation method for structural health monitoring

Journal of Intelligent Material Systems and Structures ◽

10.1177/1045389x14522531 ◽

2014 ◽

Vol 26 (2) ◽

pp. 125-143 ◽

Cited By ~ 23

Author(s):

Debejyo Chakraborty ◽

Narayan Kovvali ◽

Antonia Papandreou-Suppappola ◽

Aditi Chattopadhyay

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Adaptive Learning ◽

Estimation Method ◽

Damage Estimation ◽

Structural Health

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Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

PLoS ONE ◽

10.1371/journal.pone.0257849 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0257849

Author(s):

Muhammad Wasim ◽

Ahsan Ali ◽

Mohammad Ahmad Choudhry ◽

Faisal Saleem ◽

Inam Ul Hasan Shaikh ◽

...

Keyword(s):

Kalman Filter ◽

Precision Agriculture ◽

Model Uncertainty ◽

Unscented Kalman Filter ◽

Estimation Method ◽

Wind Disturbance ◽

Model Uncertainties ◽

Nonlinear Simulation ◽

Lumped Model ◽

Disturbance Estimation

An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.

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Estimation Method of Maximum Inter-Story Drift Angle of Wood-Frame House using Two Accelerometers

Structural Health Monitoring ◽

10.21741/9781644901311-21 ◽

2021 ◽

Keyword(s):

Health Monitoring ◽

Main Shock ◽

Integration Method ◽

Damage Index ◽

Estimation Method ◽

High Accuracy ◽

2016 Kumamoto Earthquake ◽

Drift Angle ◽

Story Drift ◽

Wood Frame

Abstract. In April 2016, Kumamoto earthquake occurred in Japan and many wooden houses collapsed and many lives were lost because of the second and larger main shock. As a result, the need for Structural Health Monitoring (SHM) for wooden houses is receiving increased attention. In the SHM system, maximum inter-story drift angle is considered as the damage index. We assume that the first story of a wooden house will be damaged so that we need only to focus on the response of this first story. Hence, we install accelerometers on the ground floor and the second floor. In order to estimate the inter-story drift angle, we need to integrate the acceleration records twice. The simple double integration will result in erroneous results. Thus, in this paper, we propose the most appropriate integration method to estimate the maximum story drift angle with high accuracy using two accelerometers.

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Oxygen Saturation measurements from Green and Orange Illuminations of Multi-Wavelength Optoelectronic Patch Sensor

10.20944/preprints201810.0578.v1 ◽

2018 ◽

Author(s):

Samah Alharbi ◽

Sijung Hu ◽

David Mulvaney ◽

Laura Barrett ◽

Liangwen Yan ◽

...

Keyword(s):

Oxygen Saturation ◽

Pulse Oximetry ◽

Health Monitoring ◽

Healthy Subjects ◽

Cost Effective ◽

Physiological Monitoring ◽

Motion Artefacts ◽

Cost Effective Approach ◽

Multi Wavelength ◽

Experimental Protocols

Photoplethysmography (PPG) based pulse oximetry devices normally use red and infrared illuminations to obtain oxygen saturation (SpO2) readings. In addition, the presence of motion artefacts severely restricts the utility of pulse oximetry physiological measurements. In the current study, a combination of green and orange illuminations from a multi-wavelength optoelectronic patch sensor (mOEPS) was investigated in order to improve robustness to subjects’ movements in the extraction of SpO2 measurement. Two experimental protocols with 31 healthy subjects were designed to determine SpO2 measurement. The datasets for the first protocol were collected from 15 subjects at rest, with the subjects free to move their hands. The datasets for the second protocol with 16 subjects were collected during cycling and running exercises. The results showed good agreements with SpO2 measurements (r = 0.98) in the both protocols. The outcomes promise a robust and cost-effective approach of physiological monitoring with the prospect of providing health monitoring that does not restrict user physical movements.

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Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence

Remote Sensing ◽

10.3390/rs13204125 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4125

Author(s):

Weiping Kong ◽

Wenjiang Huang ◽

Lingling Ma ◽

Lingli Tang ◽

Chuanrong Li ◽

...

Keyword(s):

Vertical Distribution ◽

Water Content ◽

Near Infrared ◽

Field Experiments ◽

Estimation Method ◽

Middle Layer ◽

Leaf Water ◽

Leaf Water Content ◽

Growth Stages ◽

Spectral Indices

Monitoring vertical profile of leaf water content (LWC) within wheat canopies after head emergence is vital significant for increasing crop yield. However, the estimation of vertical distribution of LWC from remote sensing data is still challenging due to the effects of wheat spikes and the efficacy of sensor measurement from the nadir direction. Using two-year field experiments with different growth stages after head emergence, N rates, wheat cultivars, we investigated the vertical distribution of LWC within canopies, the changes of canopy reflectance after spikes removal, the relationship between spectral indices and LWC in the upper-, middle- and bottom-layer. The interrelationship among vertical LWC were constructed, and four ratio of reflectance difference (RRD) type of indices were proposed based on the published WI and NDWSI indices to determine vertical distribution of LWC. The results indicated a bell shape distribution of LWC in wheat plants with the highest value appeared at the middle layer, and significant linear correlations between middle-LWC vs. upper-LWC and middle-LWC vs. bottom-LWC (r ≥ 0.92) were identified. The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation. Spectral indices at the middle layer outperformed the other two layers in LWC assessment and were less susceptible to wheat spikes effects, in particular, the newly proposed narrow-band WI-4 and NDWSI-4 indices exhibited great potential in tracking the changes of middle-LWC (R2 = 0.82 and 0.84, respectively). By taking into account the effects of wheat spikes and the interrelationship of vertical LWC within canopies, an indirect induction strategy was developed for modeling the upper-LWC and bottom-LWC. It was found that the indirect induction models based on the WI-4 and NDWSI-4 indices were more effective than the models obtained from conventional direct estimation method, with R2 of 0.78 and 0.81 for the upper-LWC estimation, and 0.75 and 0.74 for the bottom-LWC estimation, respectively.

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