Microwave Transmittance Technique Using Microstrip Patch Antennas, as a Non-Invasive Tool to Determine Soil Moisture in Rhizoboxes

Herrmann, P. S. de P.; Sydoruk, V. A.; Porto, Felipe Nieves Marques

doi:10.3390/s20041166

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…The MAE values of KNN, SVM and Linear Regression are 9.27, 12.74 and 3.54, respectively. Furthermore, the R squared value, i.e., the coefficient of determination, a parameter that represents the goodness of fit and used in prediction, is determined for the different models [ 28 , 29 ]. The R 2 value equal to 1 represents that the model perfectly fits the data.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor

Uthayakumar

Mohan

Khoo

et al. 2022

Sensors

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In this paper, machine learning models for an effective estimation of soil moisture content using a microwave short-range and wideband radar sensor are proposed. The soil moisture is measured as the volumetric water content using a short-range off-the-shelf radar sensor operating at 3–10 GHz. The radar captures the reflected signals that are post processed to determine the soil moisture which is mapped to the input features extracted from the reflected signals for the training of the machine learning models. In addition, the results are compared and analyzed with a contact-based Vernier soil sensor. Different machine learning models trained using neural network, support vector machine, linear regression and k-nearest neighbor are evaluated and presented in this work. The efficiency of the model is computed using root mean square error, co-efficient of determination and mean absolute error. The RMSE and MAE values of KNN, SVM and Linear Regression are 11.51 and 9.27, 15.20 and 12.74, 3.94 and 3.54, respectively. It is observed that the neural network gives the best results with an R2 value of 0.9894. This research work has been carried out with an intention to develop cost-effective solutions for common users such as agriculturists to monitor the soil moisture conditions with improved accuracy.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor

Uthayakumar

Mohan

Khoo

et al. 2022

Sensors

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“…Rhizoboxes are usually small devices designed for laboratory/greenhouse experiments and are made of plastic or composite materials; they allow for root growth to be observed through a transparent layer and for different parts of the rhizosphere to be sampled for root, soil, microbial, or chemical analysis. Common applications are the assessment of root exudates [ 170 , 171 , 172 , 173 ], composition of bacterial communities and their changes [ 170 , 172 , 174 , 175 ], enzyme assays [ 174 , 175 ], gene abundance [ 172 , 174 ], root growth characteristics [ 175 , 176 , 177 , 178 , 179 , 180 ], nutrient status [ 175 , 176 , 180 , 181 ], and metal toxicity [ 171 , 173 , 182 ]. Tao et al [ 171 ] combined the rhizobox method with 13 C labelling to estimate CO 2 assimilation and transfer to the soil rhizosphere in the form of root exudates that have an effect on cadmium solubility.…”

Section: Methods Related To the Specific Environments And Nutrients R...mentioning

confidence: 99%

Plant Nutrition—New Methods Based on the Lessons of History: A Review

Kulhánek,

Asrade,

Suran

et al. 2023

Plants

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As with new technologies, plant nutrition has taken a big step forward in the last two decades. The main objective of this review is to briefly summarise the main pathways in modern plant nutrition and attract potential researchers and publishers to this area. First, this review highlights the importance of long-term field experiments, which provide us with valuable information about the effects of different applied strategies. The second part is dedicated to the new analytical technologies (tomography, spectrometry, and chromatography), intensively studied environments (rhizosphere, soil microbial communities, and enzymatic activity), nutrient relationship indexes, and the general importance of proper data evaluation. The third section is dedicated to the strategies of plant nutrition, i.e., (i) plant breeding, (ii) precision farming, (iii) fertiliser placement, (iv) biostimulants, (v) waste materials as a source of nutrients, and (vi) nanotechnologies. Finally, the increasing environmental risks related to plant nutrition, including biotic and abiotic stress, mainly the threat of soil salinity, are mentioned. In the 21st century, fertiliser application trends should be shifted to local application, precise farming, and nanotechnology; amended with ecofriendly organic fertilisers to ensure sustainable agricultural practices; and supported by new, highly effective crop varieties. To optimise agriculture, only the combination of the mentioned modern strategies supported by a proper analysis based on long-term observations seems to be a suitable pathway.

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“…However, these methods are typically used for fixed position detection and lack imaging ability. Microwaves have been also used in determining soil moisture in rhizoboxes [37]. A microwave resonator system was verified to have the ability to determine the plant biomass non-invasively [38], while it lacks the ability of root phenotyping.…”

Section: Methods Advantage Limitmentioning

confidence: 99%

Ultra-Wideband Microwave Imaging System for Root Phenotyping

Shi

Mukerjee

et al. 2022

Sensors

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The roots are a vital organ for plant growth and health. The opaque surrounding environment of the roots and the complicated growth process means that in situ and non-destructive root phenotyping face great challenges, which thus spur great research interests. The existing methods for root phenotyping are either unable to provide high-precision and high accuracy in situ detection, or they change the surrounding root environment and are destructive to root growth and health. Thus,we propose and develop an ultra-wideband microwave scanning method that uses time reversal to achieve in situ root phenotyping nondestructively. To verify the method’s feasibility, we studied an electromagnetic numerical model that simulates the transmission signal of two ultra-wideband microwave antennas. The simulated signal of roots with different shapes shows the proposed system’s capability to measure the root size in the soil. Experimental validations were conducted considering three sets of measurements with different sizes, numbers and locations, and the experimental results indicate that the developed imaging system was able to differentiate root sizes and numbers with high contrast. The reconstruction from both simulations and experimental measurements provided accurate size estimation of the carrots in the soil, which indicates the system’s potential for root imaging.

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Microwave Transmittance Technique Using Microstrip Patch Antennas, as a Non-Invasive Tool to Determine Soil Moisture in Rhizoboxes

Cited by 11 publications

References 22 publications

Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor

Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor

Plant Nutrition—New Methods Based on the Lessons of History: A Review

Ultra-Wideband Microwave Imaging System for Root Phenotyping

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