The Design and Experimentation of a Corn Moisture Detection Device Based on Double Capacitors.

Detecting the moisture content of grain accurately and rapidly has important significance for harvesting, transport, storage, processing, and precision agriculture. There are some problems with the slow detection speeds, unstable detection, and low detection accuracy of moisture contents in corn harvesters. In that case, an online moisture detection device was designed, which is based on double capacitors. A new method of capacitance complementation and integration was proposed to eliminate the limitation of single data. The device is composed of a sampling mechanism and a double-capacitor sensor consisting of a flatbed capacitor and a cylindrical capacitor. The optimum structure size of the capacitor plates was determined by simulation optimization. In addition to this, the detection system with software and hardware was developed to estimate the moisture content. Indoor dynamic measurement tests were carried out to analyze the influence of temperature and porosity. Based on the influencing factors and capacitance, a model was established to estimate the moisture content. Finally, the support vector machine (SVM) regressions between the capacitance and moisture content were built up so that the R2 values were more than 0.91. In the stability test, the standard deviation of the stability test was 1.09%, and the maximum relative error of the measurement accuracy test was 1.22%. In the dynamic verification test, the maximum error of the measurement was 4.62%, less than 5%. It provides a measurement method for the accurate, rapid, and stable detection of the moisture content of corn and other grains.

High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images.

Chlorophyll content reflects plants' photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) was compared. The most sensitive spectral (SVIs) and color vegetation indices (CVIs) for chlorophyll content were extracted and evaluated as reliable estimators of chlorophyll content. Using an ASD FieldSpec 4 Hi-Res spectroradiometer and a portable red, green, and blue (RGB) camera, 3600 hyperspectral reflectance measurements and 800 RGB images were acquired from lettuce grown across a gradient of nutrient levels. Ground measurements of leaf chlorophyll were acquired using an SPAD-502 m calibrated via laboratory chemical analyses. The results revealed a strong relationship between chlorophyll content and SPAD-502 readings, with an R2 of 0.95 and a correlation coefficient (r) of 0.975. The developed AutoML models outperformed all traditional models, yielding the highest values of the coefficient of determination in prediction (Rp2) for all vegetation indices (VIs). The combination of SVIs and CVIs achieved the best prediction accuracy with the highest Rp2 values ranging from 0.89 to 0.98, respectively. This study demonstrated the feasibility of spectral and color vegetation indices as estimators of chlorophyll content. Furthermore, the developed AutoML models can be integrated into embedded devices to control nutrient cycles in aquaponics systems.

Preliminary study on the diagnosis of NK stress based on the puncture mechanical characteristics of cucumber stem.

To investigate the relationship between stem puncture mechanical characteristics and NK stress diagnosis, the microstructure, surface morphology, cellulose and lignin content, puncture mechanical characteristics, and epidermal cell morphology of cucumber stems were measured herein. The results indicated that the middle stem, which had a diameter of approximately 7000 µm, was more suitable for puncturing due to its lower amount of epidermal hair, and its gradual regularity in shape. Further, the cucumber stems were protected from puncture damage due to their ability to rapidly heal within 25 h.. The epidermal penetration of the cucumber stems increased with the increase in cellulose and lignin, though cellulose played a more decisive role. The epidermal break distance increased with an increase in N application and decreased with an increase in K+ application, but the change in intercellular space caused by K+ supply was the most critical factor affecting the epidermal break distance. In addition, a decrease in K+ concentration led to a decrease in epidermal brittleness, whereas the factors affecting epidermal toughness were more complex. Finally, we found that although the detection of epidermal brittleness and toughness on nutrient stress was poor under certain treatment, the puncture mechanical characteristics of the stem still had a significant indicative effect on N application rate. Therefore, elucidating of the relationship between the puncture mechanical characteristics of the stems and crop nutritional stress is not only beneficial for promoting stem stress physiology research but also for designing on-site nutritional testing equipment in the future.

Detection Method of Fungal Spores Based on Fingerprint Characteristics of Diffraction-Polarization Images.

The most significant aspect of promoting greenhouse productivity is the timely monitoring of disease spores and applying proactive control measures. This paper introduces a method to classify spores of airborne disease in greenhouse crops by using fingerprint characteristics of diffraction-polarized images and machine learning. Initially, a diffraction-polarization imaging system was established, and the diffraction fingerprint images of disease spores were taken in polarization directions of 0°, 45°, 90° and 135°. Subsequently, the diffraction-polarization images were processed, wherein the fingerprint features of the spore diffraction-polarization images were extracted. Finally, a support vector machine (SVM) classification algorithm was used to classify the disease spores. The study's results indicate that the diffraction-polarization imaging system can capture images of disease spores. Different spores all have their own unique diffraction-polarization fingerprint characteristics. The identification rates of tomato gray mold spores, cucumber downy mold spores and cucumber powdery mildew spores were 96.02%, 94.94% and 96.57%, respectively. The average identification rate of spores was 95.85%. This study can provide a research basis for the identification and classification of disease spores.

Deep-Learning Terahertz Single-Cell Metabolic Viability Study.

Cell viability assessment is critical, yet existing assessments are not accurate enough. We report a cell viability evaluation method based on the metabolic ability of a single cell. Without culture medium, we measured the absorption of cells to terahertz laser beams, which could target a single cell. The cell viability was assessed with a convolution neural classification network based on cell morphology. We established a cell viability assessment model based on the THz-AS (terahertz-absorption spectrum) results as y = a = (x - b)c, where x is the terahertz absorbance and y is the cell viability, and a, b, and c are the fitting parameters of the model. Under water stress the changes in terahertz absorbance of cells corresponded one-to-one with the apoptosis process, and we propose a cell 0 viability definition as terahertz absorbance remains unchanged based on the cell metabolic mechanism. Compared with typical methods, our method is accurate, label-free, contact-free, and almost interference-free and could help visualize the cell apoptosis process for broad applications including drug screening.

Invasive Wedelia trilobata Performs Better Than Its Native Congener in Various Forms of Phosphorous in Different Growth Stages.

At present, many hypotheses have been proposed to explain the mechanism of alien plants' successful invasion; the resource fluctuations hypothesis indicates that nutrient availability is a main abiotic factor driving the invasion of alien plants. Higher phosphorus utilization and absorption efficiency might be one of the important reasons for alien plants successful invasion. Wedelia trilobata, one of the notorious invasive weeds in China, possesses a strong ability to continue their development under infertile habitats. In this study, firstly, W. trilobata and its native congener, W. chinensis, were grown in various phosphorus forms to test their absorption efficiency of phosphorus. Secondly, the different responses of W. trilobata and W. chinensis to the insoluble phosphorus in three growth stages (at 30, 60, and 150 days cultivation) were also tested. The results showed that the growth rate, root morphology, and phosphorus absorption efficiency of W. trilobata under various insoluble, organic, or low phosphorus conditions were significantly higher than that of W. chinensis. During the short-term cultivation period (30 d), the growth of W. trilobata under insoluble and low phosphorus treatments had no significant difference, and the growth of W. trilobata in insoluble phosphorus treatment also had no significant effect in long-term cultivation (60 and 150 d). However, the growth of W. chinensis in each period under the conditions of insoluble and low phosphorus was significantly inhibited throughout these three growth stages. Therefore, invasive W. trilobata had a higher phosphorus utilization efficiency than its native congener. This study could explain how invasive W. trilobata performs under nutrient-poor habitats, while also providing favorable evidence for the resource fluctuations hypothesis.

Study on the detection of water status of tomato (Solanum lycopersicum L.) by multimodal deep learning.

Water plays a very important role in the growth of tomato (Solanum lycopersicum L.), and how to detect the water status of tomato is the key to precise irrigation. The objective of this study is to detect the water status of tomato by fusing RGB, NIR and depth image information through deep learning. Five irrigation levels were set to cultivate tomatoes in different water states, with irrigation amounts of 150%, 125%, 100%, 75%, and 50% of reference evapotranspiration calculated by a modified Penman-Monteith equation, respectively. The water status of tomatoes was divided into five categories: severely irrigated deficit, slightly irrigated deficit, moderately irrigated, slightly over-irrigated, and severely over-irrigated. RGB images, depth images and NIR images of the upper part of the tomato plant were taken as data sets. The data sets were used to train and test the tomato water status detection models built with single-mode and multimodal deep learning networks, respectively. In the single-mode deep learning network, two CNNs, VGG-16 and Resnet-50, were trained on a single RGB image, a depth image, or a NIR image for a total of six cases. In the multimodal deep learning network, two or more of the RGB images, depth images and NIR images were trained with VGG-16 or Resnet-50, respectively, for a total of 20 combinations. Results showed that the accuracy of tomato water status detection based on single-mode deep learning ranged from 88.97% to 93.09%, while the accuracy of tomato water status detection based on multimodal deep learning ranged from 93.09% to 99.18%. The multimodal deep learning significantly outperformed the single-modal deep learning. The tomato water status detection model built using a multimodal deep learning network with ResNet-50 for RGB images and VGG-16 for depth and NIR images was optimal. This study provides a novel method for non-destructive detection of water status of tomato and gives a reference for precise irrigation management.

Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques.

As rice is one of the world's most important food crops, protecting it from fungal diseases is very important for agricultural production. At present, it is difficult to diagnose rice fungal diseases at an early stage using relevant technologies, and there are a lack of rapid detection methods. This study proposes a microfluidic chip-based method combined with microscopic hyperspectral detection of rice fungal disease spores. First, a microfluidic chip with a dual inlet and three-stage structure was designed to separate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores in air. Then, the microscopic hyperspectral instrument was used to collect the hyperspectral data of the fungal disease spores in the enrichment area, and the competitive adaptive reweighting algorithm (CARS) was used to screen the characteristic bands of the spectral data collected from the spores of the two fungal diseases. Finally, the support vector machine (SVM) and convolutional neural network (CNN) were used to build the full-band classification model and the CARS filtered characteristic wavelength classification model, respectively. The results showed that the actual enrichment efficiency of the microfluidic chip designed in this study on Magnaporthe grisea spores and Ustilaginoidea virens spores was 82.67% and 80.70%, respectively. In the established model, the CARS-CNN classification model is the best for the classification of Magnaporthe grisea spores and Ustilaginoidea virens spores, and its F1-core index can reach 0.960 and 0.949, respectively. This study can effectively isolate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores, providing new methods and ideas for early detection of rice fungal disease spores.

Influences of oxygen and temperature interaction on the antibacterial activity, antioxidant activity, serum biochemical indices, blood indices and growth performance of crucian carp.

The well-being of fish used in aquaculture is of great interest. Oxygen and temperature are the main factors affecting the welfare of the crucian carp (carassius); however, there are few studies on the combined effects of these on the species. Therefore, this study investigated the impact of different temperatures (18 °C, 24 °C, 30 °C) and oxygen concentrations (2.1 mgL-1, 5.4 mgL-1, 9.3 mgL-1) on serum antibacterial activity, antioxidant activity, hematological parameters and growth performance of the crucian carp. The results showed that there were greater antibacterial properties under conditions of hypoxia at 18 °C (L18) and hyperoxia at 24 °C (H24). The activities of catalase, glutathione peroxidase and total superoxide dismutase were the highest at 24 °C under hypoxia and hyperoxia. In addition, the contents of glucose and total protein first increased and then decreased with the change of temperature; triglycerides were the lowest at 30 °C. The blood parameters of the carp were within a normal range at 24 °C; however, the growth rate was at its lowest under hypoxia treatment at 30 °C (L30). This study showed that high temperature impairs the antibacterial ability, antioxidant capacity and growth performance of the crucian carp, and high oxygen levels can alleviate these adverse reactions. This research provides a theoretical basis for subsequent aquaculture studies.

Correction: Detection of airborne pathogens with single photon counting and a real-time spectrometer on microfluidics.

Correction for 'Detection of airborne pathogens with single photon counting and a real-time spectrometer on microfluidics' by Ning Yang et al., Lab Chip, 2022, https://doi.org/10.1039/D2LC00934J.

Research on accurate perception and control system of fertilization amount for corn fertilization planter.

At present, there are excessive fertilizer use and poor uniformity of fertilizer discharge in corn fertilizer planter. The key difficulty is that accurate perception and control of fertilizer amount has not been solved. Aiming at the above problems, a set of accurate perception and control system applied to corn fertilization planter was studied. According to the difference in dielectric properties between fertilizer and air, a sensor for online detection of fertilizer amount based on capacitance method was designed. And the relationship model of mass flow rate for N, P, K fertilizer and capacitance output was established. In order to reduce the influence of pulsation on fertilization flow, a high-precision fertilizer flow control system for fertilization planter based on the fertilizer flow feedback and PID control method was designed. The validated results showed that the maximum measurement error between the relationship model and capacitance output was 3.75%. As the temperature rises from room temperature to 55°C, the differential capacitance change rate of the sensor was less than 3%. The steady-state error of fertilizer discharge was less than 2%. The field experiment of the accurate perception and control system for corn fertilization amount show that the electric drive fertilization system has good consistency, the maximum and average variation coefficient of fertilization were 3.74%, 1.6%, respectively, and the variable control accuracy was greater than 97%. The control accuracy of the grain spacing control by electric drive seed metering was 98%. Therefore, the precision fertilization control system in this study can realize high-precision and on-demand fertilization. It is of great significance to realize the intelligence and precision for corn fertilization planter.

Detection of airborne pathogens with single photon counting and a real-time spectrometer on microfluidics.

The common practice for monitoring pathogenic bioaerosols is to collect bioaerosols from air and then detect them, which lacks timeliness and accuracy. In order to improve the detection speed, here we demonstrate an innovative airflow-based optical detection method for directly identifying aerosol pathogens, and built a microfluidic-based counter composite spectrometer detection platform, which simplifies sample preparation and collection detection from two steps to one step. The method is based on principal component analysis and partial least squares discriminant analysis for particle species identification and dynamic transmission spectroscopy analysis, and single-photon measurement is used for particle counting. Compared with traditional microscopic counting and identification methods, the particle counting accuracy is high, the standard deviation is small, and the counting accuracy exceeds 92.2%. The setup of dynamic transmission spectroscopy analysis provides high-precision real-time particle identification with an accuracy rate of 93.75%. As the system is further refined, we also foresee potential applications of this method in agricultural disease control, environmental control, and infectious disease control in aerosol pathogen detection.

A Method for Capture and Detection of Crop Airborne Disease Spores Based on Microfluidic Chips and Micro Raman Spectroscopy.

Airborne crop diseases cause great losses to agricultural production and can affect people's physical health. Timely monitoring of the situation of airborne disease spores and effective prevention and control measures are particularly important. In this study, a two-stage separation and enrichment microfluidic chip with arcuate pretreatment channel was designed for the separation and enrichment of crop disease spores, which was combined with micro Raman for Raman fingerprinting of disease conidia and quasi identification. The chip was mainly composed of arc preprocessing and two separated enriched structures, and the designed chip was numerically simulated using COMSOL multiphysics5.5, with the best enrichment effect at W2/W1 = 1.6 and W4/W3 = 1.1. The spectra were preprocessed with standard normal variables (SNVs) to improve the signal-to-noise ratio, which was baseline corrected using an iterative polynomial fitting method to further improve spectral features. Raman spectra were dimensionally reduced using principal component analysis (PCA) and stability competitive adaptive weighting (SCARS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) were employed to identify fungal spore species, and the best discrimination effect was achieved using the SCARS-SVM model with 94.31% discrimination accuracy. Thus, the microfluidic-chip- and micro-Raman-based methods for spore capture and identification of crop diseases have the potential to be precise, convenient, and low-cost methods for fungal spore detection.

A Detection Method for Crop Fungal Spores Based on Microfluidic Separation Enrichment and AC Impedance Characteristics.

The timely monitoring of airborne crop fungal spores is important for maintaining food security. In this study, a method based on microfluidic separation and enrichment and AC impedance characteristics was proposed to detect spores of fungal pathogens that cause diseases on crops. Firstly, a microfluidic chip with tertiary structure was designed for the direct separation and enrichment of Ustilaginoidea virens spores, Magnaporthe grisea spores, and Aspergillus niger spores from the air. Then, the impedance characteristics of fungal spores were measured by impedance analyzer in the enrichment area of a microfluidic chip. The impedance characteristics of fungal spores were analyzed, and four impedance characteristics were extracted: absolute value of impedance (abs), real part of impedance (real), imaginary part of impedance (imag), and impedance phase (phase). Finally, based on the impedance characteristics of extracted fungal spores, K-proximity (KNN), random forest (RF), and support vector machine (SVM) classification models were established to classify the three fungal spores. The results showed that the microfluidic chip designed in this study could well collect the spores of three fungal diseases, and the collection rate was up to 97. The average accuracy of KNN model, RF model, and SVM model for the detection of three disease spores was 93.33, 96.44 and 97.78, respectively. The F1-Score of KNN model, RF model, and SVM model was 90, 94.65, and 96.18, respectively. The accuracy, precision, recall, and F1-Score of the SVM model were all the highest, at 97.78, 96.67, 96.69, and 96.18, respectively. Therefore, the detection method of crop fungal spores based on microfluidic separation, enrichment, and impedance characteristics proposed in this study can be used for the detection of airborne crop fungal spores, providing a basis for the subsequent detection of crop fungal spores.

Cucumber powdery mildew detection method based on hyperspectra-terahertz.

To explore the use of information technology in detecting crop diseases, a method based on hyperspectra-terahertz for detecting cucumber powdery mildew is proposed. Specifically, a method of effective hyperspectrum establishment, a method of spectral preprocessing, a method of selecting the feature wavelength, and a method of establishing discriminant models are studied. Firstly, the effective spectral information under visible light and near infrared is preprocessed by Savitzky-Golay (SG) smoothing, discrete wavelet transform, and move sliding window, which determine the optimal preprocessing method to be wavelet transform. Then stepwise discriminant analysis is used to select the feature wavelengths in the visible and near-infrared bands, forming the feature space. According to the features, a linear discriminant model is established for the wave bands, and the average recognition rate of cucumber powdery mildew is 93% in the whole wave band. The preprocessing method of terahertz data, the screening method of terahertz effective spectrum, the selection method of feature wavelength and the establishment method of classification model are studied. Python 3.8 is used to preprocess the terahertz raw data and establish the terahertz effective spectral data set for subsequent processing. Through iterative variable subset optimization - iterative retaining informative variables (IVSO-IRIV), the terahertz effective spectrum is screened twice to form the terahertz feature space. After that, the optimal regularization parameter and regularization solution methods are selected, and a sparse representation classification model is established. The accuracy of cucumber powdery mildew identification under the terahertz scale is 87.78%. The extraction and analysis methods of terahertz and hyperspectral feature images are studied, and more details of lesion samples are restored. Hence, the use of hyperspectral and terahertz technology can realize the detection of cucumber powdery mildew, which provides a basis for research on the hyperspectral and terahertz technology in detection of crop diseases.

A Rapid Detection Method for Fungal Spores from Greenhouse Crops Based on CMOS Image Sensors and Diffraction Fingerprint Feature Processing.

The detection and control of fungal spores in greenhouse crops are important for stabilizing and increasing crop yield. At present, the detection of fungal spores mainly adopts the method of combining portable volumetric spore traps and microscope image processing. This method is problematic as it is limited by the small field of view of the microscope and has low efficiency. This study proposes a rapid detection method for fungal spores from greenhouse crops based on CMOS image sensors and diffraction fingerprint feature processing. We built a diffraction fingerprint image acquisition system for fungal spores of greenhouse crops and collected diffraction fingerprint images of three kinds of fungal spores. A total of 13 diffraction fingerprint features were selected for the classification of fungal spores. These 13 characteristic values were divided into 3 categories, main bright fringe, main dark fringe, and center fringe. Then, these three features were calculated to obtain the Peak to Center ratio (PCR), Valley to Center ratio, and Peak to Valley ratio (PVR). Based on these features, logistics regression (LR), K nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classification models were built. The test results show that the SVM model has a better overall classification performance than the LR, KNN, and RF models. The average accuracy rate of the recognition of three kinds of fungal spores from greenhouse crops under the SVM model was 92.72%, while the accuracy rates of the LR, KNN, and RF models were 84.97%, 87.44%, and 88.72%, respectively. The F1-Score value of the SVM model was higher, and the overall average value reached 89.41%, which was 11.12%, 7.18%, and 5.57% higher than the LR, KNN, and RF models, respectively. Therefore, the method proposed in this study can be used for the remote identification of three fungal spores which can provide a reference for the identification of fungal spores in greenhouse crops and has the advantages of low cost and portability.

Airflow ejection-wrapped clamping type seedling picking method and parameter optimization.

Since the current clamp-type and push-out-type seedling picking method brought damage to seedlings, this study aimed to proposed an airflow ejection-wrapped clamping type seedling picking method, which used airflow to eject out seedling and the seedlings were wrapped clamped to reduce the damage of seedlings during seedling picking process. The parameter model was established through theoretical design, then the parameters were optimized through coupling simulation analysis, and the validity of these parameters was verified through experiments. We found that the diameter of the airflow nozzle was selected as 3.5 mm to match with the drainage outlet of the plug tray, and the airflow pressure which could eject out seedlings was calculated as 0.146 Mpa~0.315 Mpa on the basis of gas jet dynamic. The fluid-solid coupling simulation of airflow ejection in Comsol proposed that the seedlings could be ejected out under the airflow pressure was equal to or greater than 0.4 Mpa, and the airflow should be maintained for about 0.3 s to ensure the posture of the seedlings ejected out for better seedling clamping. The further fluid-discrete body simulation of airflow ejection by using Fluent-Edem coupling method indicated that the seedling was damaged under airflow pressure of 0.5 MPa, so the airflow pressure should be set as 0.4 MPa during seedling ejection process. Besides, a wrapped clamping type effector which clamped the seedlings from all sides in the form of flexible package was also designed to match with the airflow ejection method, and the RecurDyn-Edem coupling simulation showed that the end-effector could tightly clamp the seedling without damage when the angle between the clamping slices and the vertical direction was 8.5°. Finally, the airflow ejection-wrapped clamping type seedling picking device was manufactured, and the verification tests verified the simulation results. This research can provide some references for the automatic seedling picking technology.

A Rapid Detection Method for Tomato Gray Mold Spores in Greenhouse Based on Microfluidic Chip Enrichment and Lens-Less Diffraction Image Processing.

It is of great significance to find tomato gray mold in time and take corresponding control measures to ensure the production of tomato crops. This study proposed a rapid detection method for spores of Botrytis cinerea in green-house based on microfluidic chip enrichment and lens-free diffraction image processing. Microfluidic chip with a regular triangular inner rib structure was designed to achieve the enrichment of Botrytis cinerea spores. In order to obtain the diffraction image of the diseased spores, a lens-less diffraction imaging system was built. Furthermore, the collected spore diffraction images were processed and counted. The simulation results showed that the collection efficiency of 16 µm particles was 79%, 100%, and 89% at the inlet flow rate of 12, 14 and 16 mL/min, respectively. The experimental verification results were observed under a microscope. The results showed that when the flow rate of the microfluidic chip was 12, 14 and 16 mL/min, the collection efficiency of Botrytis cinerea spores was 70.65%, 87.52% and 77.96%, respectively. The Botrytis cinerea spores collected in the experiment were placed under a microscope for manual counting and compared with the automatic counting results based on diffraction image processing. A total of 10 sets of experiments were carried out, with an error range of the experiment was 5.13~8.57%, and the average error of the experiment was 6.42%. The Bland-Altman method was used to analyze two methods based on diffraction image processing and manual counting under a microscope. All points are within the 95% consistency interval. Therefore, this study can provide a basis for the research on the real-time monitoring technology of tomato gray mold spores in the greenhouse.

Highly sensitive detection of plant growth regulators by using terahertz time-domain spectroscopy combined with metamaterials.

The rapid and sensitive detection of plant-growth-regulator (PGR) residue is essential for ensuring food safety for consumers. However, there are many disadvantages in current approaches to detecting PGR residue. In this paper, we demonstrate a highly sensitive PGR detection method by using terahertz time-domain spectroscopy combined with metamaterials. We propose a double formant metamaterial resonator based on a split-ring structure with titanium-gold nanostructure. The metamaterial resonator is a split-ring structure composed of a titanium-gold nanostructure based on polyimide film as the substrate. Also, terahertz spectral response and electric field distribution of metamaterials under different analyte thickness and refractive index were investigated. The simulation results showed that the theoretical sensitivity of resonance peak 1 and peak 2 of the refractive index sensor based on our designed metamaterial resonator approaches 780 and 720 gigahertz per refractive index unit (GHz/RIU), respectively. In experiments, a rapid solution analysis platform based on the double formant metamaterial resonator was set up and PGR residues in aqueous solution were directly and rapidly detected through terahertz time-domain spectroscopy. The results showed that metamaterials can successfully detect butylhydrazine and N-N diglycine at a concentration as low as 0.05 mg/L. This study paves a new way for sensitive, rapid, low-cost detection of PGRs. It also means that the double formant metamaterial resonator has significant potential for other applications in terahertz sensing.

A study of plant growth regulators detection based on terahertz time-domain spectroscopy and density functional theory.

Terahertz technology is receiving increasing attention for its use as an efficient non-destructive, non-contact and label-free optical method for qualitative and quantitative detection. The aim of this study was to develop a chemical analysis methodology based on terahertz time-domain spectra that could be used to detect plant growth regulators, such as glyphosine, naphthaleneacetic acid, daminozide and gibberellic acid. The THz fingerprint spectra of these four PGRs were located in the 0.3-1.8 THz, with the peaks of glyphosine at 0.32, 0.49, 0.74, 0.87, 0.96, and 1.49 THz; daminozide at 0.33, 0.39, 0.55, 0.67, and 1.17 THz; gibberellic acid at 0.46, 0.58, 0.92, and 1.38 THz and naphthaleneacetic acid at 0.43, 0.57, 0.73, and 0.90 THz. The results showed that these four plant growth regulators exhibited numerous distinct spectral features in frequency-dependent absorption spectra, which demonstrated the qualitative capacity of terahertz time-domain. The origin of the observed terahertz absorption peaks of these four plant growth regulators was determined through density functional theory calculations and analysis of absorption spectra. Discriminant analysis method was used to evaluate the classification trends of the four plant growth regulators based on their THz absorbance spectra. Generally, this study provides a reference for the rapid detection of plant growth regulators in fruits and vegetables by using terahertz spectroscopy technology.