In order to monitor plant chlorophyll content in real-time, a new vehicle-mounted detection system was developed to measure crop canopy spectral characteristics. It was designed to work as a wireless sensor network wi...In order to monitor plant chlorophyll content in real-time, a new vehicle-mounted detection system was developed to measure crop canopy spectral characteristics. It was designed to work as a wireless sensor network with one control unit and one measuring unit. The control unit included a personal digital assistant （PDA） device with a ZigBee wireless network coordinator. As the coordinator of the whole wireless network, the control unit was used to receive, display and store all the data sent from sensor nodes. The measuring unit consisted of several optical sensor nodes. All the sensor nodes were mounted on an on-board mechanical structure so that the measuring unit could collect the canopy spectral data while moving. Each sensor node contained four optical channels to measure the light radiation at the wavebands of 550, 650, 766, and 850 nm. The calibration tests veriifed a good performance in terms of the wireless transmission ability and the sensor measurement precision. Both stationary and moving ifeld experiments were also conducted in a winter wheat experimental ifeld. There was a high correlation between chlorophyll content and vegetation index, and several estimation models of the chlorophyll content were established. The highest R2 of the estimation models was 0.718. The results showed that the vehicle-mounted crop detection system has potential for ifeld application.展开更多
Plant height can be used for assessing plant vigor and predicting biomass and yield. Manual measurement of plant height is time-consuming and labor-intensive. We describe a method for measuring maize plant height usin...Plant height can be used for assessing plant vigor and predicting biomass and yield. Manual measurement of plant height is time-consuming and labor-intensive. We describe a method for measuring maize plant height using an RGB-D camera that captures a color image and depth information of plants under field conditions. The color image was first processed to locate its central area using the S component in HSV color space and the Density-Based Spatial Clustering of Applications with Noise algorithm. Testing showed that the central areas of plants could be accurately located. The point cloud data were then clustered and the plant was extracted based on the located central area. The point cloud data were further processed to generate skeletons, whose end points were detected and used to extract the highest points of the central leaves. Finally, the height differences between the ground and the highest points of the central leaves were calculated to determine plant heights. The coefficients of determination for plant heights manually measured and estimated by the proposed approach were all greater than 0.95. The method can effectively extract the plant from overlapping leaves and estimate its plant height. The proposed method may facilitate maize height measurement and monitoring under field conditions.展开更多
Estimation of leaf chlorophyll content(LCC) by proximal sensing is an important tool for photosynthesis evaluation in high-throughput phenotyping. The temporal variability of crop biochemical properties and canopy str...Estimation of leaf chlorophyll content(LCC) by proximal sensing is an important tool for photosynthesis evaluation in high-throughput phenotyping. The temporal variability of crop biochemical properties and canopy structure across different growth stages has great impacts on wheat LCC estimation, known as growth stage effects. It will result in the heterogeneity of crop canopy at different growth stages, which would mask subtle spectral response of biochemistry variations. This study aims to explore spectral responses on the growth stage effects and establish LCC models suited for different growth stages. A total number of 864 pairwise samples of wheat canopy spectra and LCC values with 216 observations of each stage were sampled at the tillering, jointing, booting and heading stages in 2021. Firstly, statistical analysis of LCC and spectral response presented different distribution traits and typical spectral variations peak at 470, 520 and 680 nm. Correlation analysis between LCC and reflectance showed typical red edge shifts. Secondly, the testing model of partial least square(PLS) established by the entire datasets to validate the predictive performance at each stage yielded poor LCC estimation accuracy. The spectral wavelengths of red edge(RE) and blue edge(BE) shifts and the poor estimation capability motivated us to further explore the growth stage effects by establishing LCC models at respective growth periods.Finally, competitive adaptive reweighted sampling PLS(CARS-PLS), decision tree(DT) and random forest(RF) were used to select sensitive bands and establish LCC models at specific stages. Bayes optimisation was used to tune the hyperparameters of DT and RF regression. The modelling results indicated that CARS-PLS and DT did not extract specific wavelengths that could decrease the influences of growth stage effects. From the RF out-of-bag(OOB) evaluation, the sensitive wavelengths displayed consistent spectral shifts from BE to GP and from RE to RV from tillering to heading stages. Compared with CARS-PLS and DT,results of RF modelling yielded an estimation accuracy with deviation to performance(RPD) of 2.11, 2.02,3.21 and 3.02, which can accommodate the growth stage effects. Thus, this study explores spectral response on growth stage effects and provides models for chlorophyll content estimation to satisfy the requirement of high-throughput phenotyping.展开更多
A smart sprayer comprises a detection system and a chemical spraying system.In this study,the development status and challenges of the detection systems of smart sprayers are discussed along with perspectives on these...A smart sprayer comprises a detection system and a chemical spraying system.In this study,the development status and challenges of the detection systems of smart sprayers are discussed along with perspectives on these technologies.The detection system of a smart sprayer is used to collect information on target areas and make spraying decisions.The spraying system controls sprayer operation.Various sensing technologies,such as machine vision,spectral analysis,and remote sensing,are used in target detection.In image processing,morphological features are employed to segment characteristics such as shape,structure,color,and pattern.In spectral analysis,the characteristics of reflectance and multispectral images are applied in crop detection.For the remote sensing application,vegetation indices and hyperspectral images are used to provide information on crop management.Other sensors,such as thermography,ultrasonic,laser,and X-ray sensors,are also used in the detection system and mentioned in the review.On the basis of this review,challenges and perspectives are suggested.The findings of this study may aid the understanding of smart sprayer systems and provide feasible methods for improving efficiency in chemical applications.展开更多
A crop monitoring system was developed to nondestructively monitor the crop growth status in the field.With a two channel multispectral camera with one lens,controlling platform,wireless remote control module and cont...A crop monitoring system was developed to nondestructively monitor the crop growth status in the field.With a two channel multispectral camera with one lens,controlling platform,wireless remote control module and control software,the system was able to synchronously acquire visible image(red(R),green(G),blue(B):400-700 nm)and near-infrared(NIR)image(760-1000 nm).The tomato seedlings multi-spectral images collection experiment in the greenhouse was conducted by using the developed system from the seeding stage to fruiting stage.More than 240 couples of tomato seedlings pictures were acquired with the Soil and Plant Analyzer Development(SPAD)value measured at the same time.The obtained images were available to process,and some vegetation indexes,such as normalized difference vegetation index(NDVI),ratio vegetation index(RVI)and normalized difference green index(NDGI),were calculated.Considering the SPAD value and the correlation coefficient between SPAD and other parameters in different fertilization treatments,the multiple linear regressions(MLR)model for estimating tomato seedlings chlorophyll content was built based on the average gray value in red,green,blue and NIR,vegetable indexes,NDVI,RVI and NDGI in the 33.3%(N1),66.6%(N2),and 100%(N3)nutrient levels during seeding stage and blossom and fruit stage.The R2 of the model is 0.88.The results revealed that the developed crop monitoring system provided a feasible tool to detect the growth status of tomato.More filed experiments and multi-spectral image analysis will be investigated to evaluate the crop growth status in the near future.展开更多
Two new control algorithms based on MSP430 microcontroller unit(MCU)were developed to improve the performance of a fertigation system controlled by the electrical conductivity(EC)value of an irrigation nutrient soluti...Two new control algorithms based on MSP430 microcontroller unit(MCU)were developed to improve the performance of a fertigation system controlled by the electrical conductivity(EC)value of an irrigation nutrient solution in a greenhouse.The first algorithm is incremental proportional-integral-derivative(PID),and the second one is a two-stage combination algorithm(PID+fuzzy).With an improved multi-line mixing Venturi tube,several sets of experiments were conducted for a fertilizer absorption test under two conditions,namely,various suction lines and different EC target values settings.Under the first condition,with an EC target value of 2.0 mS/cm and opening of various suction pipes,the steady-state times are 186 s,172 s,134 s,and 122 s corresponding to the opening of one to four suction pipes,respectively,for PID+fuzzy control.The corresponding values are 220 s,196 s,158 s,and 148 s for incremental PID control.Under the second condition,four suction pipes are opened with different target EC values of 1.5 mS/cm,2.0 mS/cm,and 2.5 mS/cm,and the shortest response time and the minimum overshoot were obtained for PID+fuzzy control when the target EC value is 1.5 mS/cm,which are 96 s and 0.18 mS/cm,respectively.While the corresponding values are 112 s and 0.4 mS/cm,respectively for incremental PID control.The two control strategies can adjust the EC value to the target value for real-time control,but the combination control algorithm can be implemented more rapidly,accurately,and steadily with a small overshoot compared with incremental PID control.The combination algorithm(PID+fuzzy)control strategy also possesses better properties for automatic fertigation control in greenhouses than the incremental PID control strategy,the combination algorithm provides an optimal way of water and fertilizer management for crops in greenhouses which will contribute to water and fertilizer saving.展开更多
In the establishment of differential equations,the determination of time-varying parameters is a difficult problem,especially for equations related to life activities.Thus,we propose a new framework named BioE-PINN ba...In the establishment of differential equations,the determination of time-varying parameters is a difficult problem,especially for equations related to life activities.Thus,we propose a new framework named BioE-PINN based on a physical information neural network that successfully obtains the time-varying parameters of differential equations.In the proposed framework,the learnable factors and scale parameters are used to implement adaptive activation functions,and hard constraints and loss function weights are skillfully added to the neural network output to speed up the training convergence and improve the accuracy of physical information neural networks.In this paper,taking the electrophysiological differential equation as an example,the characteristic parameters of ion channel and pump kinetics are determined using BioE-PINN.The results demonstrate that the numerical solution of the differential equation is calculated by the parameters predicted by BioE-PINN,the RootMean Square Error(RMSE)is between 0.01 and 0.3,and the Pearson coefficient is above 0.87,which verifies the effectiveness and accuracy of BioE-PINN.Moreover,realmeasuredmembrane potential data in animals and plants are employed to determine the parameters of the electrophysiological equations,with RMSE 0.02-0.2 and Pearson coefficient above 0.85.In conclusion,this framework can be applied not only for differential equation parameter determination of physiological processes but also the prediction of time-varying parameters of equations in other fields.展开更多
Precision agriculture,which can be also called precision farming,may be defined as a management strategy that uses information and communication technologies(ICT)to bring data from multiple sources to bear on decision...Precision agriculture,which can be also called precision farming,may be defined as a management strategy that uses information and communication technologies(ICT)to bring data from multiple sources to bear on decisions associated with crop production.As the introduction of this new technological and industrial revolution proceeds,biotechnology,new materials and nanotechnology,Internet of things(IoT),and new energy technologies have been infiltrating rapidly into agriculture.Advanced manufacturing of agricultural equipment,agricultural big data,and agricultural robots are being adopted by the industry and are gradually being introduced to all fields of production agriculture.Smart agriculture,as the upgrade of precision agriculture is often called,has developed a strong momentum in terms of research,development,commercialization and adoption.展开更多
Rational management of CO_(2) can improve the net photosynthetic rate of plants,thereby improving crop yield and quality.In order to precisely manage CO_(2) in a greenhouse,a wireless sensor network(WSN)system was dev...Rational management of CO_(2) can improve the net photosynthetic rate of plants,thereby improving crop yield and quality.In order to precisely manage CO_(2) in a greenhouse,a wireless sensor network(WSN)system was developed to monitor greenhouse environmental parameters in real time,including air temperature,humidity,CO_(2) concentration,soil temperature,soil moisture,and light intensity.The WSN system includes several sensor nodes,a gateway node,and remote management software.The sensor nodes can collect 0-5 V and 4-20 mA analog signals and universal asynchronous receiver/transmitter(UART)data.The gateway node can process and transmit the data and commands between sensor nodes and remote management software.The remote management software provides a friendly interface between user and machine.Users can inquire about real-time data,and set the parameters of the WSN.The photosynthetic rate of tomato plants were studied in the flowering stage.A LI-6400XT portable photosynthesis analyzer was used to measure the photosynthetic rates of the tomato plants,and the environmental parameters of leaves were controlled according to the presetting rule.The photosynthetic rate prediction model of a single leaf was established based on a back propagation neural network(BPNN).The environmental parameters were used as input neurons after being processed by principal component analysis(PCA),and the photosynthetic rate was taken as the output neuron.The performance of the prediction model was evaluated,and the results showed that the correlation coefficient between the simulated and observed data sets was 0.9899,and root-mean-square error(RMSE)was 1.4686.Furthermore,when different CO_(2) concentrations were selected as the input to predict the photosynthetic rate,the simulated and observed data showed the same trend.According to the above analysis,it was concluded that the model can be used for quantitative regulation of CO_(2) for tomato plants in greenhouses.展开更多
In order to improve the efficiency of CO2 fertilizer and promote high quality and yield,it is necessary to precisely control CO2 fertilizer by wireless sensor network based on a model of photosynthetic rate prediction...In order to improve the efficiency of CO2 fertilizer and promote high quality and yield,it is necessary to precisely control CO2 fertilizer by wireless sensor network based on a model of photosynthetic rate prediction in greenhouse.An experiment was carried out on tomato plants in greenhouse for photosynthetic rate prediction modeling combined rough set and BP neural network.In data acquiring phase,plants growth information and greenhouse environmental information that may have influences on photosynthetic rate,including plant height,stem diameter,the number of leaves and chlorophyll content of functional leaves,air temperature,air humidity,light intensity,CO2 concentration and soil moisture,which were measured.And LI-6400XT photosynthetic rate instrument was used for obtaining net photosynthetic rate of functional leaf.After preliminary processing,135 sets of data were obtained.And twelve of them were used for model test of neural network,while the others were used for modeling.All of the data were normalized before modeling.Two models were built to predict photosynthetic rate based on BP neural network.One had total nine input parameters.The other had six input parameters,chlorophyll content,air temperature,air humidity,light intensity,CO2 concentration,and soil moisture,which were reducted from original nine based on attributes reduction theory of rough set.Both two models have one output parameter,the net photosynthetic rate of single leaf.The genetic algorithm was adopted to reduct attributes.Since continuous data cannot be processed by rough set,the K-mean cluster method was used to discretize the data of nine input parameters before attributes reduction.The prediction results of two models showed that the model with six input parameters had a mean absolute error of 0.6958,an average relative error of 7.28%,a root-mean-square error of 0.7428,and a correlation coefficient of 0.9964,while the other model respectively had 0.4026,4.53%,0.3245 and 0.9965,which proved that the model with minimum attributes had higher prediction accuracy.On the other hand,the number of iterations was used to represent the neural network train speed.The result showed that the model with six input parameters had an iteration of 544,while the other had 1038.Hence,the reduction model was applied to controlling CO2 concentration.The net photosynthetic rates at different CO2 concentrations were predicted at a certain condition.The results had the same curve trend with theory analysis,and a high prediction accuracy,which proved that the model was useful for CO2 concentration control.展开更多
In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy technology.The spectral reflectance of maize canopy...In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy technology.The spectral reflectance of maize canopy was measured and processed following wavelet denoising and multivariate scatter correction(MSC)to reduce the noise influence.Firstly,the signal to noise ratio(SNR)and curve smoothness(CS)were used to evaluate the denoising effect of different wavelet functions and decomposition levels.As a result,the Sym6 wavelet basis function and the 5th level decomposition were determined to denoise the original signal.The MSC method was used to eliminate the scattering effect after denoising.Then three spectral ranges were extracted by interval partial least squares(IPLS)including the 525-549 nm,675-749 nm and 850-874 nm.Finally,the chlorophyll content estimation model was developed by using support vector regression(SVR)method.The calibration Rc2 of the SVR model was 0.831,the RMSEC was 1.3852 mg/L;the validation Rv2 was 0.809,the RMSEP was 0.8664 mg/L.The results show that the SNR and CS indicators can be used to select the parameters for wavelet denoising and model can be used to estimate the chlorophyll content of maize canopy in the field.展开更多
Soil infiltration is the process by which water on the soil surface penetrates the soil.Quantifying the soil infiltration capacity(soil infiltrability)is very important for determining components of the hydrological m...Soil infiltration is the process by which water on the soil surface penetrates the soil.Quantifying the soil infiltration capacity(soil infiltrability)is very important for determining components of the hydrological modeling,irrigation design and many other natural or man made processes.In this paper,commonly used methods for soil infiltration rate measurement with their principles and application conditions are introduced.The advantages and disadvantages of each method under various application conditions are discussed for comparison.Three new methods for soil infiltrability measurement,including the corresponding algorithm models,and the experimental apparatus and procedures are introduced.These analyses should facilitate the choice of method used for soil infiltrability measurement.展开更多
Square wave anodic stripping voltammetry(SWASV)is an effective method for the detection of Cd(II),but the presence of Pb(II)usually has some potential and negative interference on the SWASV detection of Cd(II).In this...Square wave anodic stripping voltammetry(SWASV)is an effective method for the detection of Cd(II),but the presence of Pb(II)usually has some potential and negative interference on the SWASV detection of Cd(II).In this paper,a novel method was proposed to predict the concentration of Cd(II)in the presence of Pb(II)based on the combination of chemically modified electrode(CME),machine learning algorithms(MLA)and SWASV.A Bi film/ionic liquid/screen-printed electrode(Bi/IL/SPE)was prepared and used for the sensitive detection of trace Cd(II).The parameters affecting the stripping currents were investigated and optimized.The morphologies and electrochemical properties of the modified electrode were characterized by scanning electron microscopy(SEM)and SWASV.The measured SWASV spectrograms obtained at different concentrations were used to build the mathematical models for the prediction of Cd(II),which taking the combined effect of Cd(II)and Pb(II)into consideration on the SWASV detection of Cd(II),and to establish a nonlinear relationship between the stripping currents of Pb(II)and Cd(II)and the concentration of Cd(II).The proposed mathematical models rely on an improved particle swarm optimization-support vector machine(PSO-SVM)to assess the concentration of Cd(II)in the presence of Pb(II)in a wide range of concentrations.The experimental results suggest that this method is suitable to fulfill the goal of Cd(II)detection in the presence of Pb(II)(correlation coefficient,mean absolute error and root mean square error were 0.998,1.63 and 1.68,respectively).Finally,the proposed method was applied to predict the trace Cd(II)in soil samples with satisfactory results.展开更多
CO_(2)concentration is an environmental factor affecting photosynthesis and consequently the yield and quality of tomatoes.In this study,a photosynthesis prediction model for the entire growth stage of tomatoes was co...CO_(2)concentration is an environmental factor affecting photosynthesis and consequently the yield and quality of tomatoes.In this study,a photosynthesis prediction model for the entire growth stage of tomatoes was constructed to elevate CO_(2)level on the basis of crop requirements and to evaluate the effect of CO_(2)elevation on leaf photosynthesis.The effect of CO_(2)enrichment on tomato photosynthesis was investigated using two CO_(2)enrichment treatments at the entire growth stage.A wireless sensor network-based environmental monitoring system was used for the real-time monitoring of environmental factors,and the LI-6400XT portable photosynthesis system was used to measure the net photosynthetic rate of tomato leaf.As input variables for the model,environmental factors were uniformly preprocessed using independent component analysis.Moreover,the photosynthesis prediction model for the entire growth stage was established on the basis of the support vector machine(SVM)model.Improved particle swarm optimization(PSO)was also used to search for the best parameters c and g of SVM.Furthermore,the relationship between CO_(2)concentration and photosynthetic rate under varying light intensities was predicted using the established model,which can determine CO_(2)saturation points at the various growth stages.The determination coefficients between the simulated and observed data sets for the three growth stages were 0.96,0.96,and 0.94 with the improved PSO-SVM and 0.89,0.87,and 0.86 with the original PSO-SVM.The results indicate that the improved PSO-SVM exhibits a high prediction accuracy.The study provides a basis for the precise regulation of CO_(2)enrichment in greenhouses.展开更多
Huanglongbing (HLB, citrus greening) is one of the most serious quarantine diseases of citrus worldwide. To monitor in real-time, recognize diseased trees, and efficiently prevent and control HLB disease in citrus, it...Huanglongbing (HLB, citrus greening) is one of the most serious quarantine diseases of citrus worldwide. To monitor in real-time, recognize diseased trees, and efficiently prevent and control HLB disease in citrus, it is necessary to develop a rapid diagnostic method to detect HLB infected plants without symptoms. This study used Newhall navel orange plants as the research subject, and collected normal color leaf samples and chlorotic leaf samples from a healthy orchard and an HLB-infected orchard, respectively. First, hyperspectral data of the upper and lower leaf surfaces were obtained, and then the polymerase chain reaction (PCR) was used to detect the HLB bacterium in each leaf. The PCR test results showed that all samples from the healthy orchard were negative, and a portion of the samples from the infected orchard were positive. According to these results, the leaf samples from the orchards were divided into disease-free leaves and HLB-positive leaves, and the least squares support vector machine recognition model was established based on the leaf hyperspectral reflectance. The effect on the model of the spectra obtained from the upper and lower leaf surfaces was investigated and different pretreatment methods were compared and analyzed. It was observed that the HLB recognition rate values of the calibration and validation sets based on upper leaf surface spectra under 9-point smoothing pretreatment were 100% and 92.5%, respectively. The recognition rate values based on lower leaf surface spectra under the second-order derivative pretreatment were also 100% and 92.5%, respectively. Both upper and lower leaf surface spectra were available for recognition of HLB-infected leaves, and the HLB PCR-positive leaves could be distinguished from the healthy by the hyperspectral modeling analysis. The results of this study show that early and nondestructive detection of HLBinfected leaves without symptoms is possible, which provides a basis for the hyperspectral diagnosis of citrus with HLB.展开更多
Given the lack of technical conditions and research methods,instruments that can measure the canopy apparent photosynthetic rate have low precision and are rarely studied.Comparative studies on canopy apparent photosy...Given the lack of technical conditions and research methods,instruments that can measure the canopy apparent photosynthetic rate have low precision and are rarely studied.Comparative studies on canopy apparent photosynthetic rate and single leaf photosynthetic rate are also relatively few.This study aims to measure and predict the canopy apparent photosynthetic rate of tomato.A canopy apparent photosynthetic rate measuring system,which was comprised of a wireless sensor network(WSN),an assimilation chamber,and a LI-6400XT photosynthetic rate instrument was established.The system was used to determine the greenhouse environmental parameters and CO2 absorptive capacity of the whole growth stage of tomato.A semi-closed assimilation chamber was designed as a side opening to conveniently measure the canopy apparent photosynthetic rate.WSN nodes were placed in the chamber to monitor environmental parameters,including air temperature,air humidity,and assimilation chamber temperature.A grid and pixel conversion method was used to measure the whole plant leaf areas of tomato.As a semi-closed measurement system,the assimilation chamber was used to calculate the canopy apparent photosynthetic rate.To conduct a comparative research on the canopy apparent photosynthetic rate and the single leaf photosynthetic rate,the LI-6400XT portable photosynthesis system was used to measure the single leaf photosynthetic rate,and the support vector machine was used to establish the prediction model of canopy apparent photosynthetic rate.The experimental results indicated that the correlation coefficients of the photosynthesis prediction model in the seeding and flowering stages were 0.9420 and 0.9226,respectively,showing the high accuracy of the SVM model.展开更多
Semi-circular open channel plays an important role in various applications and the measurement of its discharge is of interests.In this study,theoretical formulae for free overflow in a semi-circular channel are devel...Semi-circular open channel plays an important role in various applications and the measurement of its discharge is of interests.In this study,theoretical formulae for free overflow in a semi-circular channel are developed and presented for the discharge and wetted area relationship.The traditional discharge formulation and available experimental data are used to verify and validate the proposed relationships.The discharges calculated by using the proposed relationship show very good agreement with the experimental data sets.The results from this study supply the basis for circular weir development.展开更多
Foodborne pathogenic bacteria have been considered as a major risk factor for food safety. It is of great significance to carry out in-field screening of pathogenic bacteria to prevent the outbreaks of foodborne disea...Foodborne pathogenic bacteria have been considered as a major risk factor for food safety. It is of great significance to carry out in-field screening of pathogenic bacteria to prevent the outbreaks of foodborne diseases. In this study, a portable lab-on-a-disc platform with a microfluidic disc was developed for rapid and automatic detection of Salmonella typhimurium using a nickel nanowire(Ni NW) net for effective separation of target bacteria, horseradish peroxidase nanoflowers(HRP NFs) for efficient amplification of biological signals, and a self-developed smartphone APP for accurate analysis of colorimetric images. First,the microfluidic disc was preloaded with reagents and samples and centrifuged to form one bacterial sample column, one immune Ni NW column, one HRP NF column, two washing buffer columns and one tetramethylbenzidine(TMB) column, which were separated by air gaps. Then, a rotatable magnetic field was specifically developed to assemble the Ni NWs into a net, which was automatically controlled by a stepped motor to successively pass through the sample column for specific capture of target bacteria, the HRP NF column for specific label of target bacteria, the washing columns for effective removal of sample background and non-specific binding NFs, and the TMB column for colorimetric determination of target bacteria. The color change of TMB from colorless to blue was finally analyzed using the smartphone APP to quantitatively determine the target bacteria. This lab-on-a-disc platform could detect Salmonella typhimurium from 5.6 × 10^(1) CFU/20 μL to 5.6 × 10^(5) CFU/20 μL in 1 h with a lower detection limit of 56 CFU/20 μL. The recovery of target bacteria in spiked chicken samples ranged from 97.5% to 101.8%. This portable platform integrating separation, labeling, washing, catalysis and detection onto a single disc is featured with automatic operation, fast reaction, and small size and has shown its potential for in-field detection of foodborne pathogens.展开更多
A soil electrical conductivity(EC)measurement system based on direct digital synthesizer(DDS)and digital oscilloscope was developed.The system took the“current-voltage four-electrode method”as the design principal a...A soil electrical conductivity(EC)measurement system based on direct digital synthesizer(DDS)and digital oscilloscope was developed.The system took the“current-voltage four-electrode method”as the design principal and adopted a six-pin structure of the probe,two center pins to measure the soil EC in shallow layer,two outside pins to measure the soil EC in deep layer,and two middle pins for inputting the driving current.A signal generating circuit using DDS technology was adopted to generate sine signals,which was connected with the two middle pins.A digital oscilloscope was used to record and store the two soil output signals with noises in microseconds,which were from the two center pins and two outside pins,respectively.Then a digital bandpass filter was used to filter the soil output signals recorded by the digital oscilloscope.Compared with the traditional analog filter circuit,the digital filter could filter out the noises of all frequency except for the frequency of the excitation source.It could improve the effect of filtering and the accuracy of the soil EC measurement system.The DDS circuit could provide more stable sine signals with larger amplitudes.The use of digital oscilloscope enables us to analyze the soil output signals in microseconds and measure the soil EC more accurately.The new soil EC measurement system based on DDS and digital oscilloscope can provide a new effective tool for soil sensing in precision agriculture.展开更多
In recent years,the outbreaks of foodborne diseases caused by pathogenic bacteria have made considerable economic losses and shown a threat to public health.The key to prevent and control these diseases is fast screen...In recent years,the outbreaks of foodborne diseases caused by pathogenic bacteria have made considerable economic losses and shown a threat to public health.The key to prevent and control these diseases is fast screening of pathogenic bacteria,which is usually performed with three procedures:sample collection,bacteria separation and bacteria detection.For sample collection,the national standard methods are often employed.For bacteria detection,currently available methods such as Polymerase Chain Reaction and Enzyme Linked Immuno-Sorbent Assay are often used.For bacteria separation,traditional methods such as filtration and centrifugation are not capable to specifically separate the target bacteria.However,food samples are very complicated and require efficient pretreatment for bacteria separation and concentration to achieve accurate and reliable results.The conventional immune magnetic separation method can be used to specifically separate the bacteria,but it still cannot meet the requirements for food sample pretreatment due to very low concentration of target bacteria in food.Therefore,this study developed an automatic and efficient immuno-separator of foodborne bacteria based on magnetophoresis and magnetic mixing,and E.coli O157:H7 was used as research model.A magnetic mixer was applied to facilitate the immunoreaction between the immune magnetic nanoparticles and the target bacteria cells,and a magnetophoretic separation tubing was utilized for magnetophoretic separation of the magnetic bacteria.Under the optimal mixing time of 20 min and the optimal flow rate of 50μL/min,the separation efficiency of E.coli O157:H7 could be more than 90%,showing that the developed immuno-separator is promising to be applied for efficient separation of foodborne bacteria and can be easily extended for separation of other biological targets by using their specific antibodies.展开更多
基金supported by the Key Technologies R&D Program of China during the 12th Five-Year Plan period (2012BAH29B04)the National High-Tech R&D Program of China (2013AA102303,2012AA101901)
文摘In order to monitor plant chlorophyll content in real-time, a new vehicle-mounted detection system was developed to measure crop canopy spectral characteristics. It was designed to work as a wireless sensor network with one control unit and one measuring unit. The control unit included a personal digital assistant （PDA） device with a ZigBee wireless network coordinator. As the coordinator of the whole wireless network, the control unit was used to receive, display and store all the data sent from sensor nodes. The measuring unit consisted of several optical sensor nodes. All the sensor nodes were mounted on an on-board mechanical structure so that the measuring unit could collect the canopy spectral data while moving. Each sensor node contained four optical channels to measure the light radiation at the wavebands of 550, 650, 766, and 850 nm. The calibration tests veriifed a good performance in terms of the wireless transmission ability and the sensor measurement precision. Both stationary and moving ifeld experiments were also conducted in a winter wheat experimental ifeld. There was a high correlation between chlorophyll content and vegetation index, and several estimation models of the chlorophyll content were established. The highest R2 of the estimation models was 0.718. The results showed that the vehicle-mounted crop detection system has potential for ifeld application.
基金supported by the Key Project of Intergovernmental Collaboration for Science and Technology Innovation under the National Key R&D Plan (2019YFE0103800)CAU Special Fund to Build World-class University (in disciplines) and Guide Distinctive Development (2021AC006)。
文摘Plant height can be used for assessing plant vigor and predicting biomass and yield. Manual measurement of plant height is time-consuming and labor-intensive. We describe a method for measuring maize plant height using an RGB-D camera that captures a color image and depth information of plants under field conditions. The color image was first processed to locate its central area using the S component in HSV color space and the Density-Based Spatial Clustering of Applications with Noise algorithm. Testing showed that the central areas of plants could be accurately located. The point cloud data were then clustered and the plant was extracted based on the located central area. The point cloud data were further processed to generate skeletons, whose end points were detected and used to extract the highest points of the central leaves. Finally, the height differences between the ground and the highest points of the central leaves were calculated to determine plant heights. The coefficients of determination for plant heights manually measured and estimated by the proposed approach were all greater than 0.95. The method can effectively extract the plant from overlapping leaves and estimate its plant height. The proposed method may facilitate maize height measurement and monitoring under field conditions.
基金supported by the National Key Research and Development Program (2019YFE0125500)University-Locality Integrative Development Project of Yantai (2020XDRHXMPT35)+1 种基金the National Natural Science Foundation of China (31971785 and41801245)the Graduate Training Project of China Agricultural University (JG2019004, JG202026, YW2020007, QYJC202101, and JG202102)。
文摘Estimation of leaf chlorophyll content(LCC) by proximal sensing is an important tool for photosynthesis evaluation in high-throughput phenotyping. The temporal variability of crop biochemical properties and canopy structure across different growth stages has great impacts on wheat LCC estimation, known as growth stage effects. It will result in the heterogeneity of crop canopy at different growth stages, which would mask subtle spectral response of biochemistry variations. This study aims to explore spectral responses on the growth stage effects and establish LCC models suited for different growth stages. A total number of 864 pairwise samples of wheat canopy spectra and LCC values with 216 observations of each stage were sampled at the tillering, jointing, booting and heading stages in 2021. Firstly, statistical analysis of LCC and spectral response presented different distribution traits and typical spectral variations peak at 470, 520 and 680 nm. Correlation analysis between LCC and reflectance showed typical red edge shifts. Secondly, the testing model of partial least square(PLS) established by the entire datasets to validate the predictive performance at each stage yielded poor LCC estimation accuracy. The spectral wavelengths of red edge(RE) and blue edge(BE) shifts and the poor estimation capability motivated us to further explore the growth stage effects by establishing LCC models at respective growth periods.Finally, competitive adaptive reweighted sampling PLS(CARS-PLS), decision tree(DT) and random forest(RF) were used to select sensitive bands and establish LCC models at specific stages. Bayes optimisation was used to tune the hyperparameters of DT and RF regression. The modelling results indicated that CARS-PLS and DT did not extract specific wavelengths that could decrease the influences of growth stage effects. From the RF out-of-bag(OOB) evaluation, the sensitive wavelengths displayed consistent spectral shifts from BE to GP and from RE to RV from tillering to heading stages. Compared with CARS-PLS and DT,results of RF modelling yielded an estimation accuracy with deviation to performance(RPD) of 2.11, 2.02,3.21 and 3.02, which can accommodate the growth stage effects. Thus, this study explores spectral response on growth stage effects and provides models for chlorophyll content estimation to satisfy the requirement of high-throughput phenotyping.
基金supported by the National Natural Science Foundation of China(Project No.U0931001,31071330)the 863 Program(2012BAH29 B04,2012AA101901)the Science Research Foundation for Young Teachers(Project No.2011JS151)。
文摘A smart sprayer comprises a detection system and a chemical spraying system.In this study,the development status and challenges of the detection systems of smart sprayers are discussed along with perspectives on these technologies.The detection system of a smart sprayer is used to collect information on target areas and make spraying decisions.The spraying system controls sprayer operation.Various sensing technologies,such as machine vision,spectral analysis,and remote sensing,are used in target detection.In image processing,morphological features are employed to segment characteristics such as shape,structure,color,and pattern.In spectral analysis,the characteristics of reflectance and multispectral images are applied in crop detection.For the remote sensing application,vegetation indices and hyperspectral images are used to provide information on crop management.Other sensors,such as thermography,ultrasonic,laser,and X-ray sensors,are also used in the detection system and mentioned in the review.On the basis of this review,challenges and perspectives are suggested.The findings of this study may aid the understanding of smart sprayer systems and provide feasible methods for improving efficiency in chemical applications.
基金948 Project(No.2011-G32)High Technology Research and Development Research Fund(No.2013AA102303).
文摘A crop monitoring system was developed to nondestructively monitor the crop growth status in the field.With a two channel multispectral camera with one lens,controlling platform,wireless remote control module and control software,the system was able to synchronously acquire visible image(red(R),green(G),blue(B):400-700 nm)and near-infrared(NIR)image(760-1000 nm).The tomato seedlings multi-spectral images collection experiment in the greenhouse was conducted by using the developed system from the seeding stage to fruiting stage.More than 240 couples of tomato seedlings pictures were acquired with the Soil and Plant Analyzer Development(SPAD)value measured at the same time.The obtained images were available to process,and some vegetation indexes,such as normalized difference vegetation index(NDVI),ratio vegetation index(RVI)and normalized difference green index(NDGI),were calculated.Considering the SPAD value and the correlation coefficient between SPAD and other parameters in different fertilization treatments,the multiple linear regressions(MLR)model for estimating tomato seedlings chlorophyll content was built based on the average gray value in red,green,blue and NIR,vegetable indexes,NDVI,RVI and NDGI in the 33.3%(N1),66.6%(N2),and 100%(N3)nutrient levels during seeding stage and blossom and fruit stage.The R2 of the model is 0.88.The results revealed that the developed crop monitoring system provided a feasible tool to detect the growth status of tomato.More filed experiments and multi-spectral image analysis will be investigated to evaluate the crop growth status in the near future.
基金This work was supported by Yunnan Academician Expert Workstation(Li Minzan,Grant No.20170907)the National Key Research and Development Program(Grant No.2016YED0201000-2016YED0201003)the Key Laboratory Project(2019TC124).
文摘Two new control algorithms based on MSP430 microcontroller unit(MCU)were developed to improve the performance of a fertigation system controlled by the electrical conductivity(EC)value of an irrigation nutrient solution in a greenhouse.The first algorithm is incremental proportional-integral-derivative(PID),and the second one is a two-stage combination algorithm(PID+fuzzy).With an improved multi-line mixing Venturi tube,several sets of experiments were conducted for a fertilizer absorption test under two conditions,namely,various suction lines and different EC target values settings.Under the first condition,with an EC target value of 2.0 mS/cm and opening of various suction pipes,the steady-state times are 186 s,172 s,134 s,and 122 s corresponding to the opening of one to four suction pipes,respectively,for PID+fuzzy control.The corresponding values are 220 s,196 s,158 s,and 148 s for incremental PID control.Under the second condition,four suction pipes are opened with different target EC values of 1.5 mS/cm,2.0 mS/cm,and 2.5 mS/cm,and the shortest response time and the minimum overshoot were obtained for PID+fuzzy control when the target EC value is 1.5 mS/cm,which are 96 s and 0.18 mS/cm,respectively.While the corresponding values are 112 s and 0.4 mS/cm,respectively for incremental PID control.The two control strategies can adjust the EC value to the target value for real-time control,but the combination control algorithm can be implemented more rapidly,accurately,and steadily with a small overshoot compared with incremental PID control.The combination algorithm(PID+fuzzy)control strategy also possesses better properties for automatic fertigation control in greenhouses than the incremental PID control strategy,the combination algorithm provides an optimal way of water and fertilizer management for crops in greenhouses which will contribute to water and fertilizer saving.
基金This work was supported by the National Natural Science Foundation of China under 62271488 and 61571443.
文摘In the establishment of differential equations,the determination of time-varying parameters is a difficult problem,especially for equations related to life activities.Thus,we propose a new framework named BioE-PINN based on a physical information neural network that successfully obtains the time-varying parameters of differential equations.In the proposed framework,the learnable factors and scale parameters are used to implement adaptive activation functions,and hard constraints and loss function weights are skillfully added to the neural network output to speed up the training convergence and improve the accuracy of physical information neural networks.In this paper,taking the electrophysiological differential equation as an example,the characteristic parameters of ion channel and pump kinetics are determined using BioE-PINN.The results demonstrate that the numerical solution of the differential equation is calculated by the parameters predicted by BioE-PINN,the RootMean Square Error(RMSE)is between 0.01 and 0.3,and the Pearson coefficient is above 0.87,which verifies the effectiveness and accuracy of BioE-PINN.Moreover,realmeasuredmembrane potential data in animals and plants are employed to determine the parameters of the electrophysiological equations,with RMSE 0.02-0.2 and Pearson coefficient above 0.85.In conclusion,this framework can be applied not only for differential equation parameter determination of physiological processes but also the prediction of time-varying parameters of equations in other fields.
文摘Precision agriculture,which can be also called precision farming,may be defined as a management strategy that uses information and communication technologies(ICT)to bring data from multiple sources to bear on decisions associated with crop production.As the introduction of this new technological and industrial revolution proceeds,biotechnology,new materials and nanotechnology,Internet of things(IoT),and new energy technologies have been infiltrating rapidly into agriculture.Advanced manufacturing of agricultural equipment,agricultural big data,and agricultural robots are being adopted by the industry and are gradually being introduced to all fields of production agriculture.Smart agriculture,as the upgrade of precision agriculture is often called,has developed a strong momentum in terms of research,development,commercialization and adoption.
基金The authors acknowledge that this work was supported by the National Natural Science Fund(Grant No.31271619)the Doctoral Program of Higher Education of China(Grant No.20110008130006).
文摘Rational management of CO_(2) can improve the net photosynthetic rate of plants,thereby improving crop yield and quality.In order to precisely manage CO_(2) in a greenhouse,a wireless sensor network(WSN)system was developed to monitor greenhouse environmental parameters in real time,including air temperature,humidity,CO_(2) concentration,soil temperature,soil moisture,and light intensity.The WSN system includes several sensor nodes,a gateway node,and remote management software.The sensor nodes can collect 0-5 V and 4-20 mA analog signals and universal asynchronous receiver/transmitter(UART)data.The gateway node can process and transmit the data and commands between sensor nodes and remote management software.The remote management software provides a friendly interface between user and machine.Users can inquire about real-time data,and set the parameters of the WSN.The photosynthetic rate of tomato plants were studied in the flowering stage.A LI-6400XT portable photosynthesis analyzer was used to measure the photosynthetic rates of the tomato plants,and the environmental parameters of leaves were controlled according to the presetting rule.The photosynthetic rate prediction model of a single leaf was established based on a back propagation neural network(BPNN).The environmental parameters were used as input neurons after being processed by principal component analysis(PCA),and the photosynthetic rate was taken as the output neuron.The performance of the prediction model was evaluated,and the results showed that the correlation coefficient between the simulated and observed data sets was 0.9899,and root-mean-square error(RMSE)was 1.4686.Furthermore,when different CO_(2) concentrations were selected as the input to predict the photosynthetic rate,the simulated and observed data showed the same trend.According to the above analysis,it was concluded that the model can be used for quantitative regulation of CO_(2) for tomato plants in greenhouses.
基金This work was supported by the National Natural Science Fund(Grant No.31271619)the Doctoral Program of Higher Education of China(Grant No.20110008130006).
文摘In order to improve the efficiency of CO2 fertilizer and promote high quality and yield,it is necessary to precisely control CO2 fertilizer by wireless sensor network based on a model of photosynthetic rate prediction in greenhouse.An experiment was carried out on tomato plants in greenhouse for photosynthetic rate prediction modeling combined rough set and BP neural network.In data acquiring phase,plants growth information and greenhouse environmental information that may have influences on photosynthetic rate,including plant height,stem diameter,the number of leaves and chlorophyll content of functional leaves,air temperature,air humidity,light intensity,CO2 concentration and soil moisture,which were measured.And LI-6400XT photosynthetic rate instrument was used for obtaining net photosynthetic rate of functional leaf.After preliminary processing,135 sets of data were obtained.And twelve of them were used for model test of neural network,while the others were used for modeling.All of the data were normalized before modeling.Two models were built to predict photosynthetic rate based on BP neural network.One had total nine input parameters.The other had six input parameters,chlorophyll content,air temperature,air humidity,light intensity,CO2 concentration,and soil moisture,which were reducted from original nine based on attributes reduction theory of rough set.Both two models have one output parameter,the net photosynthetic rate of single leaf.The genetic algorithm was adopted to reduct attributes.Since continuous data cannot be processed by rough set,the K-mean cluster method was used to discretize the data of nine input parameters before attributes reduction.The prediction results of two models showed that the model with six input parameters had a mean absolute error of 0.6958,an average relative error of 7.28%,a root-mean-square error of 0.7428,and a correlation coefficient of 0.9964,while the other model respectively had 0.4026,4.53%,0.3245 and 0.9965,which proved that the model with minimum attributes had higher prediction accuracy.On the other hand,the number of iterations was used to represent the neural network train speed.The result showed that the model with six input parameters had an iteration of 544,while the other had 1038.Hence,the reduction model was applied to controlling CO2 concentration.The net photosynthetic rates at different CO2 concentrations were predicted at a certain condition.The results had the same curve trend with theory analysis,and a high prediction accuracy,which proved that the model was useful for CO2 concentration control.
基金This study was supported by the Chinese High Technology Research and Development Research Fund(2016YFD0300600-2016YFD0300606,2016YFD0300600-2016YFD0300610)NSFC program(31501219)+1 种基金the Fundamental Research Funds for the Central Universities(2018TC020,2018XD003)Industry Research Project(QingPu 2017-12).
文摘In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy technology.The spectral reflectance of maize canopy was measured and processed following wavelet denoising and multivariate scatter correction(MSC)to reduce the noise influence.Firstly,the signal to noise ratio(SNR)and curve smoothness(CS)were used to evaluate the denoising effect of different wavelet functions and decomposition levels.As a result,the Sym6 wavelet basis function and the 5th level decomposition were determined to denoise the original signal.The MSC method was used to eliminate the scattering effect after denoising.Then three spectral ranges were extracted by interval partial least squares(IPLS)including the 525-549 nm,675-749 nm and 850-874 nm.Finally,the chlorophyll content estimation model was developed by using support vector regression(SVR)method.The calibration Rc2 of the SVR model was 0.831,the RMSEC was 1.3852 mg/L;the validation Rv2 was 0.809,the RMSEP was 0.8664 mg/L.The results show that the SNR and CS indicators can be used to select the parameters for wavelet denoising and model can be used to estimate the chlorophyll content of maize canopy in the field.
基金This work was supported by the Natural Science Foundation of China under project No.40635027 and Changjiang Scholars and Innovative Research Team in University。
文摘Soil infiltration is the process by which water on the soil surface penetrates the soil.Quantifying the soil infiltration capacity(soil infiltrability)is very important for determining components of the hydrological modeling,irrigation design and many other natural or man made processes.In this paper,commonly used methods for soil infiltration rate measurement with their principles and application conditions are introduced.The advantages and disadvantages of each method under various application conditions are discussed for comparison.Three new methods for soil infiltrability measurement,including the corresponding algorithm models,and the experimental apparatus and procedures are introduced.These analyses should facilitate the choice of method used for soil infiltrability measurement.
基金supported by General Program of National Natural Science Foundation of China(Grant No.31671578)National High Technology Research and Development Program of China(Grant No.2013AA102302).
文摘Square wave anodic stripping voltammetry(SWASV)is an effective method for the detection of Cd(II),but the presence of Pb(II)usually has some potential and negative interference on the SWASV detection of Cd(II).In this paper,a novel method was proposed to predict the concentration of Cd(II)in the presence of Pb(II)based on the combination of chemically modified electrode(CME),machine learning algorithms(MLA)and SWASV.A Bi film/ionic liquid/screen-printed electrode(Bi/IL/SPE)was prepared and used for the sensitive detection of trace Cd(II).The parameters affecting the stripping currents were investigated and optimized.The morphologies and electrochemical properties of the modified electrode were characterized by scanning electron microscopy(SEM)and SWASV.The measured SWASV spectrograms obtained at different concentrations were used to build the mathematical models for the prediction of Cd(II),which taking the combined effect of Cd(II)and Pb(II)into consideration on the SWASV detection of Cd(II),and to establish a nonlinear relationship between the stripping currents of Pb(II)and Cd(II)and the concentration of Cd(II).The proposed mathematical models rely on an improved particle swarm optimization-support vector machine(PSO-SVM)to assess the concentration of Cd(II)in the presence of Pb(II)in a wide range of concentrations.The experimental results suggest that this method is suitable to fulfill the goal of Cd(II)detection in the presence of Pb(II)(correlation coefficient,mean absolute error and root mean square error were 0.998,1.63 and 1.68,respectively).Finally,the proposed method was applied to predict the trace Cd(II)in soil samples with satisfactory results.
基金the National Key Research and Development Program(Grant No.2016YFD0200602)National Natural Science Fund(Grant No.31271619).
文摘CO_(2)concentration is an environmental factor affecting photosynthesis and consequently the yield and quality of tomatoes.In this study,a photosynthesis prediction model for the entire growth stage of tomatoes was constructed to elevate CO_(2)level on the basis of crop requirements and to evaluate the effect of CO_(2)elevation on leaf photosynthesis.The effect of CO_(2)enrichment on tomato photosynthesis was investigated using two CO_(2)enrichment treatments at the entire growth stage.A wireless sensor network-based environmental monitoring system was used for the real-time monitoring of environmental factors,and the LI-6400XT portable photosynthesis system was used to measure the net photosynthetic rate of tomato leaf.As input variables for the model,environmental factors were uniformly preprocessed using independent component analysis.Moreover,the photosynthesis prediction model for the entire growth stage was established on the basis of the support vector machine(SVM)model.Improved particle swarm optimization(PSO)was also used to search for the best parameters c and g of SVM.Furthermore,the relationship between CO_(2)concentration and photosynthetic rate under varying light intensities was predicted using the established model,which can determine CO_(2)saturation points at the various growth stages.The determination coefficients between the simulated and observed data sets for the three growth stages were 0.96,0.96,and 0.94 with the improved PSO-SVM and 0.89,0.87,and 0.86 with the original PSO-SVM.The results indicate that the improved PSO-SVM exhibits a high prediction accuracy.The study provides a basis for the precise regulation of CO_(2)enrichment in greenhouses.
基金supported by the 2011 Collaborative Innovation Center of the Southern Mountain Orchard Intelligent Management Technology and Equipment of Jiangxi Province(Jiangxi Finance Instruction No.156 [2014])the National Key R&D Program of China(2016YFD0200703)
文摘Huanglongbing (HLB, citrus greening) is one of the most serious quarantine diseases of citrus worldwide. To monitor in real-time, recognize diseased trees, and efficiently prevent and control HLB disease in citrus, it is necessary to develop a rapid diagnostic method to detect HLB infected plants without symptoms. This study used Newhall navel orange plants as the research subject, and collected normal color leaf samples and chlorotic leaf samples from a healthy orchard and an HLB-infected orchard, respectively. First, hyperspectral data of the upper and lower leaf surfaces were obtained, and then the polymerase chain reaction (PCR) was used to detect the HLB bacterium in each leaf. The PCR test results showed that all samples from the healthy orchard were negative, and a portion of the samples from the infected orchard were positive. According to these results, the leaf samples from the orchards were divided into disease-free leaves and HLB-positive leaves, and the least squares support vector machine recognition model was established based on the leaf hyperspectral reflectance. The effect on the model of the spectra obtained from the upper and lower leaf surfaces was investigated and different pretreatment methods were compared and analyzed. It was observed that the HLB recognition rate values of the calibration and validation sets based on upper leaf surface spectra under 9-point smoothing pretreatment were 100% and 92.5%, respectively. The recognition rate values based on lower leaf surface spectra under the second-order derivative pretreatment were also 100% and 92.5%, respectively. Both upper and lower leaf surface spectra were available for recognition of HLB-infected leaves, and the HLB PCR-positive leaves could be distinguished from the healthy by the hyperspectral modeling analysis. The results of this study show that early and nondestructive detection of HLBinfected leaves without symptoms is possible, which provides a basis for the hyperspectral diagnosis of citrus with HLB.
基金supported by the Yunnan Academician Expert Workstation(Li Minzan,Grant No.20170907).
文摘Given the lack of technical conditions and research methods,instruments that can measure the canopy apparent photosynthetic rate have low precision and are rarely studied.Comparative studies on canopy apparent photosynthetic rate and single leaf photosynthetic rate are also relatively few.This study aims to measure and predict the canopy apparent photosynthetic rate of tomato.A canopy apparent photosynthetic rate measuring system,which was comprised of a wireless sensor network(WSN),an assimilation chamber,and a LI-6400XT photosynthetic rate instrument was established.The system was used to determine the greenhouse environmental parameters and CO2 absorptive capacity of the whole growth stage of tomato.A semi-closed assimilation chamber was designed as a side opening to conveniently measure the canopy apparent photosynthetic rate.WSN nodes were placed in the chamber to monitor environmental parameters,including air temperature,air humidity,and assimilation chamber temperature.A grid and pixel conversion method was used to measure the whole plant leaf areas of tomato.As a semi-closed measurement system,the assimilation chamber was used to calculate the canopy apparent photosynthetic rate.To conduct a comparative research on the canopy apparent photosynthetic rate and the single leaf photosynthetic rate,the LI-6400XT portable photosynthesis system was used to measure the single leaf photosynthetic rate,and the support vector machine was used to establish the prediction model of canopy apparent photosynthetic rate.The experimental results indicated that the correlation coefficients of the photosynthesis prediction model in the seeding and flowering stages were 0.9420 and 0.9226,respectively,showing the high accuracy of the SVM model.
基金supported by Natural Science Foundation of China under project No.40635027with financial support from State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau in China.
文摘Semi-circular open channel plays an important role in various applications and the measurement of its discharge is of interests.In this study,theoretical formulae for free overflow in a semi-circular channel are developed and presented for the discharge and wetted area relationship.The traditional discharge formulation and available experimental data are used to verify and validate the proposed relationships.The discharges calculated by using the proposed relationship show very good agreement with the experimental data sets.The results from this study supply the basis for circular weir development.
基金supported by National Natural Science Foundation of China (No. 32071899)Walmart Foundation (No.61626817)Walmart Food Safety Collaboration Center for its great support。
文摘Foodborne pathogenic bacteria have been considered as a major risk factor for food safety. It is of great significance to carry out in-field screening of pathogenic bacteria to prevent the outbreaks of foodborne diseases. In this study, a portable lab-on-a-disc platform with a microfluidic disc was developed for rapid and automatic detection of Salmonella typhimurium using a nickel nanowire(Ni NW) net for effective separation of target bacteria, horseradish peroxidase nanoflowers(HRP NFs) for efficient amplification of biological signals, and a self-developed smartphone APP for accurate analysis of colorimetric images. First,the microfluidic disc was preloaded with reagents and samples and centrifuged to form one bacterial sample column, one immune Ni NW column, one HRP NF column, two washing buffer columns and one tetramethylbenzidine(TMB) column, which were separated by air gaps. Then, a rotatable magnetic field was specifically developed to assemble the Ni NWs into a net, which was automatically controlled by a stepped motor to successively pass through the sample column for specific capture of target bacteria, the HRP NF column for specific label of target bacteria, the washing columns for effective removal of sample background and non-specific binding NFs, and the TMB column for colorimetric determination of target bacteria. The color change of TMB from colorless to blue was finally analyzed using the smartphone APP to quantitatively determine the target bacteria. This lab-on-a-disc platform could detect Salmonella typhimurium from 5.6 × 10^(1) CFU/20 μL to 5.6 × 10^(5) CFU/20 μL in 1 h with a lower detection limit of 56 CFU/20 μL. The recovery of target bacteria in spiked chicken samples ranged from 97.5% to 101.8%. This portable platform integrating separation, labeling, washing, catalysis and detection onto a single disc is featured with automatic operation, fast reaction, and small size and has shown its potential for in-field detection of foodborne pathogens.
基金This study was supported by the Chinese National Key Research and Development Plan(2016YFD0700300-2016YFD0700304)the National Natural Science Foundation of China(31801265).
文摘A soil electrical conductivity(EC)measurement system based on direct digital synthesizer(DDS)and digital oscilloscope was developed.The system took the“current-voltage four-electrode method”as the design principal and adopted a six-pin structure of the probe,two center pins to measure the soil EC in shallow layer,two outside pins to measure the soil EC in deep layer,and two middle pins for inputting the driving current.A signal generating circuit using DDS technology was adopted to generate sine signals,which was connected with the two middle pins.A digital oscilloscope was used to record and store the two soil output signals with noises in microseconds,which were from the two center pins and two outside pins,respectively.Then a digital bandpass filter was used to filter the soil output signals recorded by the digital oscilloscope.Compared with the traditional analog filter circuit,the digital filter could filter out the noises of all frequency except for the frequency of the excitation source.It could improve the effect of filtering and the accuracy of the soil EC measurement system.The DDS circuit could provide more stable sine signals with larger amplitudes.The use of digital oscilloscope enables us to analyze the soil output signals in microseconds and measure the soil EC more accurately.The new soil EC measurement system based on DDS and digital oscilloscope can provide a new effective tool for soil sensing in precision agriculture.
基金supported by the Chinese Academy of Engineering(2018-ZD-02-04-01)。
文摘In recent years,the outbreaks of foodborne diseases caused by pathogenic bacteria have made considerable economic losses and shown a threat to public health.The key to prevent and control these diseases is fast screening of pathogenic bacteria,which is usually performed with three procedures:sample collection,bacteria separation and bacteria detection.For sample collection,the national standard methods are often employed.For bacteria detection,currently available methods such as Polymerase Chain Reaction and Enzyme Linked Immuno-Sorbent Assay are often used.For bacteria separation,traditional methods such as filtration and centrifugation are not capable to specifically separate the target bacteria.However,food samples are very complicated and require efficient pretreatment for bacteria separation and concentration to achieve accurate and reliable results.The conventional immune magnetic separation method can be used to specifically separate the bacteria,but it still cannot meet the requirements for food sample pretreatment due to very low concentration of target bacteria in food.Therefore,this study developed an automatic and efficient immuno-separator of foodborne bacteria based on magnetophoresis and magnetic mixing,and E.coli O157:H7 was used as research model.A magnetic mixer was applied to facilitate the immunoreaction between the immune magnetic nanoparticles and the target bacteria cells,and a magnetophoretic separation tubing was utilized for magnetophoretic separation of the magnetic bacteria.Under the optimal mixing time of 20 min and the optimal flow rate of 50μL/min,the separation efficiency of E.coli O157:H7 could be more than 90%,showing that the developed immuno-separator is promising to be applied for efficient separation of foodborne bacteria and can be easily extended for separation of other biological targets by using their specific antibodies.
