A novel nondestructive detection approach for seed cotton lint percentage using deep learning

Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.

Experimental study on the compressive strength of grouted concrete block masonry based on nondestructive detection methods

Existing nondestructive detection methods were adopted to test the compressive strength of grouted concrete block masonry,i.e.the rebound method,pulling-out method and core drilling method were employed to test the strength of block,mortar and grouted concrete,respectively.The suitability of these methods for the testing of strength of grouted concrete block masonry was discussed,and the comprehensive strength of block masonry was appraised by combining existing nondestructive or micro-destructive detection methods.The nondestructive detection test on 25 grouted concrete block masonry specimens was carried out.Experimental results show that these methods mentioned above are applicable for the strength detection of grouted concrete block masonry.Moreover,the formulas of compressive strength,detection methods and proposals are given as well.

Application of ultrasonic CT method in nondestructive detection of interior defects in large scale concrete structural member of bridge

The ultrasonic computed tomography （USCT） method is derived from the basic principles of X-ray section scanning. This method records the arriving times of ultrasonic wave between the probes and the sources to ealculate the elastic wave velocity values in the section using the arrival times. Through analyzed the distribution Of elastic wave velocity in aim area, the information of the strength and the homogeneity of the investigated zone could be got indirectly. The authors introduced the operational principle of USCT and a practical case of using this method to detect the interior defects in large scale concrete structural member. Compared with other exploration methods, this method is more efficient and accurate.

Nondestructive detection of infertile hatching eggs based on spectral and imaging information

In order to quickly distinguish infertile eggs from fertile eggs,the hyperspectral imaging technology consisting of imaging and spectral information was used for detecting the fertile information of eggs.Before hatching eggs were incubated,a hyperspectral imaging system(wavelength between 400 to 1000 nm)was used to acquire the images one-by-one manually.The characteristic information of ratios of length to short axis,elongation,roundness and the ratios of the yolk area to the whole area was extracted based on the images.The normalization method was used as the spectral data preprocessing,and then 155 spectral characteristic variables were extracted from 520 nm waveband through the correlation coefficient method.Principal component analysis(PCA)method was adopted to reduce the dimensions of image-spectrum fusion information;the top six principal components were extracted.Support vector machine(SVM)method was used to establish classification of fertile and infertile eggs models,which are based on image,spectrum and image-spectrum fusion information respectively.The accuracy rates of the SVM models were 84.00%,90.00%and 93.00%respectively.The experimental results show that the model based on image-spectrum fusion information technology is superior to the single information model.Hyperspectral transmission imaging technology is effective and feasible to detect the fertile hatching eggs before incubation.

RESEARCH AND PRACTICE ON NONDESTRUCTIVE FLAW DETECTION INSTALLATION FOR WIRE-CORE BELT

Electromagnetic self-induction theory and computer are adopted and study of online monitoring technique for wire-core belt is conducted, the study shows that there is direct proportion between distance Ⅰ of broken ends and output volt Ⅴ, when Ⅰ ≥60 mm, Ⅴ keeps constantly, the running speed v of wire-core belt has no big effect on output volt Ⅴ, there is inverse proportion between the height h from probe to the surface of the belt and output volt Ⅴ, when h≥30 mm, Ⅴ tends to be zero. Based on the test result, on-line monitoring installation is developed, the practice proved that the accuracy of broken wire monitoring can be above 95%, the monitoring accuracy of joint twitch can be 0 .04 Ⅴ/mm.

Application of Laser Speckle Technology on the Detection of Agricultural Products

[Objective] The research aimed at investigating the application of laser speckle technology in agricultural products detection.[Method] This article described the basic principle of the laser speckle technology,provided a review of application development of the laser speckle technology in agricultural products detection,analyzed the problems in agriculture products detection using laser speckle technology and described the prospects of laser speckle technology in agricultural products detection.[Result] The laser speckle technology is a non-destructive detection technology for quality determination of agricultural products,which can be used to classify the agricultural products reasonably according to the quality of agricultural products.[Conclusion] The article provided reference and consult for laser speckle detection technology research.

Nondestructive determination of soluble solids and firmness in mix-cultivar melon using near-infrared CCD spectroscopy

Nondestructive evaluation of melon quality is in great need of comprehensive study.Soluble solids content(SSC)and firmness are the two indicators of melon internal quality that mostly a®ect consumer acceptance.To provide guidance for fruit classification,internal quality standards was preliminarily established through sensory test,as:Melon with SSC over 12
Brix,firmness 4–5.5 kgf·cm^(-2)2 were considered as satisfactory class sample;and SSC over 10
Brix,¯rmness 3.5–6.5 kgf·cm^(-2) as average class sample.The near infrared(NIR)nondestructive detection program was set as spectra collected from the stylar-end,Brix expressed by the average SSC of inner and outer mesocarp,each cultivar of melon was detected with its own optimum integration time,and the second derivative algorithm was used to equalize them.Using wavelength selected by genetic algorithms(GA),a robust SSC model of mix-cultivar melon was established,the root mean standard error of cross-validation(RMSECV)was 0.99 and the ratio performance deviation(RPD)nearly reached 3.0,which almost could meet the accuracy requirement of 1.5
Brix.Firmness model of mix-cultivar melon was acceptable but inferior.

Nonlinear ultrasonic detection for curing properties of the liner

The relationship between ultrasonic nonlinearity and microstructure of the liner was studied during the whole curing process by ultrasonic transmission method and infrared spectroscopy.Nonlinearity of input instrumentation was minimized by the natural filtering effect of piezoelectric discs and the maximum excitation energy was acquired simultaneously so as to improve the accuracy of the measuring data.The experimental results indicate that in the liner curing reaction at40℃ultrasonic nonlinearity parameter decreases gradually after a sharp decline,which is consistent with the outcome of infrared spectroscopy as the curing degree increases.The research suggests an effective nondestructive approach to detect the curing properties of the liner in a nonlinear ultrasonic way.

Experimental investigation of subsurface damage depth of lapped optics by fluorescent method

Subsurface defects were fluorescently tagged with nanoscale quantum dots and scanned layer by layer using confocal fluorescence microscopy to obtain images at various depths. Subsurface damage depths of fused silica optics were characterized quantitatively by changes in the fluorescence intensity of feature points. The fluorescence intensity vs scan depth revealed that the maximum fluorescence intensity decreases sharply when the scan depth exceeds a critical value. The subsurface damage depth could be determined by the actual embedded depth of the quantum dots. Taper polishing and magnetorheological finishing were performed under the same conditions to verify the effectiveness of the nondestructive fluorescence method. The results indicated that the quantum dots effectively tagged subsurface defects of fused-silica optics, and that the nondestructive detection method could effectively evaluate subsurface damage depths.

Measurement of soluble solids content in watermelon by Vis/NIR diffuse transmittance technique

Watermelon is a popular fruit in the world with soluble solids content （SSC） being one of the major characteristics used for assessing its quality. This study was aimed at obtaining a method for nondestructive SSC detection of watermelons by means of visible/near infrared （Vis/NIR） diffuse transmittance technique. Vis/NIR transmittance spectra of intact watermelons were acquired using a low-cost commercially available spectrometer operating over the range 350-1000 nm. Spectra data were analyzed by two multivariate calibration techniques： partial least squares （PLS） and principal component regression （PCR） methods. Two experiments were designed for two varieties of watermelons [Qilin （QL）, Zaochunhongyu （ZC）], which have different skin thickness range and shape dimensions. The influences of different data preprocessing and spectra treatments were also investigated. Performance of different models was assessed in terms of root mean square errors of calibration （RMSEC）, root mean square errors of prediction （RMSEP） and correlation coefficient （r） between the predicted and measured parameter values. Results showed that spectra data preprocessing influenced the performance of the calibration models. The first derivative spectra showed the best results with high correlation coefficient of determination [r=0.918 （QL）; r=0.954 （ZC）], low RMSEP [0.65 °Brix （QL）; 0.58 °Brix （ZC）], low RMSEC [0.48 °Brix （QL）; 0.34°Brix （ZC）] and small difference between the＇RMSEP and the RMSEC by PLS method. The nondestructive Vis/NIR measurements provided good estimates of SSC index of watermelon, and the predicted values were highly correlated with destructively measured values for SSC. The models based on smoothing spectra （Savitzky-Golay filter smoothing method） did not enhance the performance of calibration models obviously. The results indicated the feasibility of Vis/NIR diffuse transmittance spectral analysis for predicting watermelon SSC in a nondestructive way.

Determination of potassium sorbate and sorbic acid in agricultural products using THz time-domain spectroscopy

t The aim of this study was to investigate the feasibility of detecting potassium sorbate(PS)and sorbic acid(SA)in agricultural products using THz time-domain spectroscopy(THz-TDS).The absorption spectra of PS and SA were measured from 0.2 to 1.6 THz at room temperature.The main characteristic absorption peaks of PS and SA in polyethylene and powdered agricultural products with different weight ratios were detected and analyzed.Interval partial least squares(iPLS)combined with a particle swarm optimization and support vector classification(PSO-SVC)algorithm was proposed in this paper.iPLS was used for frequency optimization,and the PSO-SVC algorithm was used for spectrum analysis of the preservative content based on the optimal spectrum ranges.Optimized PSO-SVC models were obtained when the THz spectrum from the PS/SA mixture was divided into 11 or 12 subintervals.The optimal penalty parameter c and kernel parameter g were found to be 1.284 and 0.863 for PS(0.551-1.487 THz),1.374 and 0.906 for SA(0.454-1.216 THz),respectively.The preliminary results indicate that THz-TDS can be an effective nondestructive analytical tool used for the quantitative detection of additives in agricultural products.

An Improved Damage Identification Method Based on Mode Information

This paper presents a new algorithm to predict locations and severities of damage in structures by changing modal parameters. An existing algorithm of damage detection is reviewed and the new algorithm is formulated to improve the accuracy of damage locating and severity estimation by eliminating the erratic assumptions and limits in the existing algorithm. The damage prediction accuracy is numerically assessed for each algorithm when applied to a two-dimensional frame structure for which pre-damage and post-damage modal parameters are available for only a few modes of vibration. The analysis results illustrate the improved accuracy of the new algorithm when compared to the existing algorithm.

Theoretical and experimental research on characteristics of lateral vibration for a pre-stress bolt supporting system

According to the structure and stress trait of bearing bolts,a lateral-vibrationmechanics model was established for them,and the relation between lateral-vibration frequencyand axial load was analyzed;then,lateral-vibration trait of bearing bolts was studiedthrough laboratory simulation test.The results indicate that vibration frequency of boltsupport system increases as well as axial force,the detection on axial load of bolts can bemade by generating lateral vibration of bearing bolts.Theoretical and experimental researchresults show that frequency method is effective for detecting the axial force of boltsupport system.

Nondestructive perception of potato quality in actual online production based on cross-modal technology

Nowadays,China stands as the global leader in terms of potato planting area and total potato production.The rapid and nondestructive detection of the potato quality before processing is of great significance in promoting rural revitalization and augmenting farmers’income.However,existing potato quality sorting methods are primarily confined to theoretical research,and the market lacks an integrated intelligent detection system.Therefore,there is an urgent need for a post-harvest potato detection method adapted to the actual production needs.The study proposes a potato quality sorting method based on cross-modal technology.First,an industrial camera obtains image information for external quality detection.A model using the YOLOv5s algorithm to detect external green-skinned,germinated,rot and mechanical damage defects.VIS/NIR spectroscopy is used to obtain spectral information for internal quality detection.A convolutional neural network(CNN)algorithm is used to detect internal blackheart disease defects.The mean average precision(mAP)of the external detection model is 0.892 when intersection of union(IoU)=0.5.The accuracy of the internal detection model is 98.2%.The real-time dynamic defect detection rate for the final online detection system is 91.3%,and the average detection time is 350 ms per potato.In contrast to samples collected in an ideal laboratory setting for analysis,the dynamic detection results of this study are more applicable based on a real-time online working environment.It also provides a valuable reference for the subsequent online quality testing of similar agricultural products.

Detection of rice seed vigor by low-field nuclear magnetic resonance

A new method to predict the seed vigor of rice was developed to control adulteration during the seed trading process and to address the deficiencies of traditional manual detection methods.Low-field nuclear magnetic resonance(LF-NMR)technique was used to detect the vigor of rice seeds.Four varieties(Beijing-1,Qianchonglang-2,Yanfeng-47 and Shennong-265)of rice seeds from the Rice Research Institute of Shenyang Agricultural University were chosen for the experiment.The transverse relaxation time T_(2),T_(21) and T_(22) were observed in the experiment.The peak start time of free water(transverse relaxation time T_(22)),signal amplitude of bound water(transverse relaxation time T_(21)),and moisture content decreased with the decrease in the vigor of the seeds.There were no obvious trends observed for the top of the peak and the end point of the transverse relaxation time T_(22).In addition,the start,top,and end time of the peak(transverse relaxation time T_(21)),and the signal amplitude of bound water showed no consistent changes.The results indicated that LF-NMR could be used as a method to distinguish the vigor of rice seeds rapidly.This study provided theoretical basis and technical support for the rapid detection of rice seed vigor.

Online automatic grading of salted eggs based on machine vision

The quality of salted eggs differs in curing process.They need to be tested and graded before factory packaging.The dynamic images of salted eggs were acquired on conveyor.Firstly,preprocessing of color images must be done:the target area of the binary image was determined by mathematical morphology and removal of the object of a small area.According to the binary image is a convex or concave figure,the target region light leaked or not was determined.The effects of leaked region were eliminated by searching for mutation points,fitting salted egg boundary by the Least Square algorithm,labeling the binary image and extracting single target area.Then,six characteristic parameters were extracted in color space,and quality testing model was established by minimum error probability.The experimental results indicated that the detection accuracy reached above 93%and classification efficiency was 5400/h.It is proved the model is feasible for salted egg grading.

Early diagnosis and monitoring of nitrogen nutrition stress in tomato leaves using electrical impedance spectroscopy

Nitrogen(N)is a life element for crop growth.In tomato growth and development,N stress often occurs and degrades crop yield and quality.Superfluous N can noticeably increase the nitrate content,which can be degraded into strong carcinogenic substance-nitrite.An accurate and timely monitoring and diagnosis of nutrition during crop growth is premise to realize a precise nutrient management.Crop N monitoring methods have been developed to improve N fertilizer management,and most of them are based on leaf or canopy optical property measurements.Although many optical/spectral plant N sensors have already commercialized for production use,low accuracy for phosphorus(P)and potassium(K)detection and diagnosis remains an important drawback of these methods.To explore the potential of N diagnosis by electrical impedance and perform study for nutrition status of plant NPK meanwhile by the electrical impedance,it is necessary that evaluate the N nutrition level by leaf impedance spectroscopy.Electrical impedance was applied to determine the physiological and nutritional status of plant tissues,but few studies related to plant N contents have been reported.The objective of this study was to evaluate the N nutrition level by leaf impedance spectroscopy and realize the early diagnosis and monitoring of N nutrition stress in tomato.Five sets of tomato plant samples with different N contents were cultivated in a Venlo greenhouse.N content of leaves was determined,and electrical impedance data were recorded in a frequency range of 1 Hz to 1 MHz.The obtained impedance data were analyzed using an equivalent circuit model for cellular tissues.The variation of equivalent parameters along with N content was analyzed,and the sensitive impedance spectroscopy characteristics of N nutrition level were extracted.Furthermore,the effect of moisture content on impedance measurement was discussed and the prediction model for N content was developed.Results showed that electrical impedance can be conveniently applied to early diagnosis and monitoring for tomato N nutrition stress.

Discrimination of brownheart of Korla pear using vibration frequency spectrum technique

The purpose of this work was to use a nondestructive method for detecting the brownheart of Korla pear to reduce the chance of infection among pears without brownheart.A mechanical impulse method based on vibration testing system was used to excite the fruits.The consistent acquisition signal indicated that the test is repeatable at the same positions of fruit equator(cheek).A remarkable frequency signal was excited at 9-12 N force by a rubber tipped hammer.The dominant frequency was identified at the maximum response magnitude to assess the internal defect,and the result was not influenced by the distances between the defect borders and the excitation points.The sharp increase of defect mass could significantly affect the dominant frequency.Relationship between the dominant response frequency(fd)and the defect mass percentage(ω)was characterized by an equation fd=410.649e-0.0833ω+261.947 with a good correlation coefficient(R2=0.925).A defect mass of 2.281%was determined as a discrimination threshold.Once the threshold exceeded 2.281%,the defective pear could be classified with a high accuracy rate of 96.7%.This finding would provide guidance for determining the optimal detecting time to the brownheart of Korla pears,according to the specific storage conditions when the vibration frequency spectrum method is deployed.

Study of imaging unknown objects by cosmic-ray muons

Introduction Cosmic-ray muon imaging is a kind of nondestructive detection technology which can be used to detect unknown objects in geological exploration,civil engineering and nuclear safety.Transmission imaging and scattering tomography schemes are studied.Method The transmission scheme uses a multilayer detector to measure the direction of a cosmic-ray muon passing through an object.The scattering scheme involves placing two detectors upstream and downstream of the object to record the incident and exit directions of the muon passing through the object.The effect of the detector resolution on the imaging clarity of transmission imaging was studied.The applicable scenarios of the two schemes were analyzed.Results The results by calculating show that in the transmission imaging of a hundred-meter object,a spatial resolution of 2.5 m can be achieved,and Cu and Fe can be discriminated with a density difference of 1.1 g/cm3.Scattering tomography is mainly suitable for meter-level objects,which can detect 0.2 m chamber and distinguish 0.05 m heavy metal blocks in rock.

Predicting bruise susceptibility in apples using Vis/SWNIR technique combined with ensemble learning

Bruise susceptibility in fruits is an important indicator in evaluating risk factors for bruising caused by external factors.Prediction of the bruising susceptibility of fruit can provide useful information for proper postharvest handling and storage operations.In this study,visible and shortwave near-infrared(Vis/SWNIR)technique was used to develop nondestructive method for predicting the bruise susceptibility of apples.Vis/SWNIR spectra covering 400-1100 nm were collected for 300‘Golden Delicious’apples over a time period of three weeks after harvest.A pendulum-like device was used to simulate impact bruise at three impact energy levels of 1.11 J,0.66 J and 0.33 J.Bruise volumes were estimated from the digital images of the bruised apples by using the bruise thickness model.Three prediction models,i.e.partial least squares model(PLS),partial least squares model combined with successful projection algorithm(SPA-PLS),and selective ensemble learning based on feature selection(SELFS),for bruise susceptibility were developed for each impact energy level as well as for the pooled data.Compared with PLS and SPA-PLS model,SELFS gave the better prediction results for bruise susceptibility,with the correlation coefficient of R_(p)=0.800-0.886 for the prediction set,the root-mean-square error of 38.7-62.1 mm^(3)/J for the prediction set(RMSEP),and the residual predictive deviation(RPD)of 1.78-2.14 for three impact energy level.For three impact energy levels,the RMSEP and RPD value obtained by SELFS model improved by 14.8%-20.0%and 15.0%-24.5%compared to PLS model,and 11.4%-21.2%and 11.5%-27.1%compared to SPA-PLS model,respectively.The SELFS model achieved relatively lower prediction accuracies for the pooled data,with the R_(p) values of 0.731,RMSEP of 85.46 mm^(3)/J,and RPD of 1.46,which were also better than that of PLS model and SPA-PLS model.This research demonstrated that Vis/SWNIR technique combined with ensemble learning is promising technique for rapid assessment of bruise susceptibility of fruit,which would be useful for postharvest handling of fruit.