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

Journal of Cotton Research 2024年第2期7卷 148-162页

作者： GENG Lijie YAN Pengji JI Zhikun SONG Chunyu SONG Shuaifei ZHANG Ruiliang ZHANG Zhifeng ZHAI Yusheng JIANG Liying YANG Kun School of Electronic and Information Henan Key Laboratory of Magnetoelectronic Information Functional MaterialsZhengzhou University of Light IndustryZhengzhou 450002HenanChina

Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its *** traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is *** recent years,classification-based deep learning and computer vision have shown promise in solving various classification *** In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer *** model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed *** evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of *** 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 *** proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.

关键词： Neural network MobileNetV2 nondestructive detection Smart agriculture Seed cotton lint percentage

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

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International Journal of Agricultural and Biological Engineering 2015年第4期8卷 69-76页

作者： Zhu Zhihui Liu Ting Xie Dejun Wang Qiaohua Ma Meihu College of Engineering Huazhong Agricultural UniversityWuhan 430070China National R&D Center for Egg Processing College of Food Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China

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 *** hatching eggs were incubated,a hyperspectral imaging system(wavelength between 400 to 1000 nm)was used to acquire the images one-by-one *** 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 *** 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 *** component analysis(PCA)method was adopted to reduce the dimensions of image-spectrum fusion information;the top six principal components were *** 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 *** accuracy rates of the SVM models were 84.00%,90.00%and 93.00%*** experimental results show that the model based on image-spectrum fusion information technology is superior to the single information *** transmission imaging technology is effective and feasible to detect the fertile hatching eggs before incubation.

关键词： hyperspectral image hatching eggs information fusion nondestructive detection

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nondestructive detection of Brown Core in Chinese Pear 'Yali' by Transmission Visible-NIR Spectroscopy

Nondestructive Detection of Brown Core in Chinese Pear 'Yali...

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国际农业工程大会

作者： Runlin Tu, Graduate Student College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China Donghai Han, Associate professor Xinxin Liu, Graduate Student Zhaohui Wen, Graduate Student

Brown core is an internal disorder sometimes seen in pears under controlled atmosphere (CA) storage. The symptoms are not externally recognizable and visible only after cutting the fruit. Development of nondestructive measurements of brown core of pears benefits the producers, processors, and packers. A technique using body transmittance in the (651-1282) nm region was evaluated as a nondestructive technique for identifying Chinese pear ’Yali’ with brown core. Based on (651282) nm region, the discriminant analysis with Mahalanobis Distance

关键词： Chinese Pear ’Yali’ grading visible-NIR spectroscopy nondestructive detection brown core

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

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Chinese Physics B 2020年第9期29卷 122-128页

作者：蒋玉英李广明吕明葛宏义张元 Key Laboratory of Grain Information Processing and Control(Henan University of Technology) Ministry of EducationZhengzhou 450001China Henan Provincial Key Laboratory of Grain Photoelectric detection and Control Zhengzhou 450001China College of Information Science and Engineering Henan University of TechnologyZhengzhou 450001China

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 *** main characteristic absorption peaks of PS and SA in polyethylene and powdered agricultural products with different weight ratios were detected and *** partial least squares(iPLS)combined with a particle swarm optimization and support vector classification(PSO-SVC)algorithm was proposed in this *** was used for frequency optimization,and the PSO-SVC algorithm was used for spectrum analysis of the preservative content based on the optimal spectrum *** PSO-SVC models were obtained when the THz spectrum from the PS/SA mixture was divided into 11 or 12 *** 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),*** preliminary results indicate that THz-TDS can be an effective nondestructive analytical tool used for the quantitative detection of additives in agricultural products.

关键词： THz-TDS preservative content quantitative analysis iPLS PSO-SVC nondestructive detection

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

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Journal of Innovative Optical Health Sciences 2015年第6期8卷 17-24页

作者： Jie Lu Shuye Qi Ran Liu Enyang Zhou Wu Li Shuhui Song Donghai Han College of Food Science and Nutritional Engineering China Agricultural University Beijing 100083P.R.China National Engineering Research Center for Vegetables(NERCV)Beijing 100097 P.R.China

nondestructive evaluation of melon quality is in great need of comprehensive *** solids content(SSC)and firmness are the two indicators of melon internal quality that mostly a®ect consumer *** provide guidance for fruit classification,internal quality standards was preliminarily established through sensory test,as:Melon with SSC over 12Brix,firmness 4–5.5 kgf·cm^(-2)2 were considered as satisfactory class sample;and SSC over 10Brix,¯rmness 3.5–6.5 kgf·cm^(-2) as average class *** 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 *** 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*** model of mix-cultivar melon was acceptable but inferior.

关键词： Melon nondestructive detection near-infrared fruit quality soluble solids content firmness

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Online automatic grading of salted eggs based on machine vision

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International Journal of Agricultural and Biological Engineering 2015年第1期8卷 35-41页

作者： Xu Kunrui Lu Xi Wang Qiaohua Ma Meihu College of Engineering Huazhong Agricultural UniversityWuhan 430070China National Research and Development Center for Egg Processing Huazhong Agricultural UniversityWuhan 430070China College of Food Science and Technology Huazhong Agricultural UniversityWuhan 430070China

The quality of salted eggs differs in curing *** need to be tested and graded before factory *** dynamic images of salted eggs were acquired on ***,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 *** to the binary image is a convex or concave figure,the target region light leaked or not was *** 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 ***,six characteristic parameters were extracted in color space,and quality testing model was established by minimum error *** experimental results indicated that the detection accuracy reached above 93%and classification efficiency was 5400/*** is proved the model is feasible for salted egg grading.

关键词： salted egg automatic grading image processing machine vision nondestructive detection

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

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International Journal of Agricultural and Biological Engineering 2017年第3期10卷 194-205页

作者： Li Meiqing Li Jinyang Wei Xinhua Zhu Wenjing Key Laboratory of Modern Agricultural Equipment and Technology Ministry of Education of the People’s Republic of ChinaInstitute of Agricultural EngineeringJiangsu UniversityZhenjiang 212013China

Nitrogen(N)is a life element for crop *** tomato growth and development,N stress often occurs and degrades crop yield and *** N can noticeably increase the nitrate content,which can be degraded into strong carcinogenic *** accurate and timely monitoring and diagnosis of nutrition during crop growth is premise to realize a precise nutrient *** N monitoring methods have been developed to improve N fertilizer management,and most of them are based on leaf or canopy optical property *** 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 *** 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 *** impedance was applied to determine the physiological and nutritional status of plant tissues,but few studies related to plant N contents have been *** 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 *** 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 *** obtained impedance data were analyzed using an equivalent circuit model for cellular *** variation of equivalent parameters along with N content was analyzed,and the sensitive impedance spectroscopy characteristics of N nutrition level were ***,the effect of moisture content on impedance measurement was discussed and the prediction model for N content was *** showed that electrical impedance can be c

关键词： electrical impedance spectroscopy nitrogen stress tomato(Solanum lycopersicum)leaves nitrogen nutrition diagnosis monitoring nondestructive detection

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

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International Journal of Agricultural and Biological Engineering 2018年第6期11卷 195-200页

作者： Ping Song Peng Song Hongwei Yang Tao Yang Jing Xu Kaitian Wang College of Information and Electrical Engineering Shenyang Agricultural UniversityShenyang 110866China Beijing Research Center for Information Technology in Agriculture Beijing 10097China National Research Center of Intelligent Equipment for Agriculture Beijing 10097China

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 ***-field nuclear magnetic resonance(LF-NMR)technique was used to detect the vigor of rice *** 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 *** transverse relaxation time T_(2),T_(21) and T_(22) were observed in 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 *** 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 *** results indicated that LF-NMR could be used as a method to distinguish the vigor of rice seeds *** study provided theoretical basis and technical support for the rapid detection of rice seed vigor.

关键词： nondestructive detection nuclear magnetic resonance transverse relaxation time signal amplitude rice seeds vigor

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Discrimination of brownheart of Korla pear using vibration frequency spectrum technique

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International Journal of Agricultural and Biological Engineering 2017年第2期10卷 259-266页

作者： Xu Hubo Wu Jie Wang Zhaopeng Gao Yongmao Wang Zhipeng Zhao Zhengqiang College of Mechanical and Electrical Engineering Shihezi UniversityShihezi 832003XinjiangChina

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 *** 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 *** 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 *** sharp increase of defect mass could significantly affect the dominant *** 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 *** 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.

关键词： nondestructive detection vibration frequency spectrum brownheart of Korla pear internal defect fruit quality

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

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International Journal of Agricultural and Biological Engineering 2017年第5期10卷 144-153页

作者： Yao Jian Guan Jiyu Zhu Qibing Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan UniversityWuxi 214122China

Bruise susceptibility in fruits is an important indicator in evaluating risk factors for bruising caused by external *** of the bruising susceptibility of fruit can provide useful information for proper postharvest handling and storage *** this study,visible and shortwave near-infrared(Vis/SWNIR)technique was used to develop nondestructive method for predicting the bruise susceptibility of ***/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 *** volumes were estimated from the digital images of the bruised apples by using the bruise thickness *** prediction models,*** 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 *** 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 *** 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,*** 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 *** research demonstrated that Vis/SWNIR technique combined with ensemble learning is promising technique for rapid assessment of

关键词： apple nondestructive detection bruise susceptibility visible/short-wave near-infrared technique ensemble learning

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