|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Target detection method for moving cows based on background subtraction显示文摘Target detection is the fundamental work for perceiving the behavior of cows using video analysis automatically.The videos captured in farming scenes often suffer from a complex background,which leads to difficulty in detecting the target and inconvenience in the subsequent images analysis.In this study,a method was proposed to detect the moving target accurately for cows based on background subtraction.Firstly,the bounding rectangle of cows was calculated using the frames difference method to extract the local background in frames,which were averaged and spliced into one image as the entire background image.Secondly,the size and location of a cow’s body were determined by the bounding rectangle of cows,and the body area was tracked through the video by the binary images.Thirdly,the summation coefficients on RGB channels were adjusted to improve the contrast between the target and background images.Finally,taking the body area in every frame as reference area,the performance of target detection was evaluated by the reference area to determine the optimal summation coefficients on RGB channels,and then background subtraction was processed again to finish the detection.A total of 129 videos were used to test the detection algorithm,and the accuracy of the algorithm was 88.34%,which was 24.85%higher than the classical background subtraction method.The study shows that the algorithm proposed in this study is feasible to detect the target accurately and timely when cows are walking straight in the farming environment under natural light,and this method can improve the detection performance and is an extension to the classical background subtraction method. | Zhao Kaixuan He Dongjian | 2015 | International Journal of Agricultural and Biological Engineering2015，8，1： | 13 |
| 2 | Intelligent monitoring method of cow ruminant behavior based on video analysis technology显示文摘To overcome the limitations of traditional dairy cow's rumination detection methods,a video-based analysis on the intelligent monitoring method of cow ruminant behavior was proposed in this study.The Mean Shift algorithm was used to track the jaw motion of dairy cows accurately.The centroid trajectory curve of the cow mouth motion was subsequently extracted from the video.In this way,the monitoring of the ruminant behavior of dairy cows was realized.To verify the accuracy of the method,six videos,a total of 99'00',24000 frames were selected.The test results demonstrated that the success rate of this method was 92.03%,despite the interference of behaviors,such as raising or turning of the cow’s head.The results demonstrate that this method,which monitors the ruminant behavior of dairy cows,is effective and feasible. | Chen Yujuan He Dongjian Fu Yinxi Song Huaibo | 2017 | International Journal of Agricultural and Biological Engineering2017，10，5： | 11 |
| 3 | Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues显示文摘Pesticide residue is an important factor that affects food safety.In order to achieve effective detection of pesticide residues in apples,a machine-vision-based segmentation algorithm and hyperspectral techniques were used to segment the foreground and background regions of the apple image.By calculating the roundness value and extracting the region with the highest roundness value in the connected region,a region of interest(ROI)maskwas created for the apple.Four pesticides(chlorpyrifos,carbendazimand two mixed pesticides)and an inactive control were used at the same concentration of 100 ppm(except for the control group),and the hyperspectral region of the corresponding sample image was extracted by obtaining the different types of pesticide residues in the ROI masks.To increase the diversity of the samples and to expand the dataset,Gaussianwhite noise with a varying signal-to-noise ratio was added to each of the hyperspectral images of the apple.The number of samples was increased from four types of 12 samples to four types of 72 samples,giving 4608 hyperspectral data images in each category.The structure and parameters of a convolutional neural network(CNN)were determined using theoretical analysis and experimental verification.All the extracted hyperspectral images of apples were normalized to 227×227×3 pixels as the input of the CNN network for pesticide residue detection.There were 18,432 sample data of four types for 72 samples.Of these,12,288 images were selected using a bootstrap sampling method as the training set,and 6144 as the test set,with no overlap.The test results showthatwhen the number of training epochswas 10,the accuracy of the test set detectionwas 99.09%,and the detection accuracy of the single-band average imagewas 95.35%.A comparison with traditional k-nearest neighbor(KNN)and support vectormachine classification algorithms showed that the detection accuracy for KNNwas 43.75%and the average time was 0.7645 s.These results demonstrate that our method is a small-sample,noncontact,fast,effective and low-cost technique that can provide effective pesticide residue detection in postharvest apples. | Bo Jiang Jinrong He Shuqin Yang Hongfei Fu Tong Li Huaibo Song Dongjian He | 2019 | Artificial Intelligence in Agriculture2019，，1： | 8 |
| 4 | Automatic detection of ruminant cows’ mouth area during rumination based on machine vision and video analysis technology显示文摘In order to realize the automatic monitoring of ruminant activities of cows,an automatic detection method for the mouth area of ruminant cows based on machine vision technology was studied.Optical flow was used to calculate the relative motion speed of each pixel in the video frame images.The candidate mouth region with large motion ranges was extracted,and a series of processing methods,such as grayscale processing,threshold segmentation,pixel point expansion and adjacent region merging,were carried out to extract the real area of cows’mouth.To verify the accuracy of the proposed method,six videos with a total length of 96 min were selected for this research.The results showed that the highest accuracy was 87.80%,the average accuracy was 76.46%and the average running time of the algorithm was 6.39 s.All the results showed that this method can be used to detect the mouth area automatically,which lays the foundation for automatic monitoring of cows’ruminant behavior. | Yanru Mao Dongjian He Huaibo Song | 2019 | International Journal of Agricultural and Biological Engineering2019，12，1： | 4 |
| 5 | Predicting wheat kernels’protein content by near infrared hyperspectral imaging显示文摘The objective of this study was to explore the potential of near infrared hyperspectral imaging combined with statistical regression models and neural networks for nondestructive prediction of protein content of wheat kernels.Seventy-nine samples from 11 breeds of wheat kernels were collected.The protein percentage of each sample measured by semimicro-Kjeldahl method was taken as the reference value.After comparing the prediction models of principal components regression(PCR)and partial least squares regression(PLSR)with various pretreatment methods,PLSR preprocessed by zero mean normalization(z score)function of MATLAB was found to obtain better prediction results than other regression models.Based on 10 latent variables of PLSR,the radial basis function(RBF)neural network was applied to improve the prediction,in which the coefficients of determination(R2)were greater than 0.92 for both the calibration set and validation set,while the corresponding RMSE values were 0.3496 and 0.4005,respectively.Therefore,hyperspectral imaging can provide a fast and non-destructive method for predicting the wheat kernels’protein content. | Yang Shuqin He Dongjian Ning Jifeng | 2016 | International Journal of Agricultural and Biological Engineering2016，9，2： | 2 |
| 6 | An improved hybrid model for automatic salient region detection显示文摘 | Liu Shangwang He Dongjian Liang Xinhong | 2012 | IEEE Signal Processing Letters2012，19，4： | 1 |
| 7 | Automatic monitoring method of cow ruminant behavior based on spatio-temporal context learning显示文摘Automatic monitoring of cow rumination has great significance in the development of modern animal husbandry.In order to solve the problem of high real-time requirement of ruminant behavior monitoring,a tracking method based on STC(Spatio-Temporal Context)learning was carried out.On the basis of cow’s mouth region extraction,the spatial context model between target object and its local surrounding background was built based on their spatial correlations by solving the deconvolution problem,and the learned spatial context model was used to update the STC learning model for the next frame.Tracking in the next frame was formulated by computing a confidence map as a convolution problem that integrates the STC learning information,and the best object location could be estimated by maximizing the confidence map.Then the target scale was estimated based on the confidence evaluation.Finally,accurate tracking of the mouth movement trajectory was realized.To verify the effectiveness of the proposed method,the performance of the algorithm was tested using 20 video sequences.Besides,the tracking results were compared with the Mean-shift algorithm.The results showed that the average success rate of STC learning monitoring algorithm was 85.45%,which was 9.45%higher than the Mean-shift algorithm,the detection rate of STC learning monitoring algorithm was 18.56 s per video,which was 22.08%higher than that of the Mean-shift algorithm.The results showed that the fast tracking method based on STC learning monitoring algorithm is effective and feasible. | Yujuan Chen Dongjian He Huaibo Song | 2018 | International Journal of Agricultural and Biological Engineering2018，11，4： | 1 |
| 8 | An adaptive segmentation method combining MSRCR and mean shift algorithm with K-means correction of green apples in natural environment显示文摘During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be solved.In this study,the multi-scale Retinex with color restoration(MSRCR)algorithm was applied to enhance the original green apple images captured in an orchard environment,aiming to minimize the impacts of varying light conditions.The enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent fruit.After that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold segmentation.Finally,the poorly segmented images were corrected using the K-means algorithm according to the shape,color and texture features.The users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this study.Compared with the manifold ranking algorithm,the K-means clustering algorithm and the traditional mean shift algorithm,the segmentation accuracy of the proposed method was 86.67%,which was 13.32%,19.82%and 9.23%higher than that of the other three algorithms,respectively.Additionally,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared algorithms.The proposed method accurately recognized the green apples under complex illumination conditions and growth environments.Additionally,it provided effective references for intelligent growth monitoring and yield estimation of fruits. | Sashuang Sun Huaibo Song Dongjian He Yan Long | 2019 | Information Processing in Agriculture2019，6，2： | 1 |
| 9 | Realistic animation of interactive trees显示文摘 | Shaojun Hu Norishige Chiba Dongjian He | 2012 | The Visual Computer2012，，6： | 1 |
| 10 | Digital Relief Generation from 3D Models显示文摘It is difficult to extend image-based relief generation to high-relief generation, as the images contain insufficient height information. To generate reliefs from three-dimensional(3D) models, it is necessary to extract the height fields from the model, but this can only generate bas-reliefs. To overcome this problem, an efficient method is proposed to generate bas-reliefs and high-reliefs directly from 3D meshes. To produce relief features that are visually appropriate, the 3D meshes are first scaled. 3D unsharp masking is used to enhance the visual features in the 3D mesh, and average smoothing and Laplacian smoothing are implemented to achieve better smoothing results. A nonlinear variable scaling scheme is then employed to generate the final bas-reliefs and high-reliefs. Using the proposed method, relief models can be generated from arbitrary viewing positions with different gestures and combinations of multiple3 D models. The generated relief models can be printed by 3D printers. The proposed method provides a means of generating both high-reliefs and bas-reliefs in an efficient and effective way under the appropriate scaling factors. | WANG Meili SUN Yu ZHANG Hongming QIAN Kun CHANG Jian HE Dongjian | 2016 | Chinese Journal of Mechanical Engineering2016，29，6： | 1 |
| 11 | Ripe Fuji apple detection model analysis in natural tree canopy显示文摘 | Huang Lvwen He Dongjian | 2012 | Elkomnika2012，10，7： | 1 |
| 12 | Detection of moldy core in apples and its symptom types using transmittance spectroscopy显示文摘A detection method based on transmittance spectroscopy and support vector machine(SVM)was proposed to achieve rapid nondestructive detection of moldy core in apples.A visible to near-infrared(Vis/NIR)spectroradiometer was used for scanning transmittance spectra of 215 apple samples in the wavelength range of 200-1025 nm.Wavelet transform was used to reduce the dimensionality of the spectra and extract wavelet coefficients.Two classification algorithms including artificial neural network(ANN)and SVM were used to develop models whose parameters were optimized by genetic algorithms(GA)for determination of the presence and types of moldy core in apples.Comparisons results of the models showed that the GA-SVM model obtained the optimal result with an accuracy of 96.92%for detecting the presence of moldy core and 81.48%for distinguishing symptom types of the disease.These results indicate that it is feasible to detect moldy core in apples nondestructively and rapidly based on transmittance spectroscopy and that wavelet transform is an effective method for extraction of characteristics from spectra.Moreover,the GA-SVM algorithm in conjunction with Vis/NIR transmittance spectroscopy can accurately achieve fast and nondestructive detection of the presence and types of moldy core in apples. | Zhou Zhaoyong Lei Yu Su Dong Zhang Haihui He Dongjian Chenghai Yang | 2016 | International Journal of Agricultural and Biological Engineering2016，9，6： | 1 |
| 13 | Semi-supervised fuzzy chstering with feature discrimination 显示文摘 | Li Longlong Jonathan G He Dongjian | 2015 | Plos One2015，10，01： | 1 |
| 14 | Model for tomato photosynthetic rate based on neural network with genetic algorithm显示文摘A photosynthetic rate model provides a theoretical basis for fine-grained control of light,and has become the key component to determine the effectiveness of light-controlled environments.Therefore,it is critical to identify an intelligent algorithm that can be used to build an efficient and precise photosynthetic rate model.Depending on the initial weights of a BP(Back Propagation)neural network algorithm for arbitrary random numbers,the establishment of a regressive prediction model can be easily trapped in a partially-flat area.Existing photosynthetic rate models based on neural networks are facing problems such as a slow convergence speed and a long training time,and this study presents a photosynthetic rate model of a heuristic neural network for tomatoes based on a genetic algorithm to address the above problems.The performance of the model can be effectively improved using a genetic algorithm to optimize the initial weights.A multi-factor nesting experiment was firstly conducted to obtain 825 groups of tomato seedling photosynthesis rate test data in the foundation,and the photosynthetic rate model of the heuristic neural network for the tomato is established through BP network structure construction and data preprocessing.The genetic algorithm was used to optimize the network weights and threshold,and the LM(Levenberg-Marquardt)training method for network training.On this basis,the training performance and precision of the photosynthetic rate prediction models can be further compared with the genetic neural network model and the neural network model.The test results have shown that the training effects and accuracy of the genetic neural network prediction model of the photosynthetic rate were better than those of the neural network prediction model.The correlation coefficient between the model predicted data and the measured data is 0.987,and the absolute error of the photosynthetic rate is less than±0.5μmol/(m^(2)·s). | Jin Hu Pingping Xin Siwei Zhang Haihui Zhang Dongjian He | 2019 | International Journal of Agricultural and Biological Engineering2019，12，1： | 0 |
| 15 | Whole-exome mutational landscape of neuroendocrine carcinomas of the gallbladder显示文摘Neuroendocrine carcinoma(NEC)of the gallbladder(GB-NEC)is a rare but extremely malignant subtype of gallbladder cancer(GBC).The genetic and molecular signatures of GB-NEC are poorly understood;thus,molecular targeting is currently unavailable.Inthe present study,we applied whole-exome sequencing(WES)technology to detect gene mutations and predicted somatic singlenucleotide variants(SNVs)in 15 cases of GB-NEC and 22 cases of general GBC.in 15 GB-NECs,the C>T mutation was predominantamong the 6 types of SNVs.TP53 showed the highest mutation frequency(73%,11/15).Compared with neuroendocrine carcinomasof other organs,signifcantly mutated genes(SMGs)in GB-NECs were more similar to those in pulmonary large-cell euroendocrinecarcinomas(LCNECs),with drver roles for TP53 and RB1.Iin the COSMIC database of cancer-related genes,211 genes were mutated.Strikingly,RB1(4/15,27%)and NAB2(3/15,20%)mutations were found specifically in GB-NECs;in contrast,mutations in 29 genes,including ERB82 and ERBB3,were identified exclusively in GBC.Mutations in RB1 and NAB2 were significanty related to downregulation of the RB1 and NAB2 proteins,respectively,according to immunohistochemical(IHC)data(p values=0.0453 and0.0303).Clinically actionable genes indicated 23 mutated genes,including ALK,BRCA1,and BRCA2.Iin addition,potential somaticSNVs predicted by ISowN and SomVarlUS constituted 6 primary coSMIC mutation signatures(1,3,30,6,7,and 13)in GB-NEC.Genes carrying somatic SNVs were enriched mainly in oncogenic signaling pathways involving the Notch,WNT,Hippo,and RTK-RASpathways.In summary,we have systematically identified the mutation landscape of GB-NEC,and these findings may providemechanistic insights into the specifc pathogenesis of this deadly disease. | Fatao Liu Yongsheng Li Dongjian Ying Shimei Qiu Yong He Maoian Li Yun Liu Yijian Zhang Qin Zhu Yunping Hu Liguo Liu Guoqiang Li Weihua Pan Wei Jin Jiasheng Mu Yang Cao Yingbin Liu | 2021 | Signal Transduction and Targeted Therapy2021，6，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员