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| 1 | Image segmentation of overlapping leaves based on Chan–Vese model and Sobel operator显示文摘To improve the segmentation precision of overlapping crop leaves,this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator.The approach consists of three stages.First,a feature that identifies hues with relatively high levels of green is used to extract the region of leaves and remove the background.Second,the Chan–Vese model and improved Sobel operator are implemented to extract the leaf contours and detect the edges,respectively.Third,a target leaf with a complex background and overlapping is extracted by combining the results obtained by the Chan–Vese model and Sobel operator.To verify the effectiveness of the proposed algorithm,a segmentation experiment was performed on 30 images of cucumber leaf.The mean error rate of the proposed method is 0.0428,which is a decrease of 6.54%compared with the mean error rate of the level set method.Experimental results show that the proposed method can accurately extract the target leaf from cucumber leaf images with complex backgrounds and overlapping regions. | Zhibin Wang Kaiyi Wang Feng Yang Shouhui Pan Yanyun Han | 2018 | Information Processing in Agriculture2018，5，1： | 7 |
| 2 | Au@Ag-labeled SERS lateral flow assay for highly sensitive detection of allergens in milk显示文摘Casein andα-lactalbumin(α-LA)are the main allergens in cow's milk,which can affect the skin,respiratory system,and gastrointestinal tract,even cause anaphylactic shock.Developing a rapid and sensitive detection method of casein andα-LA is still pursued.Herein,a surface-enhanced Raman scattering based lateral flow assay(SERS-LFA)method for rapid and highly sensitive detection of milk allergens in food was established for effectively preventing allergic symptoms.Gold@silver nanoparticles(Au@Ag NPs)were synthesized as SERS active substrate to prepare the antibody-modified SERS Probe and SERS-LFA strips toward casein andα-LA were assembled according to the sandwich mode.The detection results were calculated according to colorimetric and Raman signal.The introduction of SRES signal significantly increased the sensitivity of detection with the limit of detection(LOD)of 0.19 ng/mL and 1.74 pg/mL,and exhibited an excellent linear relationship within the range of 0.55-791.50 ng/mL and 0.1 pg/mL-100.0 ng/mL for casein andα-LA,respectively.Furthermore,SERS-LFA strips was highly specific with the recovery rates for corresponding80.36%-105.12%and 85.73%-118.22%for casein andα-LA,respectively.Therefore,the SERS-LFA could be in great potential to develop a unique allergen detection method. | Jing Li Jia Xu Yi Pan Yongzhi Zhu Yuanfeng Wang Shouhui Chen Xinlin Wei | 2023 | Food Science and Human Wellness2023，12，3： | 1 |
| 3 | A Review on Technologies for Oil Shale Surface Retort显示文摘 | Yi Pan Xiaoming Zhang Shouhui Liu | 2012 | Journal of The Chemical SocietyofPakistan2012，34，6： | 1 |
| 4 | Dynamic ensemble selection of convolutional neural networks and its application in flower classification显示文摘In recent years,convolutional neural networks(CNNs)have achieved great success in image classification.However,CNN models usually have complex network structures that tend to cause some related problems,such as redundancy of network parameters,low training efficiency,overfitting,and weak generalization ability.To solve these problems and improve the accuracy of flower classification,the advantages of CNNs were combined with those of ensemble learning and a method was developed for the dynamic ensemble selection of CNNs.First,MobileNet models pre-trained on a public dataset were transferred to flower datasets to train thirteen different MobileNet classifiers,and a resampling strategy was used to enhance the diversity of individual models.Second,the thirteen classifiers were sorted by a classifier sorting algorithm,before ensemble selection,to avoid an exhaustive search.Finally,with the credibility of recognition results,a classifier subset was dynamically selected and integrated to identify the flower species from their images.To verify the effectiveness,the proposed method was used to classify the images of five flower species.The accuracy of the proposed method was 95.50%,an improvement of 1.62%,3.94%,22.04%,13.77%,and 0.44%,over those of MobileNet,Inception-v1,ResNet-50,Inception-ResNet-v2,and the linear ensemble method,respectively.In addition,the performance of the proposed method was compared with five other methods for flower classification.The experimental results demonstrated the accuracy and robustness of the proposed method. | Zhibin Wang Kaiyi Wang Xiaofeng Wang Shouhui Pan Xiaojun Qiao | 2022 | International Journal of Agricultural and Biological Engineering2022，15，1： | 0 |
| 5 | Image enhancement for crop trait information acquisition system显示文摘Collecting images using portable devices is an effective and convenient method for acquiring crop trait information.Because of uncertain environmental conditions in the field,enhancement is necessary to improve the visual quality of images.With this aim,here we propose an adaptive image enhancement method based on guided filtering.Our method automatically calculates the enhancement weights of the detail in an image according to the distribution characteristics of the illumination intensity of a crop image,so as to adaptively adjust the contrast of the image.To verify the effectiveness of the proposed algorithm,we performed enhancement experiments on 50 images of four kinds of cucumber leaf tissues,namely,leaves infected with target spot,powdery mildew,and downy mildew,and healthy leaves.The results showed that our proposed method substantially improved the visual quality of the images.Moreover,the mean ratios of the contrast to color difference obtained using the proposed method were higher than the mean ratios obtained using five conventional enhancement methods.We consider the proposed method for image enhancement will be a valuable addition to the crop trait information acquisition system(http://ebreed.com.cn/). | Zhibin Wang Kaiyi Wang Feng Yang Shouhui Pan Yanyun Han Xiangyu Zhao | 2018 | Information Processing in Agriculture2018，5，4： | 0 |
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