To realize the robotic harvesting of Hangzhou White Chrysanthemums,the quick recognition and 3D vision localization system for target Chrysanthemums was investigated in this study.The system was comprised of three mai...To realize the robotic harvesting of Hangzhou White Chrysanthemums,the quick recognition and 3D vision localization system for target Chrysanthemums was investigated in this study.The system was comprised of three main stages.Firstly,an end-effector and a simple freedom manipulator with three degrees were designed to meet the quality requirements of harvesting Hangzhou White Chrysanthemums.Secondly,a segmentation based on HSV color space was performed.A fast Fuzzy C-means(FCM)algorithm based on S component was proposed to extract the target image from irrelevant background.Thirdly,binocular stereo vision was used to acquire the target spatial information.According to the shape of Hangzhou White Chrysanthemums,the centroids of stamens were selected as feature points to match in the right and left images.The experimental results showed that the proposed method was able to recognize Hangzhou White Chrysanthemums with the accuracy of 85%.When the distance between target and baseline was 150-450 mm,the errors between the calculated and measured distance were less than 14 mm,which could meet the requirements of the localization accuracy of the harvesting robot.展开更多
In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment,a color image segmentation method for Hangzhou white chrysanthemum based on least squares support ve...In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment,a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine(LS-SVM)was proposed.Firstly,bilateral filter was used to filter the RGB channels image respectively to eliminate noise.Then the pixel-level color feature and texture feature of the image,which was used as input of LS-SVM model(classifier)and SVM model(classifier),were extracted via RGB value of image and gray level co-occurrence matrix.Finally,the color image was segmented with the trained LS-SVM model(classifier)and SVM model(classifier)separately.The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting,back-lighting and overshadow,with the accuracy of above 90%.When segmenting an image,the SVM algorithm required 1.3 s,while the LS-SVM algorithm proposed in this paper just needed 0.7 s,which was better than the SVM algorithm obviously.The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81%picking success rate.展开更多
[Objective] This research aimed at exploring an effective way for inoculation and identification of chrysanthemum white rust under controlled conditions. [Method] By combining the observation methods with the naked ey...[Objective] This research aimed at exploring an effective way for inoculation and identification of chrysanthemum white rust under controlled conditions. [Method] By combining the observation methods with the naked eye and under optical microscope, we had established the identification standards for chrysanthemum white rust with six classifications and optimized artificial inoculation methods in vitro. [Result] The results showed that bottled cuttings identification method and petri dished leaves identification method both can be used for identification in vitro of chrysanthemum white rust, bottled cuttings identification method had shown better effects than petri dished leaves identification method, and was supposed to be best artificial inoculation and identification method in vitro. [Conclusion] This research had provided a scientific method for safe and effective researches on chrysanthemum white rust, in order to control the occurrence and diffusion of this quarantine disease.展开更多
The white rust,caused by Puccinia horiana was one of the most important epidemic diseases on Chrysanthemum,and also was a quarantine action pest in the world.Trials determined the teliospores germinating biology of P....The white rust,caused by Puccinia horiana was one of the most important epidemic diseases on Chrysanthemum,and also was a quarantine action pest in the world.Trials determined the teliospores germinating biology of P.horiana : the teliospores were germinated between 4 ℃ and 32 ℃,while the optimum temperature range was 15 ℃ to 24 ℃,especially between 18 ℃ and 21 ℃.Water was necessary for the teliospores germination.Without free water,it couldn’t germinate in 24 h,even with 100 % R.H.The propriety of pH for germination was pH 4 to 6.5, while pH 6 was the most favorite.2 % glucose solution promoted the germination of the teliospores evidently,whereas the fresh juice of chrysanthemum foliages restrained it prominently.Light didn’t influence on the germination.展开更多
基金This work was financially supported by the project of National Science and Technology Supporting Plan(2015BAF01B02)the Open Foundation of Intelligent Robots and Systems at the University of Beijing Institute of Technology,High-tech Innovation Center(2016IRS03).
文摘To realize the robotic harvesting of Hangzhou White Chrysanthemums,the quick recognition and 3D vision localization system for target Chrysanthemums was investigated in this study.The system was comprised of three main stages.Firstly,an end-effector and a simple freedom manipulator with three degrees were designed to meet the quality requirements of harvesting Hangzhou White Chrysanthemums.Secondly,a segmentation based on HSV color space was performed.A fast Fuzzy C-means(FCM)algorithm based on S component was proposed to extract the target image from irrelevant background.Thirdly,binocular stereo vision was used to acquire the target spatial information.According to the shape of Hangzhou White Chrysanthemums,the centroids of stamens were selected as feature points to match in the right and left images.The experimental results showed that the proposed method was able to recognize Hangzhou White Chrysanthemums with the accuracy of 85%.When the distance between target and baseline was 150-450 mm,the errors between the calculated and measured distance were less than 14 mm,which could meet the requirements of the localization accuracy of the harvesting robot.
基金This work was financially supported by the project of National Science and Technology Supporting Plan(2015BAF01B02)the Open Foundation of Intelligent Robots and Systems at the University of Beijing Institute of Technology,High-tech Innovation Center(2016IRS03).
文摘In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment,a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine(LS-SVM)was proposed.Firstly,bilateral filter was used to filter the RGB channels image respectively to eliminate noise.Then the pixel-level color feature and texture feature of the image,which was used as input of LS-SVM model(classifier)and SVM model(classifier),were extracted via RGB value of image and gray level co-occurrence matrix.Finally,the color image was segmented with the trained LS-SVM model(classifier)and SVM model(classifier)separately.The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting,back-lighting and overshadow,with the accuracy of above 90%.When segmenting an image,the SVM algorithm required 1.3 s,while the LS-SVM algorithm proposed in this paper just needed 0.7 s,which was better than the SVM algorithm obviously.The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81%picking success rate.
基金Supported by the"Eleventh Five-Year"National Technology Support Program"Breeding of New Varieties of High Yield and Quality of Major Commercial Flowers"(2006BAD01A18)the Postdoctoral Research Fund of Shenyang Agricultural University~~
文摘[Objective] This research aimed at exploring an effective way for inoculation and identification of chrysanthemum white rust under controlled conditions. [Method] By combining the observation methods with the naked eye and under optical microscope, we had established the identification standards for chrysanthemum white rust with six classifications and optimized artificial inoculation methods in vitro. [Result] The results showed that bottled cuttings identification method and petri dished leaves identification method both can be used for identification in vitro of chrysanthemum white rust, bottled cuttings identification method had shown better effects than petri dished leaves identification method, and was supposed to be best artificial inoculation and identification method in vitro. [Conclusion] This research had provided a scientific method for safe and effective researches on chrysanthemum white rust, in order to control the occurrence and diffusion of this quarantine disease.
文摘The white rust,caused by Puccinia horiana was one of the most important epidemic diseases on Chrysanthemum,and also was a quarantine action pest in the world.Trials determined the teliospores germinating biology of P.horiana : the teliospores were germinated between 4 ℃ and 32 ℃,while the optimum temperature range was 15 ℃ to 24 ℃,especially between 18 ℃ and 21 ℃.Water was necessary for the teliospores germination.Without free water,it couldn’t germinate in 24 h,even with 100 % R.H.The propriety of pH for germination was pH 4 to 6.5, while pH 6 was the most favorite.2 % glucose solution promoted the germination of the teliospores evidently,whereas the fresh juice of chrysanthemum foliages restrained it prominently.Light didn’t influence on the germination.
