| Super hybrid rice is widely cultivated in china.Because super hybrid rice has strong tiller ability,it requires precise and low seeding rate,which need to ensure 2~3 grains each cell in tray plugs.But during the sowing process,some problems arise.Rice seed traits,such as length,shape,moisture,weight change,which greatly affect the performance of seeder sowing machine.As a result,seed distribution on the tray plugs is uneven and sowing quantity changes now and then.To solve the problem,a seeding performance measurement system based on computer vision and embedded computer vision is put forward.Some critical technologies are planned to study by the following methods.Firstly seeding tray images sequence are acquired on line,then image mosaic algorithm is applied to form a complete nursery tray sowing image,which retain the detail information of seeding tray image.Secondly estimation of the nursery tray sowing quantity per cell is achieved and sowing performance parameters are counted.In the aspect of rice seed counting algorithm,particles quantity detection is base on seed connected regions pattern recognition,multi-feature extraction and feature selection technologies.The method improves the counting accuracy of multiple particle seed quantity,and works well when seeds are overlapped,crossed and adhered.Research on segmentation and counting for touching hybrid rice grains based on the improved watershed agrolithm was also studied.At last the development of machine vision system ensures high-speed and efficient operation of sowing performance measurement system.The theory and method of this research will open up a new technology of precision constant seeding;also the research has great theoretical value and practical application significance.The main contents and findings are as follows:(1)The image acquisition system was designed and image preprocession was studied.According to the actual situation of the application of super hybrid rice seeding,and considering the height and shooting window of camera and physical property of super hybrid rice,a nuersy plug tray image acquisition system which was placed on the sowing test line was designed.Three types of super hybrid rice with different physical property were choosed as the experimental samples of this project.As super rice was small and the color difference between rice and soil,the Otsu threshold segmentation method based on RGB color model was applied.The Otsu algorithm was applied to the G component of seeding images for getting the threshold adaptively.Through the analysis and comparison of the existing edge extraction algorithm,canny operator was used to extract the edge of the rice connected regions.To this end,the image preprocessiong process was done.(2)Research on quantity recognition algorithm of connected regions of super hybrid rice based on BP neural netweork.The experiment result showed that after feature selection of super hybrid rice,three varieties with respective establishment of pattern classifier obtained the highes average accuracy,91.6%,93.5% and 93.1% respectively.After rice seeds are sowed onto the nursery trays,seed connected regions extracted from the acquired image may occur as the following situations: impurity,single grain and grains that are overlapped and adhered.To great extent,the shape features of each connected region can determine the grain quantity.Six shape geometric features and seven invariant moments of each seed connected regions were extracted,which were used for inputs of BP neural network and grain quantity estimation.In order to reduce the redundant information between multi-dimension features and improve the efficiency of pattern recognition,the feature selection algorithm was studied.Based on the mean impact value MIV and BP neural network algorithm,different group of feature parameters were evaluated and sorted,so as to select the best group of feature parameters.Then,a BP neural network classifier was employed to classify seed connected region into impurities,one grain,two grains,three grains,four grains or five grains and above.The result showed that the BP neural network classifier was effectiveness.(3)Research on segmentation and counting for touching hybrid rice grains based on the improved watershed agrolithm.The results showed that the average counting accuracy of three types super hybrid rice were 93.84%,92.2% and 91.9,respectively.This study proposes an improved watershed segmentation algorithm,which is based on preprocessing and post segmentation.The study can achieve the automatic segmentation and counting of touching rice.In the preprocessing stage,an improved watershed segmentation algorithm is put forward.First,wavelet transformation is used for rice gray image enhancement which better reflect the gray difference level of rice surface and edge,and then,Gauss filter is used for image smoothing.At last,the number of over-segmentation can be greatly reduced with the application of the watershed segmentation algorithm for segmenting and counting touching rice.However,there are some over-segmentation regions that inevitably exist.In order to accurately merge the over-segmentation regions,firstly SUSAN detector is applied to detect the corners around the rice seed boundary,including convex points and concave points,and then,the watershed segmentation lines are skeletonized and the endpoints of the lines are detected.Finally,whether the endpoints of the lines are coincided with the corners are detected.If the endpoints of the lines coincide with one of the corners,the segmentation lines are judged as correct lines,in contrast,if the endpoints of lines don’t coincided with one of the corners,the segmentation lines can be judged as over-segmentation lines.The two nearest connected regions of the endpoints of over-segmentation lines are over-segmented regions,and the two regions will be merged.25 images of each kinds of hybrid rice Pei Za Tai Feng,Teyou No.338 and Tai Feng You 208 were used for the tests.The result showed that the average accuracy of segmentation and counting of Pei Za Tai Feng,Teyou No.338 and Tai Feng You 208 rice grains were 93.84%、92.2% and 91.9% respectively.(4)A method was presented to estimate the sowing quantity per cell in tray plug and precision seeding performance.The test result showed that the test accuracy of qualified seeding rate,leakage rate,reply rate and the average grain number are 99.36%、91.77%、90.49% and 96.45% respectively.After sowing quantity of connected regions of super hybrid rice was studied,map projection method of binary image was performed to locate target detection area and cell plug.In order to fully and accurately obtain the imformation of the seeding performance nursey tray,a fast nursery plug tray image mosaic technique based on phase correlation and Speeded up Robust Features(SURF)detection is introduced.The test results show that the proposed method greatly outperforms the traditional SURF method in point matching accuracy,time consumption,and image mosaic accuracy.The average feature point matching accuracy of this new method is improved by approximately 7.14%.The implementation time is almost three times faster.The Root Mean Squared Error(RMSE)of image blending in the R channel RMSEr,the G channel RMSEg,and the B channel RMSEb,are decreased by approximately 8.4%,6.9%,6.9% respectively.The Root Mean Squared Error of image registration RMSEreg is decreased by 33.8%.(5)A rice nursery tray images wireless transmission system based on embedded machine vision was designed.The embedded machine vision system was composed of embedded development platform Tiny4410,Wifi gateway,network camera,infrared sensor module and remote computer.The embedded Linux operating system,camera driver,GPIO port driver and network file system configuration were installed in embedded development platform.Applications for the device were programmed with Qt development tool.The applications included image acquisition,real-time images displayed on screen and friendly interactive interface.Jpeglib static library was used to compress the images.Through the Wifi network,embedded system and remote server achieve socket communication in accordance with the provision of the protocol data transmission.The remote server achieved collecting,validating,displaying and saving the images based on the Netty framework.The test results showed that the transmission of BMP and the compressed JPEG images could meet the operational requirements of automated rice sowing test line.The transmission rate of JPEG images was greatly improved.The embedded data acquisition terminal could collect stable seeding tray images,and successfully uploaded to the server.The network average packet loss was 0.23% and the error rate was 0.23%. |
PDF Full Text Request