Study On Key Technology Of Potted-Seedling Transplanting Information Acquisition Using Machine Vision

Viewed from the automation of facility agriculture production, to solve the problem of require artificial quality separation in potted-seedlings transplanting process, this dissertation takes potted-seedling as the research object, potted-seedling transplanting fitness and angle modulation of potted-seedlings leaves as the research goals, studied the methods of potted-seedlings transplanting fitness information acquisition and potted-seedlings leaves adjustment direction using machine vision, designed hardware and software systems of potted-seedling automatic sorting transplanter, and optimized design of it’s key component. The main research contents and conclusions were as follows:(1) Method of potted-seedling image segmentation was studied based on color space. The stability of image segmentation was study respectively to the four color space for potted-seedling test samples with different light conditions by increasing the monochrome background panels and lifting of potted-seedling obtained video images. Threshold segmentation algorithm was determined that combining the S component extraction of HSI color space and OTSU for video image segmentation of potted seedlings. It could reduce the difficulty of image processing, and improve the efficiency of potted-seedling image segmentation, recognition and robustness, and meet real-time performance and stability requirements that the stems and leaves of potted-seedling image extraction algorithm.(2) Method of potted-seedling height information acquisition was studied. Three recognition algorithm of potted-seedling height information was analysed using the potted-seedling segmentation images. Method of potted-seedling height acquisition was determined that minimum external rectangle algorithm based on vertex chain code spindle after evaluate synthetically relative deviation and algorithm efficiency of three kinds of potted-seedling height recognition algorithm.(3) Method of potted-seedling perpendicularity information acquisition was studied. Three recognition algorithm of potted-seedling perpendicularity information was analysed using the main stem of potted-seedling was extracted after thinning,1×5horizontal dilation and13×1vertical erosion image. Method of potted-seedling perpendicularity acquisition was determined that straight line fitting method of corner based on SUSAN corner detection algorithm after evaluate synthetically relative deviation and algorithm efficiency of three kinds of potted-seedling perpendicularity recognition algorithm.(4) Method of potted-seedling leaves adjustment direction was studied. Taking48frame video image of rotating potted-seedling as the research object, potted-seedling leaves adjustment direction method was proposed using kernel function interest points similarity measure trace. Interest points of potted-seedling leaves was obtained using image center pixel8neighborhood identifying after segmentation and thinning. Interest points location of the current frame video image was effectively determined by smoothing similarity function and target candidate minimized function. Missing interest points were replaced by minimized costs closer to consistency function produces the phantom points for following the tracks of potted-seedling interest points. According to x coordinate and variation of interest points in the image plane, and the direction of potted-seedling leaves could be availably adjusted.(5) Method of key frame video images extraction was presented using variation optical flow, in order to reduce the number of video images when tracing interest points. Optical flow computation of potted-seedling video images was converted into the energy functional extremum using Laplacian conserved hypothetical data and isotropic optical flow driven smoothly. Key frame sequence can be effectively obtained from48-frame video images by comparing mahalanobis distance values of the optical flow vector between two adjacent images.(6) Hardware system and software system of potted-seedling automatic sorting transplanter were designed, and key components were optimized. The set consists of mounting bracket,8flat belt conveyor, potted-seedlings lift&rotate unit, transplanting information acquisition unit and system control unit, video image processing unit and other components. Optimization design method of key components was proposed for potted-seedling lifting rotation mechanism based on fuzzy optimal algorithm, physical quantities were limited to the range with the blurred boundaries, and compared with the commonly optimization algorithms more practical. Software system was designed, and it can be run in continuous mode. It can realize acquisition and judgment of different kinds of potted-seedlings transplanting information.