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. Keywords: bilateral filter, least squares support vector machine (LS-SVM), image segmentation, Hangzhou white chrysanthemum, illumination intensity DOI: 10.25165/j.ijabe.20191204.4584 Citation: Yang Q H, Luo S L, Chang C, Xun Y, Bao G J. Segmentation algorithm for Hangzhou white chrysanthemums based on least squares support vector machine. Int J Agric & Biol Eng, 2019; 12(4): 127–134.

Apple fruits on trees tend to swing because of wind or other natural causes, therefore reducing the accuracy of apple picking by robots. To increase the accuracy and to speed up the apple tracking and identifying process, tracking and recognition method combined with an affine transformation was proposed. The method can be divided into three steps. First, the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue (2R-G-B) color feature; after improving the binary image, the apples were recognized with a local parameter adaptive Hough circle transformation method, thus improving the accuracy of recognition and avoiding the long, time-consuming process and excessive fitted circles in traditional Hough circle transformation. The process and results were verified experimentally. Second, the Shi-Tomasi corners detected and extracted from the first frame image were tracked, and the corners with large positive and negative optical flow errors were removed. The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm (RANSAC) to correct the scale of the template image and predict the apple positions. Third, the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm. The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8% and 20% in the absence of template correction and apple position prediction, respectively. In comparison, the running time of our algorithm was 25 ms, and the tracking error was less than 4%. Therefore, test results indicate that speed and efficiency can be greatly improved by using our method, and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits. Keywords: apple picking robot, tracking and recognition algorithm, oscillating apple, Hough transform, pyramid LK optical flow algorithm, affine transform, template matching DOI: 10.25165/j.ijabe.20201305.5520 Citation: Yang Q H, Chen C, Dai J Y, Xun Y, Bao G J. Tracking and recognition algorithm for a robot harvesting oscillating apples. Int J Agric & Biol Eng, 2020; 13(5): 163–170.

Reduction is a crucial stage in the surgical treatment of bone fractures.The detailed fracture information of the patient can be obtained from computed tomography (CT) scans before surgery and enable physicians to plan preoperative reduction, to reduce the operation time and thus increase the probability of getting satisfactory results. The primary purpose of this paper is to design a computer-aided automatic registration method of fracture point cloud data, so as to simplify the fracture reduction process. In this paper, we propose an integrated fracture reduction system was introduced. The system enables direct semi-automatic processing from CT images to fracture reduction. First, a 3D fracture models is reconstructed from CT images by using the modified Marching Cube (MC) algorithm and is discretized to generate a point cloud. Second, the K-dimensional (KD) tree algorithm is used to cluster and segment the point clouds to identify different fracture fragments. Last, through the combination algorithm of Normal Distributions Transform(NDT) and modified Iterative Closest Point(ICP), the coarse alignment and fine registration of point clouds are achieved step by step. This method has been successfully applied to the reduction of tibial fracture. In the tests performed, the processing time of each step, the point cloud and the 3D model after registration are displayed. The semi-automatic integrated system based on preoperative CT scanning is used to realize fracture reduction，which provides a feasible foundation for minimally invasive and accurate fracture reduction surgery.

The coupling mechanism between mechanical stimulation and biological response during fracture healing has not been fully elucidated. This study establishes an innovative mathematical model to analyze this complex process. Based on the phenomenon of early weight-bearing in clinical application of Taylor external fixation frames, we constructed a bidirectional coupling mechanic-biological computing framework. The model contains two interactive modules: the mechanical module uses porous media theory to simulate the fluid-solid coupling behavior of tissues, and the biological module uses reaction-diffusion equations to characterize processes such as cell proliferation, migration, differentiation and matrix metabolism. The two modules realize real-time data interaction through customized developed interface programs. The core of the model is to establish a quantitative relationship between mechanical stimulation (through physical quantities such as fluid shear stress) and the final morphology of mesenchymal stem cells (differentiated into chondrocytes or osteoblasts). As the simulation progresses, dynamic changes in the extracellular matrix will feedback and adjust the mechanical parameters of the tissue to form a closed-loop system. The axisymmetric finite element method was used to numerical verification of long bone fracture cases, and the results showed that the model could accurately reproduce the spatiotemporal characteristics of callus evolution. Comparison with clinical observational data confirms the reliability of the model. Parameter analysis shows that this model can quantify the impact of multiple factors such as stem cell microenvironment, initial callus characteristics and fixation device stiffness on healing mode, providing a theoretical basis for personalized treatment.

Design and experiment of the negative pressure adsorption Cartesian robot system for apple harvesting