& Context Knowledge of the occurrence of sound and loose knots on the surface of sawn sugi (Cryptomeria japonica L.f.) is important for its grading and application. & Objectives This study examined an optical system for detecting sound and loose knots in sugi instead of human being using the combining information of the color and texture features. & Methods The proposed system could be conceptually divided into two components: a CCD camera scanning system and a defect detecting algorithm developed by the authors. In the algorithm, the contrast parameter calculated from a graylevel co-occurrence matrix was used to locate the potential defects represented by sound knots and loose knots. The rulebased approach, which was built according to the color feature histograms, was used to identify sound knots and loose knots. A series of samples containing single or multiple sound and/or loose knots were selected at random to verify the efficiency and accuracy of the proposed system. & Results There were 94 sound knots and 86 loose knots on the surfaces of these samples, and the accuracy of locating the positions of sound knots and loose knots was 94.7% and 97.6%, respectively. The accuracies of identifying knots as sound or loose were 96.6% and 98.8%, respectively. The overall detection accuracy of the system was 93.9%. & ConclusionsThe results indicate that the proposed vision system is an efficient means of detecting sound knots and loose knots.

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Quad-rotor unmanned aerial vehicles (UAV) are prone to external interference during aerial photography of farmland environments. For example, they are affected by external airflow and load, resulting in route deviation and irregular image overlap, which seriously affects image quality. An aerial trajectory tracking controller is designed for this aerial photography process. To ensure that a drone can fly according to the established route during the aerial photography process and meet the requirements of large-scale topographic map stereo mapping for the flight control accuracy of the drone platform, the system was divided into a full-drive subsystem and an underactuated subsystem. The full-drive subsystem uses a fast terminal sliding mode controller to ensure that the variable ([Formula: see text]) reaches the desired value. The under-actuated subsystem adopts the second-order sliding mode control was used to achieve effective position and attitude tracking of variables ([Formula: see text]). The flight controllers are derived by using Lyapunov theory. Finally, with the aerial trajectory of a farmland taken as an example, the flight path control of the UAV is simulated. Simulation results show that the designed control system can be applied to the aerial photography process of the UAV and has strong anti-system parameter perturbation, robustness and good trajectory tracking.

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