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Highlights A linear array CCD sensor is utilized to obtain the contour signal of the vertical projection of the sugarcane. A method is provided for continuously identifying and locating sugarcane nodes. Examines the impact of scan speed and illumination on the accuracy of identification. The method performs well regarding identification rate, precision, and efficiency. Abstract. In order to achieve continuous and dynamic detection of sugarcane nodes, improve the automatic production efficiency of pre-cut sugarcane seed, and lower the cost of mechanized sugarcane production, a detection method based on linear array charge-coupled device (CCD) photoelectric sensor signal processing was developed. Firstly, the mechanical drive unit was controlled to drive the photoelectric detection system to acquire the signal of the vertical projection of the sugarcane profile. The projection information was then binarized into profile information using the Otsu algorithm. The profile signal was then decomposed using a variable mode decomposition algorithm optimized based on the sparrow search algorithm, and the component reflecting the node content was regarded as the feature signal. Finally, the position of the wave peaks above the judgment threshold in the normalized feature signal was considered the position of the sugarcane nodes. One-way and two-way experiments were conducted to investigate the effects of scan speed and illuminance on identification precision. The results showed that the identification rate, average response time, and average error values were 98.40%, 0.13 s, and 1.36 mm at a scan speed of 75 mm/s and an illuminance of 91.91 lx. Compared to other node identification methods discussed in this article, the proposed method has a high identification rate and accuracy with a high response speed, which can improve the automation efficiency of sugarcane seed production. Keywords: Identification accuracy, Non-contact detection, Photoelectric sensor, Precision agriculture, Seed production, Signal processing, Sugarcane node, Variational mode decomposition.

In this paper, we propose a new iterative learning control algorithm for sensor faults in nonlinear systems. The algorithm does not depend on the initial value of the system and is combined with the open-loop D-type iterative learning law. We design a period that shortens as the number of iterations increases. During this period, the controller corrects the state deviation, so that the system tracking error converges to the boundary unrelated to the initial state error, which is determined only by the system’s uncertainty and interference. Furthermore, based on the λ norm theory, the appropriate control gain is selected to suppress the tracking error caused by the sensor fault, and the uniform convergence of the control algorithm and the boundedness of the error are proved. The simulation results of the speed control of the injection molding machine system verify the effectiveness of the algorithm.