Prof. Yaoming Li Profile

Prof. Yaoming Li

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Proceedings Article | 1 August 2022 Paper

Design of photoelectric opposite-beam grain flow monitoring device based on BP neural network

Jin Chen, Haiyang Xu, Zhuohuai Guan, Yaoming Li

Proceedings Volume 12257, 122573P (2022) https://doi.org/10.1117/12.2640372

KEYWORDS: Sensors, Neural networks, Agriculture, Microcontrollers, Sensing systems

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During the working process of the rice-wheat combine harvester, the irregular accumulation of grains on the scraper of the scraper-type grain elevator will affect the final measurement accuracy. In this paper, a scraper type combine harvester grain flow monitoring device is designed. In this paper, the input capture function of STM32 single-chip microcomputer is used to obtain the high-level signal output by the array photoelectric opposite-beam sensor, and the grain accumulation information on the elevator scraper is obtained; the discrete element simulation software is used to analyze the grain accumulation model on the scraper, and the calculation formula of grain flow is established; the principle of BP neural network is used to improve the monitoring accuracy of grain flow. The indoor bench test and field dynamic performance test were carried out. The results show that the opposite-beam grain flow monitoring device designed in this paper operates stably, and the maximum relative error of the field harvesting test flow monitoring is 4.12%, which meets the actual needs of combine harvester grain flow monitoring, which provides a technical basis for field grain flow monitoring.

Proceedings Article | 22 October 2021 Paper

Research on visual navigation path extraction method of combine harvester based on machine vision

Jin Chen, Zhiping Wu, Yaoming Li, Zhenghua Xu, Kejiu Wang

Proceedings Volume 11928, 1192802 (2021) https://doi.org/10.1117/12.2611632

KEYWORDS: Image segmentation, Image processing, Visualization, Hough transforms, Navigation systems, Detection and tracking algorithms, Image processing algorithms and systems, Machine vision, Image filtering, Feature extraction

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Aiming at the problem that the complex environment of the paddy field affects the harvesting navigation path extraction during the visual navigation of the combine harvester, and also to improve the efficiency of the navigation path extraction, this paper proposes a navigation path extraction method based on the identification of unharvested areas. First, the Cb color channel of the image is extracted, and the image is initially segmented by adaptive threshold segmentation; then, the detection of the connected domain area and the unharvested area of the paddy field operation model are used to achieve the accurate segmentation of the unharvested area, and the operation boundary line pixel coordinate constraints are added to improve the probabilistic Hough transform algorithm by combining the characteristics of the combine harvester operation, and the boundary line slope is used as the fitting condition to speed up the extraction speed of the boundary line. Experiments show that the average recognition accuracy of the method reaches 95%, the average time of navigation path extraction is 30.5ms, the average image processing time is 0.89s/frame, and the extraction results are used for visual navigation, the relative error of visual navigation is less than or equal to 12.8cm at the operating speed of 0.8m/s, and the relative accuracy of navigation reaches 95.07%, the algorithm under the complex environment of paddy field has good applicability and high real-time performance, and provides effective navigation information for autonomous navigation of combine harvester.

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