Grain loading condition monitoring based on dual cameras in intelligent cluster grain truck

Xiangju Qu; Zhiqiang Hu; Yulong Qi; Miaomiao Wang; Chaowen Lin; Xiang Liu

doi:10.1117/12.2651750

23 January 2023 Grain loading condition monitoring based on dual cameras in intelligent cluster grain truck

Xiangju Qu, Zhiqiang Hu, Yulong Qi, Miaomiao Wang, Chaowen Lin, Xiang Liu

Proceedings Volume 12557, AOPC 2022: Optical Sensing, Imaging, and Display Technology; 125571M (2023) https://doi.org/10.1117/12.2651750
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China

Abstract

Intelligent harvesting is one of the important criteria to measure the development level of agricultural modernization. Coordinated operation of harvester-grain truck clusters can improve grain harvesting efficiency and reduce post-production losses during large-scale rice/wheat concentrated harvests. Overloading the grain on the grain truck will cause serious scattering of grain, further, insufficient loading can result in wasted capacity. How to monitor the grain loading process dynamically is a pressing matter. In this paper, two cameras and a point laser were used to measure the status of grains in the truck in real-time. The loadable capacity of the grain truck can be obtained through the reconstruction of the grain truck carriage edge and the positioning of the bottom of the truck carriage. During the harvester grain unloading process, the cone tip of the wheat pile is irradiated by the laser, and the height of the wheat pile can be obtained by measuring the location of the laser point. Then insufficient loading or overloading can be avoided by controlling the speed of the unloading port. This method has been verified in the paper box. The results show that the dual-camera monitoring system can measure the volume of the grain truck in real-time, and feedback on the total amount of grain loaded in the grain truck in time, which can effectively avoid grain loss caused by excessive loading.

Citation Download Citation

Xiangju Qu, Zhiqiang Hu, Yulong Qi, Miaomiao Wang, Chaowen Lin, and Xiang Liu "Grain loading condition monitoring based on dual cameras in intelligent cluster grain truck", Proc. SPIE 12557, AOPC 2022: Optical Sensing, Imaging, and Display Technology, 125571M (23 January 2023); https://doi.org/10.1117/12.2651750

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KEYWORDS

Cameras

Agriculture

Imaging systems

Calibration

Machine vision

CMOS cameras

Computer vision technology

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