# Design and Test of Real-Time Detection System for Cleaning Loss of Rapeseed Harvester
## 1 Introduction  
Rapeseed is the largest oil crop in China and plays an important role in ensuring the security of the edible oil supply, but the problem of high losses during rapeseed mechanized harvesting is prominent currently [1, 2, 3, 4, 5, 6]. The high loss rate is an important reason why it is difficult to promote domestic rapeseed harvesting machines at present, and it is also a major problem that hinders the mechanization process of rapeseed harvesting in China. The harvesting loss is not only an important index to measure the performance of harvesting equipment but also an important basis for adjusting the relevant working parameters of harvesters. At present, one of the main reasons leading to the high loss of rapeseed mechanized harvesting in production is the lack of real-time and relatively accurate detection methods for measuring harvesting losses, which leads to the lack of a basis for the adjustment of working parameters during production. Therefore, a cleaningloss detection system for rapeseed harvesters has been designed to improve the technical level of rapeseed harvesting equipment in China and reduce the loss of mechanized harvesting, which can ensure national oil safety and provide key technical support for rapeseed mechanized harvesting and loss reduction [7; 8; 9; 10; 11; 12]. Harvesting loss detection methods have always been a focus of research by experts and scholars both domestically and internationally. The current harvesting loss detection methods are mainly divided into three categories: the sensitive element method, the sampling separation detection method and the image processing loss detection method. The sampling separation detection method is mainly used in the research of grain loss detection for grain harvesting machinery. [PERSON] et al. designed a harvesting loss detection system for staple crops such as wheat, rice and corn using the sampling separation method. However, this method requires manual assistance in practical applications, resulting in low efficiency, time consumption and poor real-time performance [13]. [PERSON] et al. used image processing methods to carry out extensive research in the field of rapeseed loss detection based on machine vision and designed an improved Hough transform algorithm to detect rapeseed kernel loss in images [14]. However, during the rapeseed harvesting operation, the amount of excretion is large and the speed is high, and grain occlusion in the captured images cannot be avoided. Moreover, the harvesting environment is dusty, and it is difficult to keep the camera lens clean, resulting in a great impact on the image acquisition accuracy, making it difficult to detect the cleaning loss of rapeseed [15]. Compared with the image processing method, the signal acquisition and analysis method, which uses a processing circuit to distinguish between kernels and impurities, has the advantages of a fast-processing speed, strong environmental adaptability, high efficiency and a low cost. At present, it is widely used in the detection of harvesting loss in rice, corn and other crops [16; 17; 18; 19; 20; 21], and there are also a small number of studies on rapeseed crops. [PERSON] et al. studied the frequency differences between two categories of impact signals generated by rapeseed kernels and impurities using simulation software and used a band-pass filter circuit to distinguish kernels from impurities [22]. However, because the frequency domains of rapeseed kernels and impurities overlap, the threshold range is difficult to determine, and the cut-off frequency calculation and circuit design for the filter circuit are complicated. When the signal cut-off frequency to be processed changes, the circuit often needs to be redesigned, and its parameters need to be adjusted.