## 1 Introduction  
With the ongoing progress in agricultural machinery technology in China, the dissemination of intelligent and automated agricultural machinery has reached a broad scale. Among these machines, grain combine harvesters have been proven essential in crop harvest season [1; 2; 3]. It has been reported that the mainstay of China's agricultural sector consists of small-scale farmers, which comprise over 98% of the total population [4; 5]. In addition, these farmers manage approximately 70% of the total cultivated land in China. In this type of farming system, an increased harvest loss can significantly impact the farmer's crop yield. Therefore, it is crucial to accurately measure the harvest loss during harvester work and provide timely feedback to enhance the quality of the harvester's performance and diminish losses incurred by farmers. In the research process of loss detection sensors, according to different principles, the loss detection sensor can be divided into mechanical and electrical, acoustic, microwave, photoelectric, machine vision, piezoelectric, and so on. The loss detection sensor of the electromechanical principle detects the loss indirectly through flow detection. For example, [PERSON] (1972) et al. [6] installed the flowmeter at the outlet of the conveying device of the harvester to calculate the number of grains. The principle of acoustoelectricity is to count the loss according to the sound signal. For example, the particle impact sensor designed by [PERSON] (1977) et al. [7] and [PERSON] (2016) et al. [8] distinguishes different materials by using the sound signal generated by the impact of materials on the sensor collected by microphones. Particle collision sensors based on the microwave principle, such as [PERSON] (1989) et al. (1989), can be used to detect whether the rice ear is clean by using different response signals triggered by substances of different qualities in the microwave field. The photoelectric method is to count the number of grains by taking advantage of the current interruption caused by cutting off the optical path of grains. For example, the grain loss sensor designed by [PERSON] (1982) (1982) first separated the grains from other materials in combination with a fan and other devices and then counted the grain loss by photoelectric pulse. With the development of new materials and new technologies, the above detection methods have gradually faded out of the particle impact sensor research field and market due to their limitations. At present, the main application and research direction is to use machine vision and piezoelectric principle for loss detection sensor research. With the development of machine vision, some researchers try to use machine vision technology to develop sensors to detect grain loss. For example, [PERSON] (2009) (2010) from Jiangsu University designed the entrained loss sensor to calculate the number of grains after binarization, median filtering, and other processing of the pictures collected by the camera at the straw entrance. The application of the machine vision method can not only realize the detection of grain loss but also realize the detection of other operating quality parameters of combine harvesters, such as the degree of sprinkling, grain breakage rate, etc., which is also a research hotspot at present. For example, the grain quality detector designed by Deere Company (2017) in 2017, At the same time complete grain loss detection and grain yield, moisture, and other online detection. Because the problem of dust obscuring cameras can not be solved well at present, the application of machine vision has some difficulties. Therefore, the particle impact sensor based on the piezoelectric principle has been developed rapidly. The piezoelectric loss detection sensor generates piezoelectric signals with different frequencies and amplitudes when grains and other materials impact piezoelectric materials on the surface of the sensor, so as to identify materials.