## 1 Introduction  
As an important grain cash crop in southern China, research on threshing loss reduction, referring to effectively reducing grain loss and improving agricultural production efficiency by adopting appropriate harvesting equipment and control methods, is of great significance to improving farmers' profitability and guaranteeing national food security [1; 2]. Threshing losses are usually analyzed from two aspects. On the one hand, during the operation of the harvester, a considerable portion of the rice grain will fall into the field or be discharged with the stem, resulting in direct losses; on the other hand, the harvested rice can be partially broken, leading to losses such as mold and spoilage during storage and transportation. The proper feeding volume is the key to improving the threshing quality, reducing losses and achieving the optimal performance of the harvester. Under current traveling speed adaptive system regulations, the traveling speed of the harvester and its feeding volume should have a good coupling relationship [3]. When the traveling speed changes, the feeding volume changes accordingly. This direct positive correlation reflects the close coupling between the traveling speed and the feeding volume. Therefore, the feed amount can be controlled by adjusting the traveling speed. Many developed countries have researched smart traveling speed controls based on traditional manual operations, making extensive use of various types of electronic instruments and monitoring, alarms and other devices [4; 5]. [PERSON] et al. investigated a machine vision system for automatic plant division adapted to different light conditions, which can be used to monitor crop density in real time during harvester operations [6]. [PERSON] et al. presented a control system for the modeling, control design and experimental automatic calibration of a combine harvester leveling system [7]. [PERSON] et al. used deep convolutional neural network techniques to predict crop yields and thus guide harvesting [8]. Contemporary research results are more reliable and widely referenced, and mainstream products are suitable for large-scale farming operations. However, the corresponding purchase cost is relatively high and does not align with China's agricultural situation of diversified farming types and family farming [9]. Therefore, it is difficult to implement in China. Numerous Chinese scholars have researched combine harvester traveling speed controls. [PERSON] et al. established an online monitoring system for combine harvesters' hydraulic actuators using the AMESim simulation platform, and experimentally verified its high accuracy, providing harvester drivers with a reference for controlling traveling speed [10]. [PERSON] et al. electrically modified a harvester and tested functions such as sequential start/stop, reel speed tracking and blockage detection, with good results [11]. [PERSON] et al. designed a new adaptive control system for combine harvesters that considered cutting and tool advancement speeds [12]. Their experiments showed that the system controlled the tool's cutting and advancement speeds more effectively. [PERSON] et al. proposed a method for the real-time monitoring of grain breakage rates of rice combine harvesters based on machine vision, which can be used for the optimization and adjustment of traveling speeds of an intelligent harvester [13]. Some deep-learning related algorithms have inspired the development of intelligent agricultural machinery [14; 15; 16; 17].