# Model Study on the Combination of Operating Parameters of Corn Kernel Harvesters
## 1 Introduction  
Corn is the most widely distributed food crop in the world, which is grown in large quantities from latitude 53\({}^{\circ}\) N to 40\({}^{\circ}\) S [1; 2; 3]. Corn can be used not only as a kind of grain but also for feed processing, oil production, bio-energy, and other industrial fields. It is an agricultural crop with significant economic value and broad cultivation prospects [4; 5; 6].  
The corn planting area is increasing year by year, and the demand for corn kernel harvesters is also growing, while the harvesters are required to have better operating quality and operating efficiency [7; 8; 9; 10]. The leading indicators for evaluating the operating quality of crop harvesters are the impurity rate, the crushing rate, and the crop kernel loss rate; one of the indicators for assessing the operational efficiency of crop harvesters is the blockage failure rate [11; 12; 13; 14]. These evaluation indicators also could be applied to corn kernel harvesters.  
Improving the operating quality and efficiency of harvesters could reduce the loss of corn kernels and profit loss caused by harvesters. Therefore, some studies have been carried out to improve the operating quality and efficiency of harvesters [15; 16; 17; 18]. [PERSON] et al. used computer vision technology to recognize the shape of corn kernels and constructed a BP neural network with grain shape feature parameters to detect the grain shape of corn kernels and determine the rate of broken kernels [19]. [PERSON] et al. designed a machine vision system with dual front and rear light illumination to detect the impurity rate and corn kernel crushing rate through image acquisition and image analysis algorithms [20]. [PERSON] et al. obtained the raw spectra of wheat samples and established a spectral inversion model of wheat impurity by component analysis, which could quickly detect the impurityof wheat [21]. Currently, methods to reduce the failure rate are mainly based on monitoring and alarming. The harvester operating environment is harsh and the sensor detection data are prone to distortion, which may not prevent the appearance of harvester failures, such as cylinder clogging [22]. The methods used to improve the operational quality and operational efficiency of corn kernel harvesters are manual post-hoc regulation of the harvester parameters and relying on operator experience, which is less efficient and cannot be universally applied to most harvesting operating environments, and also does not allow the harvester to work with a better combination of operating parameters [23,24].  
This study analyzed the influence of several combined factors on the operating quality and efficiency, which were closely related to the engine speed, power, and load of the corn kernel harvester. The relationship curves of the engine speed and feeding capacity of the corn kernel harvester, the threshing cylinder speed and the feeding capacity, and the engine speed and the threshing cylinder speed were obtained through field tests. Through data processing and analysis using Data Processing System (DPS) V9.0.1, a regression model of the operating quality index, the efficiency index and the operating parameter combination of a corn kernel harvester was obtained. This model could obtain the optimized combination of operating parameters, so that the harvester could work under the optimized combination of operating parameters to achieve better operating quality and efficiency.  
This study provides a new solution to improve the operating quality and operating efficiency of harvesters. The operation parameter combination model could be used for the design and development of intelligent control systems for harvesters. The purpose of the study is to examine the relationships of the feed rate, concave clearance, and cylinder speed with the operating quality and operating efficiency of the harvester. In the meanwhile, the focused target of this study was to investigate the coupling effect of multiple factors in the same group of experiments.