**1. Introduction**

Rice is one of the four main staple food crops in China, with a perennial planting area of 30 million hectares [1]. Mechanized rice production relies heavily on the harvest process as an essential step. Threshing is a key link in the rice harvesting process; it is a complex, nonlinear, and uncertain process, with several influencing parameters and large nonlinearity [2,3]. The impact of threshing on rice determines how much grain is lost during the harvest and processing stages. Double cropping rice in southern China has a short harvesting duration. The performance parameters of the threshing and separation device directly affect the operation quality of the combined rice harvester, i.e., the core working component. The longitudinal axial threshing device is characterized by long threshing time, smooth threshing process, good adaptability, and relatively soft threshing

**Citation:** Ma, L.; Xie, F.; Liu, D.; Wang, X.; Zhang, Z. An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device. *Agriculture* **2023**, *13*, 788. https:// doi.org/10.3390/agriculture13040788

Academic Editors: Massimo Cecchini, Cheng Shen, Zhong Tang and Maohua Xiao

Received: 19 March 2023 Revised: 23 March 2023 Accepted: 27 March 2023 Published: 29 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

effect, and it is broadly used in combined harvesters [4]. Researchers in agricultural mechanization are interested in the flexible threshing tooth due to its lower impact force and rate of damage to the cracked grains compared to its rigid counterpart [5]. For this reason, it is suitable for increasing the synthesis benefit in grain production [6]. Several scholars have studied the application of flexible materials in agricultural engineering. In 1972, Duane L et al. [7] designed a self-made collision test device to analyze the effects of corn grain velocity, collision surface material, collision angle, and other parameters on the extent of grain collision damage. One study found that when the impact surface was polyurethane, the damage degree of the grain was one-fifth of that when the impact surface was steel, and one-sixth of that when the impact surface was concrete. This is an inaugural study focusing on the effect of flexible materials on grain, demonstrating the benefit of flexible materials in reducing grain damage degree. Shi Qingxiang et al. [8] performed a comparative study on the flexible and rigid threshing elements, demonstrating that flexible threshing with flexible teeth made of flexible materials can extend the threshing time and reduce grain breakage with feasible flexible threshing. Xie Fangping et al. [9] utilized polyurethane plastic cylindrical strips as the teeth of flexible threshing rods to conduct a dynamic analysis of the threshing of flexible rod teeth. Consequently, they found that the indexes of flexible threshing, for instance, non-removal rate and impurity rate, were similar to those of rigid rod teeth threshing, and the crushing rate was significantly lower than that of rigid rod teeth threshing. Ren Xuguang et al. [10] analyzed the threshing process of rice using the conservation law of capacity and noted that it is conducive to rice threshing when the flexible teeth periodically hit the ear of rice, and a resonance response occurred. Su Yuan et al. [11] modified the conventional Q235 carbon steel teeth into nitrile rubber composite nail teeth and polyurethane rubber nail teeth. The test found better grain removal performance of nitrile rubber composite nail teeth than that of polyurethane rubber nail teeth and traditional carbon steel nail teeth. Geng Duanyang et al. [12] designed a crossaxial flow flexible corn threshing device. To realize flexible and low-damage threshing of corn ears, the threshing element combined a structure of flexible nail teeth and an elastic short grain rod. Li Yibo et al. [13] performed a bench test to explore the effect of composite nail teeth of different outer materials on the threshing performance and self-wear resistance of corn ear. The results showed that the rubber composite nail teeth had the best comprehensive effects in threshing and self-anti-fraying performance, the breakage rate of maize was lower compared with that of traditional carbon steel nail teeth, and the nonthreshing rate of maize was similar to that of traditional carbon steel nail teeth, thus meeting the conditions of technical specifications for threshing quality evaluation of maize harvester. Fu Jun et al. [14] established a rigid–flexible coupled wheat threshing arch tooth. Under similar operating conditions, the damage rate of the rigid-flexible coupled arch tooth was significantly reduced, unlike that of the standard arch tooth, with significant loss reduction and threshing effect. Qian Zhenjie et al. [15] introduced the increase and decrease constraint strategy to establish a multi-friction dynamic model of flexible threshing teeth on grains. As a consequence, it was observed that the continuous normal striking force and repeated minor tangential kneading force of flexible teeth on grains combined to reduce the grain damage rate. Reports on the longitudinal axial flow threshing cylinder with a hollow core and flexible rod teeth used in rice threshing are limited. Flexible threshing can reduce the crushing rate of rice grains and, thus, developing a comprehensive and accurate evaluation model of flexible threshing has important theoretical value and practical significance.

In recent years, the artificial neural network (ANN) has achieved desired performance and high accuracy in predicting laboratory data because of its capacity to describe nonlinear systems. As a result, it is widely applied in the fields of mathematics, engineering, medicine, economy, environment, and agriculture [16], particularly where some traditional modeling methods have failed [17]. Artificial neural network technology has been utilized in harvester systems by some researchers [18,19]. Nonetheless, few studies have been conducted on the threshing performance of a flexible threshing device using artificial neural networks. Due to the uncertainty of the threshing condition and the complexity of the

factors affecting the threshing device, the threshing performance prediction is a nonlinear problem affected by multiple factors. Nevertheless, the BP neural network is a nonlinear dynamic system [20,21] with powerful nonlinear [22] and generalization capacity and can identify complex relationships among the data [23]. Herein, parameters [24] affecting the performance of the threshing device and threshing performance indicators [25,26] were based on the parameters reported by several studies.

In the laboratory-based flexible threshing bench test, the rotated speed of the threshing cylinder, threshing clearance of the concave sieve, and separating clearance of the concave, as well as feeding quantity, were selected as the inputs of the model based on the BP neural network. The neural network model was established between inputs and their threshing characteristic of the crushing rate, impurity rate of threshed material, and entrainment loss rate. Further, the threshing performance index was predicted under different parameters. The objectives of this study included: (1) Determining the feasibility of artificial neural network technology in predicting the threshing performance of the flexible threshing device and providing executable procedures for an artificial neural network model for practical application; (2) Investigating the effect of artificial neural network geometry and some internal parameters on model performance; (3) Exploring the relative significance of factors influencing threshing performance through sensitivity analysis.

#### **2. Materials and Methods**

### *2.1. Test Materials and Equipment*

The plots with basically similar crop growth rates were selected as the experimental sampling area. The rice variety tested was Xiangzaoxian 24. Table 1 shows the main material characteristics of the rice. The rice flexible threshing test was conducted in the Agricultural Machinery Engineering Training Center of Hunan Agricultural University from July 11 to 18, 2022. Figure 1 shows the test equipment, and Table 2 shows the equipment parameters.

**Table 1.** Main physical characteristic parameters of harvesting rice.


**Figure 1.** Flexible threshing experiment device. 1. Feeding device 2. Threshing device 3. Threshing cylinder 4. Flexible threshing teeth.


**Table 2.** Parameter table of flexible threshing device.

#### *2.2. Test Method*

The test was conducted following GB/T 5262—2008 and GB/T 5982—2005.

The thousand-grain quality was determined using the national standard method to explore grain and stem water content and according to the GB 5519-85 "grain, oil test thousand grain weight determination method".

The plots with similar crop growth were selected as the sample areas. Rice plants were artificially fed uniformly into the longitudinal axial flow threshing drum. In the multi-factor experiment, the material of each group weighed 10 kg. Three parallel tests were performed using similar parameter combinations, and the average value was taken. The performance evaluation indexes of the system were categorized into the crushing rate, impurity rate of threshed material (impurity rate for short), and entrainment loss rate. The mix that was threshed was collected in the receiving box located under the adaptable threshing mechanism. The mix released from the end of the cylinder was accumulated with the help of a tarpaulin attached to it. After each parallel test, the crushing rate and impurity rate of the threshing system were calculated using the mix, which was discharged into the receiving box. The mixture discharged onto the tarpaulin attached to the end of the cylinder was analyzed to determine the entrainment loss rate. The calculation formulas of the crushing, impurity, and entrainment loss rates are, respectively:

$$Y\_P = \frac{W\_P}{W\_X} \times 100\% \tag{1}$$

$$\mathcal{Y}\_{\rm Z} = \frac{\mathcal{W}\_{\rm XZ}}{\mathcal{W}\_{\rm Xh}} \times 100\% \tag{2}$$

$$Y\_S = \frac{W\_W}{W} \times 100\% \tag{3}$$

where *YP* is the crushing rate, %; *WP* is the mass of crushed grains in the sample, g; *WX* represents the total grain weight in the sample, g; *YZ* is the impurity rate of threshed material, %; *WXZ* is the impurity mass in the extruded sample, g; *WXh* is the total mass of extruded samples, g; *YS* is the entrainment loss rate, %; *WW* is the grain mass discharged from the tail of the drum, g; *W* is the grain weight of each group of test extracts, g.
