**3. Results and Discussion**

#### *3.1. Photoelectric Sensor Monitoring Performance and Real-Time Online Monitoring Test*

To test the performance of the photoelectric sensor, the numbers of monitored corn grains at speeds of 6, 8, 10, and 12 km/h were tested in the laboratory and the field. When the speed reached the set value, the test seeder monomer was started by the virtual button on the on-board computer, and the seeder monomer was stopped at a random time. The number of corn seeds collected in containers fixed below the metering tube was manually counted. The statistical results indicate that the photoelectric sensor monitoring performance was quite good, and there were no differences between the laboratory and field monitoring data. Table 3 shows the statistical results of the monitoring data and actual data. The average monitoring accuracy was 99.8%.

To test the reliability of the system fault alarm, two kilograms of corn seeds were added to each sowing monomer. In the initial stage of operation, the metering tube was in normal planting, and the system did not send alarm information. When the seed box was empty, the system was checked to determine whether the alarm was prompted and whether the corresponding sowing monomer was shown in the vehicle terminal. According to the same method, during the normal seeding period, the seeding tube was artificially blocked at a given time, and the system blocking alarm rate was checked. The test results of fifty trials showed that the fault alarm rate was 100%.

The statistical analysis of the performance index data is shown in Figure 14. It can be seen from the chart that each evaluation index was basically similar at different speeds and substantially exceeded the standard (NY/T 1143-2006).

Overall, the qualified rate was higher when the grain spacing was larger. It was also found that when the speed was 12 km/h, the qualified rate decreased compared with the other speeds and the missed rate increased. The reason is that with the increase in the speed of the seedbed belt, the sliding degree of the seedbed pulley relative to the seedbed belt increased, resulting in inaccurate speed measurement. When the speed was 8 km/h and 10 km/h, the consistency of the indices is good, and the difference was significant when the speed was 6 km/h and 12 km/h.


**Table 3.** Statistical results of monitoring data and actual data.

Table 4 shows the correlation analysis between the factors and performance indicators. There was a strong correlation between two factors (*V* and *Xref*) and the seed distribution uniformity index (CV, QI, RI, and MI). In addition, the statistical values describing the correlation between various factors and performance indicators show that there was a strong correlation between QI, RI, MI, and *V*: QI decreased with an increase in *V* and RI, and MI increased with an increase in *V*; there were significant correlations between QI and CV and between RI and MI. The CV, RI, and MI decreased with increasing QI, and the correlation between MI and QI was the strongest. The coefficient of determination was 0.983, and the level of visibility was far less than 0.01.

**Figure 14.** Statistical analysis of the performance index data.


**Table 4.** Correlation analysis between factors and performance indicators.

Note: Because the prior uncertainty is a positive correlation or negative correlation, the double tail test was chosen; descriptive statistics of sample data were used to calculate the average and variance; and the visibility of the output results must be marked. When the visibility level reaches 0.05, the upper right corner uses '\*'; when the visibility level reaches 0.01, the upper right corner uses '\*\*'. <sup>a</sup> represents the coefficient of determination; <sup>b</sup> represents the *p* value, namely, the level of dominance.

#### *3.2. Differences in Seeding Performance among Different Planting Units in the Field*

The test results of the seeding performance at different operating speeds are shown in Table 5. When the operating speed was 8 km/h, the seeding performance was excellent. The qualified index of single seeding was 94.14%, the reseeding index was 1.72%, and the missing seeding index was 4.14%. With the increase in the operation speed, the reseeding index always maintained a certain level. However, due to the insufficient wind pressure of the fan and the irregular bounce of the seed when landing, the missing seeding index increased significantly, resulting in a decrease in the seeding accuracy. When the operating speed was 10 km/h, the seeding qualified index was reduced to 91.48%, and the leakage index was increased to 7.46%. When the operating speed was 12 km/h, the seeding qualified index was still greater than 90%.

**Table 5.** Results of the seeding performance at different operating speeds.


Note: No. 2 and No. 7 represent the second and seventh sowing planting units, respectively.

The seeding performance test results at different seed spacing settings are shown in Table 6, and the operating speed remained 8 km/h. With the increase in the seed spacing, the qualified index decreased and the reseeding index and leakage index increased. For a grain spacing of 15 cm, the average qualified index of two single seedlings was 93.34%, the average reseeding index was 2.09%, and the average missing seeding index was 4.57%.

When the operating speed was within the range of 6~12 km/h or the grain spacing was set to 15~25 cm, there was no significant difference in the seeding performance between the No. 2 and No. 7 planting units. These differences may be caused by factors such as the processing technology of the planting unit mechanical mechanism, mechanical vibration, and measurement error. Therefore, it is considered that the variability of the seeding performance between the monomers is small. In summary, when the quality of seeds and soil preparation meets the agronomic requirements of sowing, the electric drive seeding

control system designed in this study meets the requirements of precision sowing under high-speed working conditions.


**Table 6.** Seeding performance for different driving modes.

Note: No. 2 and No. 7 represent the second and seventh sowing planting units, respectively.

#### *3.3. Discussion of the Results*

Based on the control system of the electric drive precision seeder, laboratory bench tests and field tests were carried out. Its performance indicators tended to be consistent, which also fully illustrated the system reliability. The bench test explored the effects of different operating speeds and grain spacing on the seeding performance indices. At present, many scholars have carried out electric drive seeding experiments, and their working performance has been greatly improved compared with the traditional mechanical seeders. The performance indicators involved in this study are similar to those used in previous studies. Due to the differences in the environment and mechanical structure, the qualified rate of sowing in the field was lower than that of the bench test.

Since the test bench is designed for a traditional mechanical seeder, the influence of the seedbed vibration and slip ratio of the seedbed belt during high-speed operation has not been fully considered, thus affecting the test results to a certain extent [33]. In the field experiment, previous researchers mostly used 4-row or 6-row seeding machines for experiments. In this study, an 18-row air suction precision seeding machine was used. Due to the increase in seeding monomers, the airflow of the fan was unstable at high speeds, resulting in insufficient pressure during high-speed operation and a slight decrease in the seeding qualified index; seeding monomers on both sides of the seeding machine was a common malfunction. Nevertheless, more than 90% of the qualified rates fully met the actual work requirements. In the selection of photoelectric sensors, based on previous studies, a rectangular infrared radiation surface was selected, which greatly improved the sensing area of the photoelectric sensors and reduced the blind area. The high sensitivity of the sensor increased the fault alarm rate.

The sensors used in the system and the electronic components used in the design circuit are commonly used in the market. Compared with the laser detection sensor used in [7], the photoelectric sensor has a high value for practical application, and the monitoring performance was better than the laser detection performance; compared with the expensive LiDAR used in [10], the system used the common satellite acquisition module and achieved good data acquisition and control effect through certain filtering algorithms. The CAN bus control method greatly reduces the difficulty of field wiring. The brush DC motor is easier to control and lower cost than the brushless DC motor used in [17,18]. Usually, brushless motors perform better than brush motors.

In order to prevent electrostatic interference to the system, the electrostatic shielding circuit was specially designed in the circuit, which improved the anti-interference properties and robustness of the system. In practical field applications, a shielded twisted pair is used in CAN bus transmission, and terminal resistance is connected to the transceiver end. At the same time, CAN bus through the data link layer and physical layer has achieved high bus data security and bus stability; the correctness of data transmission is ensured by establishing a CANopen object dictionary. The above measures enhance the robustness of the system to subsystem faults and electromagnetic interference. Overall, whether in economic cost or system performance, the system was suitable for agricultural machinery operation.

On the other hand, the acquisition accuracy of the tractor speed directly affects the sowing quality. Although the accuracy of GPS can meet the current operating requirements, once the GPS signal, as the only acquisition speed, is affected, it seriously affects the operating quality. In the future, multisensor information fusion technology will be used to compensate for the speed signal to ensure that the speed measurement accuracy can still be maintained under sensor fault and interference conditions to ensure the consistency of the operation quality in a complex working environment.
