Seeding Status Monitoring System for Toothed-Disk Cotton Seeders Based on Modular Optoelectronic Sensors

Abstract

In precision cotton seeding, the toothed-disk precision seeder often experiences issues with missed seeding and multiple seeding. To promptly detect and address these abnormal seeding conditions, this study develops a modular photoelectric sensing monitoring system. Initially, the monitoring time window is divided using the capacitance sensing signal between two seed drop ports. Concurrently, a photoelectric monitoring circuit is designed to convert the time when seeds block the sensor into a level signal. Subsequently, threshold segmentation is performed on the time when seeds block the photoelectric path under different seeding states. The proposed spatiotemporal joint counting algorithm identifies, in real time, the threshold type of the photoelectric sensor’s output signal within the current monitoring time window, enabling the differentiation of seeding states and the recording of data. Additionally, an STM32 micro-controller serves as the core of the signal acquisition circuit, sending collected data to the PC terminal via serial port communication. The graphical display interface, designed with LVGL (Light and Versatile Graphics Library), updates the seeding monitoring information in real time. Compared to photoelectric monitoring algorithms that detect seed pickup at the seed metering disc, the monitoring node in this study is positioned posteriorly within the seed guide chamber. Consequently, the differentiation between single seeding and multiple seeding is achieved with greater accuracy by the spatiotemporal joint counting algorithm, thereby enhancing the monitoring precision of the system. Field test results indicate that the system’s average accuracy for single-seeding monitoring is 97.30%, for missed-seeding monitoring is 96.48%, and for multiple-seeding monitoring is 96.47%. The average probability of system misjudgment is 3.25%. These outcomes suggest that the proposed modular photoelectric sensing monitoring system can meet the monitoring requirements of precision cotton seeding at various seeding speeds.

1. Introduction

In Xinjiang, the toothed-disk cotton precision seeder is currently the most popular precision seeding equipment for cotton. It boasts a simple structure, wear-resistant components, and convenient maintenance, effectively reducing user costs and gradually replacing the old mechanical hole-type cotton seeders [1,2]. With the popularization of toothed-disk seeders, the issues of missed seeding and multiple seeding have become increasingly significant. Therefore, research on monitoring toothed-disk seeders has become urgent. In China, some scholars have conducted research on seed picking state monitoring of toothed-disk seeders using through-beam photoelectric sensors, laser color sensors, and interdigital capacitance sensors [3,4,5,6,7], achieving significant results. Meanwhile, both domestic and international researchers are monitoring the frequency of seed flow using piezoelectric sensors and through-beam or diffuse reflection photoelectric sensors for small- and medium-sized seeds [8,9,10,11,12,13,14,15,16,17,18,19]. These seed flow monitoring technologies have strong reference significance for the research of cotton precision seeding monitoring systems. Moreover, with the rise of machine vision technology, this technology has also been applied to precision seeding monitoring to identify empty holes and seed distribution during seeding [20,21]. To meet the requirements of high-precision seeding monitoring and improve system anti-interference performance, researchers have conducted a series of precision seeding monitoring system studies using capacitance sensors, biomimetic strain sensors, seed signal simulation sensors, and multi-sensor fusion monitoring methods [22,23,24,25,26,27,28], achieving satisfactory results. These precision seeding monitoring methods have strong reference value for the design of toothed-disk cotton precision seeding monitoring systems.

Currently, the monitoring of toothed-disk cotton seeders primarily depends on identifying whether seeds are present in the toothed-disk seed pickup spoon to determine seeding states. This monitoring method necessitates the installation of sensors inside the seed pickup chamber, which are fixed by drilling holes into the seed storage ring without direct contact with the seeds. However, during operation, as the seeder rotates, seeds collide and rub against the moving disk cover, the seed pickup toothed disk, and each other. These interactions can cause the seed coating to deteriorate and generate a significant amount of dust within the seed pickup chamber. The dust adheres to the sensors, thereby diminishing the system’s monitoring accuracy [29]. Consequently, researchers have undertaken several studies on factors that affect sensor accuracy and their compensation techniques, with the goal of enhancing the precision of monitoring systems through improved counting algorithms and reduced signal loss [30,31].

This study, by summarizing the successful experiences of various monitoring systems, proposes the placement of the monitoring node within the seed guide chamber of the toothed-disk cotton seeder, based on the structural characteristics of the seeder and the movement trajectory of seeds within it. This location has a significantly lower dust concentration compared to traditional monitoring positions, which helps prevent the modular photoelectric sensor from experiencing reduced monitoring accuracy due to dust blocking the photoelectric monitoring port. Consequently, a cotton precision seeding monitoring system that combines modular photoelectric sensing with a spatiotemporal joint counting algorithm is proposed. Initially, a modular photoelectric sensor is designed, and a spatiotemporal correlation model based on multi-source signal fusion is established. Subsequently, through time-series correlation analysis of photoelectric signals and capacitance sensors, the seeding state of the toothed-disk seeder is identified. With the graphical human–machine interface designed using LVGL, the seeding information is updated in real time. Finally, through bench and field tests, the performance of the monitoring system at various seeding speeds is evaluated. This system meets the monitoring requirements for cotton precision seeding and provides technical support for enhancing the quality and efficiency of cotton seeding in Xinjiang.

2. Materials and Methods

2.1. Structure and Working Principle of Toothed-Disk Seeder

The research focuses on a toothed-disk cotton precision seeder. The structure, as depicted in Figure 1, primarily consists of a seed inlet pipe, a fixed disk end cover, a moving disk pressure ring, a seed pickup toothed disk, a seed storage ring, a seed drop port and guide plate assembly, along with a moving disk end cover. The components of the disk-type precision seeder can be divided into two parts: The precision seeder’s fixing mechanism comprises the seed inlet tube, fixed disk end cover, and seed storage ring. Additionally, the seeder’s rotary seeding mechanism consists of the movable disk pressure ring, seed pickup toothed disk, seed drop port and guide plate assembly, as well as the moving disk end cover.

The working principle of the toothed-disk seeder is illustrated in Figure 2a. The process proceeds sequentially through seed picking, seed transportation, seed unloading, and seed planting. During seeding operations, seeds enter the seeder through the seed inlet pipe and accumulate at the bottom of the seed storage ring.

The seed pickup toothed disk is connected to the moving disk end cover via screws and rotates synchronously. Under the drive of the moving disk, the seed pickup spoon enters the seed storage zone I for seed picking. Subsequently, the seed pickup toothed disk continues to rotate, carrying the seeds to unloading zone III. During this process, excess seeds fall back to seed storage zone I due to gravity and the vibration generated during seeder operation. When the seeds reach the seed transfer port V in unloading zone III, they lose support from the seed storage ring partition. Under the action of the seed cleaning device, they enter the seed guide chamber VI, thus completing one seed picking task. The seeds in seed guide chamber VI slide along the guide plate during seeder rotation and enter the seed drop port. The seed drop port contacts the ground and opens to complete planting. The complete seed movement trajectory after entering the seed guide chamber is shown by the red line in Figure 2b.

2.2. Sensor Design and Monitoring System Construction

2.2.1. Sensor Circuit Design

The sensor circuit primarily comprises an infrared sensing unit, a signal processing unit, a sensitivity adjustment unit, and a signal anti-interference unit. The signal output end of the infrared receiver is connected to the IN+ of an LM393 voltage comparator. The signal output end of potentiometer VR1 is connected to the IN− of the LM393 voltage comparator. The voltage comparator compares the input voltages at IN+ and IN− to alter the high and low levels of the output end OUT. When IN+ is greater than IN−, the OUT end of the voltage comparator outputs a high level. Conversely, when IN+ is less than IN−, the OUT end of the voltage comparator outputs a low level. Additionally, the sensitivity of the photoelectric monitoring circuit can be adjusted by varying the resistance value of potentiometer VR1. A capacitor C is connected in parallel with the positive and negative poles of the voltage comparator to suppress signal fluctuations within the photoelectric monitoring circuit. The photoelectric sensor monitoring circuit is depicted in Figure 3.

The sensor’s monitoring channel is designed to permit the passage of only one seed at a time, thereby preventing missed detections that could result from multiple seeds passing in parallel. Sensor calibration is achieved by adjusting the potentiometer’s resistance. After ensuring the electrical circuit is intact, the sensor is powered on. If the sensor fails to detect an object and continuously outputs a high-level signal, the reference voltage is increased until it slightly exceeds the threshold for the sensor’s low-level output. Conversely, if the sensor detects an object and outputs a low-level signal, the reference voltage is decreased until the threshold for the high-level output is reached. As a seed enters the monitoring channel, it obstructs the light path between the infrared emitter and the ceramic photodiode, causing the voltage of the infrared receiver to increase. When the receiver’s voltage surpasses the predetermined threshold set by the voltage comparator, the photoelectric monitoring circuit emits a high signal. Conversely, in the absence of a seed passing through the monitoring channel, the infrared receiver continuously detects infrared signals from the emitter, maintaining a voltage level significantly lower than the reference voltage established by the voltage comparator. This results in the photoelectric monitoring circuit outputting a low signal, thus enabling the determination of whether seeds have traversed the sensor’s monitoring port. Furthermore, the ceramic photodiode is responsive to infrared light but remains unresponsive to other wavelengths. The sensor’s installation location is relatively shielded, minimizing the risk of external light interference and thereby effectively ensuring the precision of the sensor’s monitoring capabilities.

2.2.2. Modular Photoelectric Sensing Monitoring System Construction

The designed photoelectric sensor circuit module was installed on the seed outlet guide tube of the guide slot, forming an independent photoelectric sensing monitoring module, as depicted in Figure 4.

The independent monitoring module was subsequently installed in the appropriate seed guide chamber of the seeder, ensuring that the seed outlet of the sensor module aligned with the seed drop port of the seeder, and the seed inlet of the sensor module faced the seed transfer port on the seeder. The results of installing sensors on the seeder are depicted in Figure 5. The power line and signal output line of the sensor were connected to the monitoring system’s host computer via a through-hole conductive slip ring, facilitating normal energy transfer and information transmission even as the seeder rotates. Concurrently, a capacitance sensor was mounted above the seeder, with its sensing head aligned with the metal seed drop port. Subsequently, the seeder equipped with the installed sensor is tested under dim, ambient, and bright light conditions. Additionally, the sensor’s performance is evaluated in a laboratory soil tank, where field-like vibration and dust conditions are simulated. In all tests, the sensor exhibits stable monitoring performance. This stability is attributed to the use of a ceramic photoelectric receiving diode within the sensor, which is sensitive exclusively to infrared light. Moreover, unlike traditional photoelectric sensor monitoring systems, the monitoring node in this study is positioned posteriorly within the seed guide chamber of the seeder. This chamber remains sealed during operation, preventing external light and dust from entering, and serves as the sole passage for seeds entering the seed outlet. Consequently, regardless of seeder vibration, seeds are consistently monitored as they pass through the sensor’s detection area. Collectively, these factors ensure the monitoring accuracy and anti-interference capability of the system.

The signal output line of the photoelectric sensing module is connected to the GPIO (PA0) of the MCU, while the signal output line of the capacitance sensor is connected to another GPIO (PA1) of the MCU. The MCU model used in this study is STM32H7B0VBT6 (STMicroelectronics (China) Investment Co., Ltd., Shanghai, China). The two GPIOs that connect the MCU to the sensors are configured in external interrupt mode. Through the interrupt service program, the number of interruptions from both the photoelectric sensor module and capacitance sensor is counted, and the duration of each blockage of the photoelectric sensing module is captured by the timer of the MCU. The MCU is connected to the computer via a CH340G module. Using the QSerialport library, the MCU data are received in real time, and the byte stream is parsed according to the protocol. The status data during seeding operations are extracted, and a simple seeding monitoring graphical display interface is designed using the lightweight open-source library LVGL. The theoretical seeding amount, actual seeding amount, missed-seeding rate, multiple-seeding rate, operation speed, seeder rotation speed, operation area, and seeding amount per minute are displayed in a visualized form through data and charts. The display effect of the human–machine interaction interface is shown in Figure 6.

The operation indicator light signifies a normal system power-up, while the fault indicator light indicates sensor damage conditions. When a single sensor is damaged, the fault indicator light flashes continuously once. In the case of two sensors being damaged, the fault indicator light flashes continuously twice. Should multiple sensors be damaged, the fault indicator light flashes at a frequency corresponding to the number of sensors that are compromised. Historical data are recorded via SQLite and stored in CSV format files. The operation flow of the seeding monitoring system is depicted in Figure 7.

2.3. System Counting Algorithm Based on Spatiotemporal Correlation Model

2.3.1. Division of Monitoring Time Windows

The system employs the MCU’s interrupt service routine and timer to track the frequency and duration of sensor trigger interruptions. Upon detection of a seed drop by the capacitance sensor at the seed drop port, an interrupt routine is triggered to log the number of times the seed drop port is passed. Simultaneously, a timer initiates to set up a monitoring time window for capturing the output signal from the photoelectric sensing module. The monitoring time window for the modular photoelectric sensing is divided based on the timestamps of the seed drop port triggers, as depicted in

Table 1. Monitoring time window segmentation table.

Seed Drop Port Number	Monitoring Time Window	Time Interval Description
1	[t(n, 1), t(n, 2)]	Between 1st and 2nd pulse
2	[t(n, 2), t(n, 3)]	Between 2nd and 3rd pulse
…	…	…
13	[t(n, 13), t(n, 14)]	Between 13th and 14th pulse
14	[t(n, 14), t(n+1, 1)]	Between 14th and next cycle’s 1st pulse

.

2.3.2. Seeding State Differentiation and Threshold Segmentation

When the infrared path of the photoelectric sensing module is obstructed by passing seeds, it activates an interrupt program. The MCU logs the frequency of blockages and the duration of each interruption. Given that a toothed-disk cotton precision seeder typically picks up no more than three seeds in a single operation, the passage of more than one seed through the photoelectric monitoring sensor is deemed multiple seeding. Consequently, determining the critical duration for a single cotton seed and for two cotton seeds to pass through the photoelectric sensor suffices to identify the seeder’s seeding state. Moreover, if multiple seeds traverse the sensor and generate consecutive photoelectric signals within the same monitoring time frame, this is also classified as multiple seeding.

The duration for a single cotton seed to block the photoelectric sensor is nearly equivalent to that of two connected cotton seeds. However, when multiple seeds are introduced into the sensor’s seed inlet from the seed transfer port, they disperse due to gravity and the vibration of the seeder. Additionally, the size of the sensor’s monitoring channel does not permit two seeds to pass through completely side by side. Consequently, the quickest passage for two seeds through the sensor occurs when they pass side by side with some overlap. To mimic the fastest passage of two cotton seeds through the photoelectric sensor, glue was employed to connect two cotton seeds in a staggered side-by-side configuration. Subsequently, single seeds and the glued double seeds were introduced into the sensor’s monitoring channel from the guide slot seed inlet, simulating the seeds sliding along the inner wall of the guide slot and passing through the photoelectric monitoring port during seeding. Each type of seed was seeded 100 times, and the duration for each seed to block the photoelectric path was recorded. The time intervals for single and double cotton seeds blocking the photoelectric sensor are depicted in Figure 8.

To determine the maximum allowable threshold for the duration of a single seed blocking the photoelectric sensor, thereby distinguishing between single-seeding and multiple-seeding states, the number of single and double seeds in each time interval was statistically analyzed. The statistical results are shown in Figure 9. As can be seen from the figure, $T_{\max }=15 \mathrm{ms}$ serves as a clear threshold for distinguishing between single and double seeds. Based on this threshold, the blocking signals of single and double seeds can be effectively identified.

2.3.3. Spatiotemporal Joint Counting Algorithm

During seeding, the operating speed of the seeding machine is not entirely uniform. To ensure that the signals passing through the photoelectric sensor are fully captured within the monitoring time window and to prevent signal truncation or false triggering, dynamic time calibration is necessary to ensure accurate correspondence between sensor data and the physical position of the seeder.

The timestamp of the capacitance sensor pulse trigger defines the boundary of the monitoring time window. Each photoelectric sensor corresponds to a time window for a seed drop port, which is determined by the timestamps of adjacent capacitance sensor pulses.

Let the timestamp of the kth seed drop port trigger in the nth cycle be

$$t_{(n , k)} k\in [1 , 14]$$

(1)

Let the timestamp of the kth seed drop port trigger in the (n+1)th cycle be

$$t_{(n+1 , 1)} k\in [1 , 14]$$

(2)

The rotation period of the seeder is

$$T_n=t_{n+1,1}-t_{n,1}$$

(3)

Considering the structural characteristics of the seeder, when the seeder’s internal seeds were emptied and refilled, during the first rotation cycle, no photoelectric monitoring signals could be generated in the first five monitoring time windows triggered by the seed drop port. This phase represents the initial seed filling stage of the seeder. From the 6th monitoring window onward, seed filling is completed, and seeds begin to pass through the photoelectric sensor, triggering seeding signals. If the seeder remains in a continuous seed filling state, the monitoring time window initiated by the seed drop port is synchronized with the photoelectric monitoring signal.

When the kth seed drop port capacitance pulse is detected, if k = 1, a new cycle is initiated, and the 14th monitoring time window [t(n, 14), t(n + 1, 1)] of the previous cycle is closed, and the seeding state of the corresponding seed drop port of the 14th monitoring time window is determined. If k > 1, the (k − 1)th monitoring time window [t(n, k − 1), t(n, k)] of the current cycle is closed, and the seeding state of the corresponding seed drop port of the (k − 1)th monitoring time window is determined. Subsequently, the kth monitoring time window is opened, and the photoelectric signal cache of the kth monitoring time window is cleared. The counting algorithm flow of this monitoring system is shown in Figure 10.

It is important to note that when a monitoring time window is initiated, the monitoring system begins capturing photoelectric blocking signals within this window until the current monitoring window closes.

During this period, the system records the frequency and duration of photoelectric sensor blockages. To ensure that the monitoring time window can fully accommodate the photoelectric sensor signals, measurements of the monitoring time window width at various seeder speeds were taken, and the margin of the monitoring time window corresponding to the maximum blocking duration at different speeds was determined, as illustrated in Figure 11.

When seeds pass through the photoelectric monitoring port, the rising edge signal of the photoelectric sensor is detected, indicating the start of blocking, and the start timestamp $t_{start}$ is recorded. The falling edge signal of the photoelectric sensor is detected, marking the end of blocking, and the end timestamp $t_{end}$ is recorded. The blocking duration is calculated as follows:

$$t_d=t_{end}-t_{start}$$

(4)

Each photoelectric sensing module’s digital pulse is captured via the GPIO interrupt program, recording the rising edge timestamp. The photoelectric sensing signals captured within this time window are attributed to sensor k.

When a single photoelectric blocking signal is detected within the monitoring window period and the blocking duration does not exceed the threshold, a pulse signal is triggered and recorded as single seeding. If no photoelectric blocking signal is detected within the monitoring window period, it is recorded as missed seeding. When multiple photoelectric signals are detected within the monitoring window period or when a single blocking duration exceeds the threshold, it is recorded as multiple seeding. The calculation formula is:

$$\left\{\begin{array}{c}{N}_Z={\displaystyle \sum _{i=1}^{N_D}\left[\right(K_i=1)}\wedge (T_i\le T_{\max }\left)\right]\hfill \\ N_L={\displaystyle \sum _{i=1}^{N_D}[K_i=0}]\hfill \\ N_C={\displaystyle \sum _{i=1}^{N_D}\left\{\left(\right[K_i\ge 2)\vee (K_i=1)\wedge (T_i>T_{\max }\left)\right]\right\}}\hfill \end{array}\right.$$

(5)

where $N_Z$ denotes the single-seeding monitoring quantity, $N_L$ denotes the missed-seeding monitoring quantity, $N_C$ denotes the multiple-seeding monitoring quantity, $N_D$ denotes the total number of seed drop port triggers recorded by the capacitance sensor, $K_i$ denotes the number of photoelectric sensor blocking triggers within the ith monitoring window period, $T_i$ denotes the duration of a single blocking of the photoelectric sensor within the ith monitoring window period, and $T_{\max }$ denotes the maximum allowable duration threshold for a single seed blocking the photoelectric sensor.

2.4. System Performance Testing Equipment and Methods

2.4.1. Bench Test

To evaluate the performance of the modular photoelectric sensing seeding monitoring system, a 14-hole toothed-disk cotton precision seeder, typically used in the machine-picking cotton planting pattern in the Xinjiang cotton planting area, was utilized. The test material consisted of ‘Xinluzao No. 42’ cotton seeds that had been subjected to seed coating treatment. The thousand-grain weight was 102.37 g, the moisture content was 5.6%, the impurity content was less than 0.1%, and the geometric dimensions of the cotton seeds exhibited a normal distribution. The length dimension was concentrated between 8.5 to 9.5 mm, the width dimension between 4.5 to 5.5 mm, and the thickness dimension between 4.0 to 4.4 mm. The coefficient of variation for the geometric dimensions of the test cotton seeds is presented in

Table 2. Summary of geometric dimension variability for test cotton seeds.

Dimension Type	Range (mm)	Mean (mm)	Standard Deviation (mm)	Coefficient of Variation (%)
Length	8.5~9.5	9.0	0.5	5.56
Width	4.5~5.5	5.0	0.5	10.00
Thickness	4.0~4.4	4.2	0.2	4.76

.

A precision seeding test bench was used to conduct bench tests on the seeding monitoring system, as depicted in Figure 12. The test bench comprised a test bench frame, conveyor belt, toothed-disk seeder, seed box, seed delivery pipe, conveyor belt motor, seeder drive motor, monitoring system, motor speed control device, data processing module distribution box, 12 V DC power supply, CH340G signal conversion module, PC, through-hole conductive slip ring, and so on.

The tests were conducted under indoor ambient light conditions, where external dust and vibrations affecting the seeder could be disregarded. Throughout the test, the seeder’s speed was varied by adjusting the seeder drive motor on the seeding test bench. The placement of seeds on the conveyor belt of the test bench was observed, and the actual single-seeding number, actual missed-seeding number, and actual multiple-seeding number were recorded 300 times each at seeder speeds of 30 r/min, 40 r/min, and 50 r/min. This was repeated in 7 groups and compared with the monitoring values of single seeding, missed seeding, and multiple seeding in the monitoring system to calculate the monitoring accuracy of the seeding monitoring system under different seeding states.

2.4.2. Field Test

To obtain the monitoring data of the seeding monitoring system and verify whether the performance of the seeding monitoring system meets the design requirements, field tests of the monitoring system were conducted in the ultra-wide film cotton planting test field of the 29th Regiment of Tiemenguan City, Xinjiang Uygur Autonomous Region, on 10 April 2025. The equipment and materials for this test included a Tiancheng 2MBJ-1/12 cotton film laying precision seeder (Xinjiang Tiancheng Agricultural Machinery Manufacturing Co., Ltd., Tiemenguan City, Xinjiang, China), ‘Xinluzao No. 42’ cotton seeds that had undergone seed coating treatment, a Liugong HC2004 wheeled tractor (Guangxi Liugong Agricultural Machinery Co., Ltd., Liuzhou City, Guangxi, China), a modular photoelectric sensing seeding monitoring system, a seed digging spoon, a tape measure, etc.

The seeder, equipped with the monitoring system, was installed on the 2MBJ-1/12 ultra-wide film seeder. The signal acquisition module and DC power supply of the system were installed in an electrical box and fixed on the frame of the seeder. A shielded signal line was used to transmit the collected seeding information to the PC in the cab. The PC end used an inverter to convert the 12 V DC power supply from the tractor into a 220 V AC power supply to continuously power the PC. The seeder adopted a (66 + 10) cm wide narrow row planting pattern, with a total of 12 groups of toothed-disk cotton precision seeders installed. A Liugong HC2004 wheeled tractor was used for towing. Field tests were performed outdoors under ambient light, with slight windborne dust, and vibration of the seeder was induced by passing over soil clods or stones in the test plot. During the test, the operating speed was controlled by changing the gear position and throttle size of the tractor, completing field seeding tests with the seeder speed at 30 r/min, 40 r/min, and 50 r/min. The field-testing process of the seeding monitoring system is shown in Figure 13.

According to the current Chinese national standard GB/T 6973-2005 “Test Methods for Single Seed (Precision) Planters” [32], the initial 15 m at both ends of the test field were designated as the equipment adjustment zone. The 5 m adjacent to the adjustment zone were utilized as the equipment start-up zone, while the area beyond the start-up zone constituted the stable seeding operation zone. Upon completion of seeding, 13 sets of manual seed digging inspections were performed within the stable seeding operation zone. Each set randomly selected 1625 holes, with no repeat inspections on holes that had already been dug. The actual number of single seeds, missed seeds, and multiple seeds were recorded and compared with the corresponding seeder’s monitoring values to calculate the monitoring accuracy for various seeding states. The following definitions were applied: single-seeding monitoring accuracy is the percentage of monitored single seeds relative to the actual number of single seeds; missed-seeding monitoring accuracy is the percentage of actual missed seeds relative to the monitored number of missed seeds; and multiple-seeding monitoring accuracy is the percentage of monitored multiple seeds relative to the actual number of multiple seeds.

3. Results and Discussion

3.1. Bench Test

The monitoring results for single seeding at various speeds are presented in

Table 3. Monitoring results of single seeding at different rotational speeds.

Speed (r/min)	Category	Test Group Number
		1	2	3	4	5	6	7
	Actual Single Seeding	300	300	300	300	300	300	300
30	Monitor single seeding	289	296	295	297	294	296	297
	Monitor missed seeding	2	4	5	3	6	4	3
	Monitor multiple seeding	0	0	0	0	0	0	0
	Misjudged	2	4	5	3	6	4	3
	Accuracy (%)	99.33	98.67	98.33	99.00	98.00	98.67	99.00
40	Monitor single seeding	293	291	295	294	293	295	292
	Monitor missed seeding	7	9	5	8	7	6	8
	Monitor multiple seeding	0	0	0	0	0	0	0
	Misjudged	7	9	5	8	7	6	8
	Accuracy (%)	97.67	98.33	97.00	97.33	97.67	98.00	97.33
50	Monitor single seeding	289	291	290	287	288	290	289
	Monitor missed seeding	11	9	10	12	11	10	12
	Monitor multiple seeding	0	0	0	0	0	0	0
	Misjudged	11	9	10	12	11	10	12
	Accuracy (%)	96.33	97.00	96.67	96.00	96.33	96.67	96.00

. The findings indicate that as the seeder’s speed rises, the monitoring accuracy for individual seeds by the system experiences a slight decline. During single-seeding monitoring, there are a few instances of missed seeds but no occurrences of multiple seeds. The primary cause is the variation in seed size, which results in smaller seeds sliding along the inner wall of the seed outlet and not effectively obstructing the photoelectric channel. Consequently, the system fails to detect the blocking signal and incorrectly identifies these as missed seeds. Nonetheless, the system’s accuracy for single-seeding monitoring exceeds 96.00%, satisfying the monitoring system’s criteria for single-seeding accuracy.

The monitoring results of missed seeding at various speeds are presented in

Table 4. Monitoring results of missed seeding at different rotational speeds.

Speed (r/min)	Category	Test Group Number
		1	2	3	4	5	6	7
	Monitor Missed Seeding	300	300	300	300	300	300	300
30	Actual missed seeding	297	295	296	298	295	297	298
	Actual single seeding	3	4	3	2	3	2	2
	Actual multiple seeding	0	1	1	0	2	1	0
	Misjudged	3	5	4	2	5	3	2
	Accuracy (%)	99.00	98.33	98.67	99.33	98.33	99.00	99.33
40	Actual missed seeding	291	293	291	292	289	293	290
	Actual single seeding	7	6	6	7	8	5	7
	Actual multiple seeding	2	1	3	1	3	2	3
	Misjudged	9	7	9	8	11	7	10
	Accuracy (%)	97.00	97.67	97.00	97.33	96.33	97.67	96.67
50	Actual missed seeding	289	291	287	290	288	291	289
	Actual single seeding	8	7	10	8	9	8	9
	Actual multiple seeding	3	2	3	2	3	1	2
	Misjudged	11	9	13	10	12	9	11
	Accuracy (%)	96.33	97.00	95.67	96.67	96.00	97.00	96.33

. The findings indicate that as the seeder’s speed increases, the monitoring accuracy for missed seeding by the system exhibits a slight decline. Within the missed-seeding monitoring outcomes, there are instances of single seeding and multiple seeding that were not accurately detected. The cause is identical to that of single-seeding misjudgment—smaller seeds sliding along the inner wall of the seed outlet, bypassing the photoelectric sensor’s monitoring—which leads to a few cases of single seeding and multiple seeding being incorrectly identified as missed seeding. Nonetheless, the system’s missed-seeding monitoring accuracy remains above 95.67%, satisfying the seeder’s required accuracy for missed-seeding monitoring.

The monitoring results of multiple seeding at various speeds are presented in

Table 5. Monitoring results of multiple seeding at different rotational speeds.

Speed (r/min)	Category	Test Group Number
		1	2	3	4	5	6	7
	Actual Multiple Seeding	300	300	300	300	300	300	300
30	Monitor multiple seeding	295	297	298	297	296	298	295
	Monitor single seeding	4	2	2	2	3	2	3
	Monitor missed seeding	1	1	0	1	0	0	2
	Misjudged	5	3	2	3	4	2	5
	Accuracy (%)	98.33	99.00	99.33	99.00	98.67	99.33	98.33
40	Monitor multiple seeding	293	291	294	292	293	291	292
	Monitor single seeding	5	6	5	6	5	6	6
	Monitor missed seeding	2	3	1	2	2	3	2
	Misjudged	7	9	6	8	7	9	8
	Accuracy (%)	97.67	97.00	98.00	97.33	97.67	97.00	97.33
50	Monitor multiple seeding	289	287	290	288	291	288	289
	Monitor single seeding	9	10	8	9	7	10	8
	Monitor missed seeding	2	3	2	3	2	2	3
	Misjudged	11	13	10	12	9	12	11
	Accuracy (%)	97.00	95.67	96.67	96.00	97.00	96.00	96.33

. The data indicate that as the seeder’s speed increases, the monitoring accuracy for multiple seeding by the system does not significantly decrease. Nonetheless, within the multiple-seeding monitoring outcomes, there are a few instances of single seeding and missed seeding. The cause for this is that when two seeds pass through the sensor’s seed tube simultaneously, the duration they obstruct the sensor’s light path is shorter than the multiple-seeding threshold, leading the system to misinterpret it as single seeding. Moreover, smaller seeds tend to slide along the inner wall of the seed outlet without entering the monitoring channel, thus evading detection by the photoelectric sensor, which results in the system mistaking them for missed seeding. Additionally, there are instances where one of the two seeds slides along the inner wall of the seed tube, bypassing the monitoring channel, causing only one seed to block the sensor’s light path and leading the monitoring system to register it as single seeding. However, the system’s multiple-seeding monitoring accuracy remains above 95.67%, satisfying the accuracy requirements for multiple-seeding monitoring of the seeder.

In summary, at operating speeds ranging from 2.5 to 4 km/h (corresponding to seeder speeds of 30 to 50 r/min), the system’s monitoring accuracy for single seeding, missed seeding, and multiple seeding all meet the usage requirements. During single-seeding monitoring single-factor tests, the average probability of misjudgment as missed seeding is 2.41%. In missed-seeding monitoring single-factor tests, the average probability of misjudgment for single seeding is 1.97%, and for multiple seeding, it is 0.57%. For multiple-seeding monitoring single-factor tests, the average probability of misjudgment as single seeding is 1.87% and as missed seeding is 0.59%. The system’s average misjudgment probability is 2.47%. If screened seeds are used for monitoring tests, these system misjudgment issues can be effectively controlled.

As illustrated in Figure 14, with the increase in seeder speed, the monitoring accuracy for various seeding states experiences a slight decrease. The primary reason for this decline in monitoring accuracy is that higher seeder speeds increase the likelihood of seeds colliding with the guide slot, resulting in unstable seed movement trajectories. This instability causes seeds to block the photoelectric sensor for too short a duration, not reaching the monitoring threshold, thereby increasing the probability of missed detection. Nonetheless, the system’s monitoring accuracy for various seeding states at different speeds remains above 97.00%, with an average monitoring accuracy for single seeding at 97.59%, missed seeding at 97.46%, and multiple seeding at 97.56%. These outcomes demonstrate that the system can effectively and accurately monitor the seeding state of the seeder across different operating speeds, and the overall monitoring accuracy satisfies the requirements for single seeding, missed seeding, and multiple seeding.

3.2. Field Test

The field test results of the system at various seeding speeds are presented in

Table 6. Field test results of the seeding monitoring system under different seeding speeds.

Speed (r/min)	Category	Test Group Number
		1	2	3	4	5	6	7	8	9	10	11	12	13
30	Actual single seeding	1625	1625	1625	1625	1625	1625	1625	1625	1625	1625	1625	1625	1625
	Monitor single seeding	1596	1603	1595	1590	1597	1601	1592	1600	1594	1602	1598	1591	1599
	Accuracy (%)	98.22	98.65	98.15	97.85	98.28	98.52	97.97	98.46	98.09	98.58	98.34	97.91	98.40
	Actual missed seeding	47	52	41	48	54	46	53	51	54	49	44	48	50
	Monitor missed seeding	48	53	42	49	55	47	54	52	56	50	45	49	51
	Accuracy (%)	97.92	98.11	97.62	97.96	98.18	97.87	98.15	98.08	96.43	98.00	97.78	97.96	98.04
	Actual multiple seeding	49	52	50	45	50	53	42	51	49	48	55	47	50
	Monitor multiple seeding	48	51	51	44	49	51	41	50	48	47	54	46	49
	Accuracy (%)	97.96	98.08	98.04	97.78	98.00	96.23	97.62	98.04	97.96	97.92	98.18	97.87	98.00
40	Actual single seeding	1625	1625	1625	1625	1625	1625	1625	1625	1625	1625	1625	1625	1625
	Monitor single seeding	1582	1576	1580	1583	1579	1581	1577	1584	1578	1580	1576	1581	1585
	Accuracy (%)	97.35	96.98	97.23	97.42	97.17	97.29	97.05	97.48	97.11	97.23	96.98	97.29	97.54
	Actual missed seeding	59	55	51	56	59	53	60	58	56	54	59	57	61
	Monitor missed seeding	61	57	53	58	61	55	63	60	58	56	62	59	63
	Accuracy (%)	96.72	96.49	96.23	96.55	96.72	96.36	95.24	96.67	96.55	96.43	95.16	96.61	96.83
	Actual multiple seeding	59	61	54	65	57	62	60	67	55	63	58	64	56
	Monitor multiple seeding	57	59	52	62	55	60	58	64	53	61	56	62	54
	Accuracy (%)	96.61	96.72	96.30	95.38	96.49	96.77	96.67	95.52	96.36	96.83	96.55	96.88	96.43
50	Actual single seeding	1625	1625	1625	1625	1625	1625	1625	1625	1625	1625	1625	1625	1625
	Monitor single seeding	1564	1568	1562	1567	1573	1560	1565	1561	1572	1563	1570	1566	1571
	Accuracy (%)	96.25	96.49	96.12	96.43	96.80	96.00	96.31	96.06	96.74	96.18	96.62	96.37	96.68
	Actual missed seeding	61	57	63	58	60	56	62	59	61	65	58	60	63
	Monitor missed seeding	64	60	66	61	63	59	65	62	64	62	61	63	66
	Accuracy (%)	95.31	95.00	95.45	95.08	95.24	94.92	95.38	95.16	95.31	95.38	95.08	95.24	95.45
	Actual multiple seeding	62	65	61	66	63	60	62	59	63	64	66	58	60
	Monitor multiple seeding	59	62	58	63	60	57	59	56	60	61	63	55	57
	Accuracy (%)	95.16	95.38	95.08	95.45	95.24	95.00	95.16	94.91	95.24	95.31	95.45	94.83	95.00

. The data indicate that when the seeder operates at speeds ranging from 30 to 50 revolutions per minute (corresponding to a seeder speed of 2.5 to 4 km per hour), the system’s monitoring accuracy for different seeding states exceeds 94.83%. Specifically, the average monitoring accuracy for individual seeding is 97.30%, which is 0.29 percentage points lower than the bench test results. The average monitoring accuracy for missed seeding is 96.48%, 0.98 percentage points lower than the bench test, and the average monitoring accuracy for multiple seeding is 96.47%, which is 1.09 percentage points lower than the bench test. Overall, the system’s field seeding monitoring accuracy satisfies the usage requirements.

As depicted in Figure 15, at identical seeder speeds, the system’s field monitoring accuracy is marginally lower than that of the bench test. This discrepancy arises because during field tests, the seeder adjusts to the terrain’s irregularities and the presence of stones and clumps of mud on the seeding surface can cause vibrations, disrupting the normal trajectory of seeds within the seeder. Consequently, the monitoring system’s accuracy is slightly reduced compared to the bench test. However, these factors do not significantly affect the stability and monitoring accuracy of the seeding monitoring system.

In future studies, the photoelectric monitoring module will be optimized to address misjudgments caused by changes in seed trajectory during field sowing. The monitoring area of the sensor will be enlarged to eliminate blind spots within the detection channel, ensuring that the system is not affected by field vibrations and further improving monitoring accuracy.

4. Conclusions

This study initially analyzed the working principle and seed movement trajectory of the cotton toothed-disk precision seeder. Subsequently, based on the working principle and internal structure of the seeder, a modular photoelectric sensor was designed, and for the first time, the monitoring node was proposed to be placed after the seed separation in the seed guiding chamber, ensuring that the photoelectric sensor is not affected by seed coating dust during monitoring. Then, a seeding monitoring system for cotton toothed-disk precision seeders was constructed using the designed modular photoelectric sensor and capacitance sensor.

Furthermore, based on the hardware structure of the monitoring system, a monitoring counting algorithm based on a spatiotemporal joint model was proposed. The capacitance sensor’s trigger signal was used to divide the monitoring time window, and a spatiotemporal joint model was established to correspond each monitoring time window with the photoelectric sensor. The seeder’s seeding state was determined by capturing the photoelectric signals within the monitoring time window.

Finally, the system testing experiment was conducted, and the field test results showed that the average monitoring accuracy of single seeding by the modular photoelectric monitoring system was 97.30%, the average monitoring accuracy of missed seeding was 96.48%, and the average monitoring accuracy of multiple seeding was 96.47%. The monitoring accuracy of the system meets the monitoring requirements for various seeding states of the seeder at different operating speeds, providing technical support for improving the quality and efficiency of cotton seeding in Xinjiang.