Due to the high elevator speed of the combine harvester under field operating conditions, it is difficult to capture accurate observations. Additionally, during the lifting process, corn kernels may fall, making it impossible to directly observe the accumulation of corn kernels through effective means. Therefore, EDEM simulation can be used to observe the changes in corn morphology under different feed rates. To realize this, we adopt EDEM software to simulate and analyze the corn stacking behavior on the elevator scraper associated with different feeding amounts. Firstly, a corn kernel model is constructed in EDEM to represent a physical corn. The study repeats measurements several times and takes the average value, having the three dimensions of a corn kernel as 12.8mm in length, 9mm in width, and 5mm in thickness. The study utilizes 15 small spheres to combine and generate the corn model shown in Figure 3A . Next, the elevator is determined to have an equal scale to the actual harvester’s elevator and is built in SolidWorks, as shown in Figure 3B . The elevator’s side wall and chain are made of steel, while the scraper is made of rubber. According to the literature (Wang et al., 2016) corn kernels constantly collide with the elevator’s inner wall, chain, and scraper. Thus, the mechanical properties of corn, scraper, and elevator are carefully determined (Liu et al., 2022; Hongmei et al., 2023), as are the collision parameters. Table 1 demonstrates the properties mentioned above. The collision model used in the simulation is Hertz-Mindlin (no slip), and the friction model is Standard Rolling Friction.

The model only keeps the elevator and discards the auger to reduce the computational cost and guarantee the smoothness of the simulation. The structure’s transparency is set to 0.3 to visualize the animation. Most harvesters’ elevators tilt between 5° and 15°. Such an angle allows corn to flow over the scraper while avoiding uneven stacking or slippage. In this study, the tilt angle θ is 15°. The elevator’s cross-sectional area is 300mm in length and 150mm in width. A rectangle of the same size is 150mm from the bottom of the elevator. This position is determined where the auger feeds corn into the elevator. The software uses this rectangular area as the corn plant to generate corn kernels, as displayed in Figure 4 .

In the harvesting circumstance, the feeding rate or feeding amount varies in terms of corn density and operating parameters. The feeding amount, in turn, dramatically affects the corn stacking pattern on the scraper. The study simulates three conditions regarding low, medium, and high feeding amounts for a comprehensive investigation and analysis. The study utilizes rotary and linear motions for scrapers and chains to lift the corn kernels for the additive motion. The elevator’s rotational speed ranges from 500 to 600r/min, with the linear motion speed being 2.5m/s since the elevator can constantly rotate in a harvester. The elevator model in the simulation is constructed based on the actual elevator of the combine harvester. The corn feed rates required for the simulation under different conditions are calculated by measuring the height between the scrapers in the elevator. After calculation, the initial corn weight of 2 kg, 5 kg, and 8 kg meets the demand to simulate low, medium, and high feed rate scenarios.

Figure 5 demonstrates the morphology of corn stacking in terms of different feeding amounts. When the plant imports 2kg of corn kernels, the kernels pile up at the outer end of the scraper while the inner end has less corn. The morphology of stacking corn retains a similar shape while lifting the scraper, indicating a stable stacking process that is not significantly affected by the elevator tilt and gravity. The stable shape of the corn accumulation is observed as a trigonometry. When the plant imports 5kg of corn kernels, the corn stacking on the scraper is centered and piles up at the outer end when the scraper rotates at the bottom of the elevator. However, corn deviates to the inner end during lifting: the elevator tilt and gravity cause the phenomenon. The morphology varies from the light feeding amount, and the final morphology becomes a rectangle. When the plant imports 8kg of corn kernels, the outer end of the scraper is full of corn. When the scraper ascends, the tilt and gravity compress the corn towards the inner end, resulting in higher inner accumulation than the outer. Moreover, the final morphology is a trapezium, and the incline angle tends to parallel the scraper. Based on the analysis, the morphology of corn stacking patterns on the scraper varies via lifting at different feeding amounts. Therefore, it is reasonable that a simple mathematical model only formulated by the scraper geometry will be inaccurate for predicting the yield. Repetitive corrections of such a model are required when the harvester operates at different feeding conditions, which is cumbersome, inconvenient, and lacks maneuverability.

During the field operation of the combine harvester, the scraper in the elevator conveys the grain into the grain tank. During this process, the height information of corn kernels on the elevator scraper is the most crucial data for reflecting the yield of the combine harvester. This information is also the most effectively and conveniently one to be collected by a system, and can well-illustrate the variation of flow rate and instantaneous yield. In this process, the system is well-designed to collect the variable value of the grain pile height inside the elevator, which best reflects the accumulation of kernels on the scraper at each instance. Deep Neural Network (DNN), Gradient Boosting Machine (GBM), and Random Forest (RF) are trained to construct a yield prediction model under the composite condition. Then, the study tests these models’ performances with three working conditions. The coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) are the model evaluation metrics. R² is chosen to better reflect the goodness of fit of the model, thereby evaluating the overall predictive performance of the system. RMSE and MAE are selected to reflect the average error between the predicted and actual values, allowing for the assessment of the system’s accuracy and precision. The coefficient of determination evaluates the fitting effect of the models, with its value closer to 1 indicating a better model fitting and the overall performance of the system is improved. The root mean square and absolute errors assess the model’s accuracy, whose values closer to 0 indicate more precise predictions and the higher the accuracy and precision of the system.

The experimental bench is designed according to the structure and size of the 4YL-6 (8568) corn harvester’s internal elevator, independently developed by the National Innovation Centre for Agricultural Machinery and Equipment. It mainly consists of six parts: the main elevator, weighing bucket, transfer bin, auxiliary elevator, sieve, and feeding bin, which are capable of completing the functions of maize circulation and real-time weighing. Two DC motors drive the main elevator, auxiliary elevators, and auger. The sensor bracket is used to fix the opposite-type photoelectric sensor on the main elevator, as shown in Figure 6A . The weighing bucket with a maximum capacity of 100 kg is designed to accommodate the corn discharged by the elevator. In order to obtain the accurate weight of the harvested corn in real-time, we adopt a three-point measurement method is used for weighing by adding high-precision spoke weighing sensors, as shown in Figure 6B . The weighing sensor model is Decent DSLF-102, with a 0-100kg range and a measurement accuracy error of less than 0.05%. As illustrated in Figure 6C , the rig installs a frequency converter to control the motor speed, allowing the elevator and auger speed adjustment by varying the frequency of the converter.

The study designs the training dataset under a composite working condition and a verification dataset under three independent working conditions, including low, medium, and high feeding amounts. Jin et al. (Chengqian et al., 2022) studied the grain accumulation patterns at different elevator speeds, however, the investigation of the effect of the accumulation patterns under varying feed rates is not obvious. This study proposes using different auxiliary elevator speeds to generate obvious feed rate changes during actual operations, providing a more comprehensive analysis of corn kernel accumulation patterns inside the elevator. In the experiment, the main elevator’s speed is set at a constant 550 r/min. The low feeding amount signifies the auxiliary elevator’s speed of 150 r/min, 225 r/min for the medium, and 450 r/min for the high. The height and maize bucket’s weight information are synchronously collected. During each test, the system collects the height information of the corn pile on the main elevator’s scraping board. The weighing sensor records the yield weight data correspondingly and transmits the data to the system. The composite working condition integrates low, medium, and high feeding amount conditions. When the feeding amount changes from low to high, the data are continuously collected to build the composite dataset. The verification dataset contains two sorts of data while performing three working conditions, respectively. 3.1.3 Preprocessing of raw data.

A 380V variable-frequency motor drives the experimental bench, which leads to electromagnetic interference on the sensor signals due to the high current and frequent conversion. The system introduces an optocoupler isolation circuit to suppress the noise of the sensor signals. We observe the abnormal sensor data in the indoor experiments and analyze the elevator’s vibration, which causes the kernel to fall from the scraper, some of which blocks the sensors. Moreover, we also observe such abnormal signals in the field experiments due to falling kernels induced by variant travel speeds and the unevenness of the farmland. Therefore, this paper employs a time-domain threshold smoothing interpolation algorithm to preprocess the data collected by the sensors. When the collected raw data arrives at 10, the following data is processed and filtered. The average value of the previous 10 data points is V_An , and the ratio between the following data and the mean data V_An is determined. Considering the signal changes w.r.t. different conditions, we determine the threshold as V_MAX =3 V_An and 3V _MIN = V_An . During threshold filtering, data exceeding the threshold range are replaced by smoothed interpolation as V_n = V_An , while data within the threshold range are retained.

where, n > 10 , and V n represents the original value of each data processing, V M I N is the minimum value of the threshold range, and V M A X is the maximum value of the threshold range. As observed in Figure 7A , the original signal is severely affected by noise interference, making it difficult for the system to identify the proper signal and collect information normally. On the contrary, Figure 7B shows the signal after preprocessing and observes that the interference effect is greatly reduced. At the same time, the signal-to-noise ratio has enhanced, promising adequate data information.

A total of 724 data sets were collected in the experiment, of which 700 sets are valid. Among these datasets, 400 are for compound situation sets and 100 sets for each of the three feeding levels. The data are divided into training and test sets by a ratio of 4:1. We arrived at a DNN model structure with five hidden layers through multiple training and parameter optimizations. The first layer contained 1,024 neurons; half of the neuron number is used in the subsequent layer. The ReLU activation function is also used to enhance the model’s non-linear representation capability, while Dropout is employed to prevent overfitting. The Adam optimizer, with a learning rate of 0.01, is used. The GBM model is built with 100 weak learners, while the RF model also has 100 decision trees with a maximum depth of 3. The loss function utilizes the mean squared error (MSE) running with 300 epochs. Figure 8 presents the predictive performance of these three yield prediction models derived from the compound dataset. As analyzed, the DNN model achieves the highest R² value of 0.998 for both training and testing sets. It also has stable RMSE and MAE without significant fluctuations. The GBM model exhibits the best fit in the training set; however, the model results in outliers, considerable fluctuations, and lower accuracy with the RMSE higher than 1 for the test set. The RF model performs similarly to the GBM model. Although the predicted results best fit the test set, the results exhibit outliers and considerable variability and lead to a poor RMSE greater than 1 in the training set.

Figure 9 illustrates the performance of each algorithmic model in predicting corn yields under various working conditions, aiming to determine the model proper for various corn stacking patterns comprehensively. The analysis shows that under low feeding conditions (150 r/min), the DNN model achieves the best fit, with the highest R² value of 0.997. The RF model has the worst fit, with the lowest R² of 0.986, while the GBM model’s performance is intermediate. Under medium feeding conditions (225 r/min), both the GBM and RF models show improved fit but with a higher RMSE, indicating increased error. The GBM model has the most volatility, with an R² of 0.931, while the RF model displays signs of overfitting. The DNN model performs well overall, with the highest R² of 0.992 and considerably low RMSE and MAE. Under high feeding conditions (450 r/min), although the GBM model’s fit is relatively good, it contains numerous poor fitting points. The DNN model demonstrates the highest stability, achieving an R² of 0.999, indicating superior predictive accuracy. In contrast, the RF model performs the worst, with a RMSE greater than 1. In summary, the DNN model consistently predicts the yield with the highest R² values under three working conditions, exhibits the most robust fitting capability, and stably produces low RMSE and MAE. Therefore, we propose the DNN-based prediction model to forecast the corn yields.

Field experiments for monitoring the corn harvester’s yield were performed to validate the developed system and test the prediction models. These tests were taken in mid-November 2023 in Nongqiao Town, Jiamusi City, Heilongjiang Province. The weather during the experiments was sunny, with temperatures ranging from -11°C to -3°C and a moderate southwest wind at Beaufort scale 3. Given that the crop was snow-covered, daytime melting increased the moisture content of the kernels. Such a condition potentially led to drum clogging and impeded harvesting operations. Consequently, the study carried out the tests at night.

The study installed the system and performed the field experiment on a 4YL-8 corn combine harvester designed and fabricated by the Innovation Centre for Agricultural Machinery and Equipment. Table 2 presents the harvester’s geometric and working parameters. Figure 10 demonstrates the installation positions of all the necessary components of the corn yield monitoring system. The thru-beam photoelectric sensor were mounted on a bracket and installed near the top of the harvester’s elevator. The GNSS antenna was mounted on top of the harvester’s side-view mirror. The display terminal, comprising a rugged computer and a controller unit for signal receiving and processing, was placed in the cab for real-time monitoring.

Before the field experiment, the harvester’s parameters in Table 2 were set in the yield monitoring system. For the bulk density value in the system, the study collected corn in the field and measured the bulk density, which then replaced the value for the indoor experiment, aiming to eliminate the difference due to corn variety and moisture content, promising a more accurate yield prediction. During the experiment, the system collected the height of the corn pile on scrapers and harvester positions in real-time. The system predicted the instantaneous flow rate and the cumulative yield value accordingly. The actual yield value of the harvester was periodically measured using a platform scale. The study employs and analyzes the relative error between the predicted and the actual yield value by the following equation.

where, E r is the relative error, M p is the system’s prediction of corn yield, and M r is the actual corn yield.

As presented in Table 3 , field data are imported into three models to predict the yield, including DNN, GBM, and RF. The results show that the DNN-based model has a maximum relative error of 2.29% in yield prediction, with an average accuracy of 97.85%. The GBM-based model has a maximum relative error of 3.81%, with an average accuracy of 97.8%. The RF-based model demonstrates the highest maximum relative error at 4.26%, with an average accuracy of 96.45%. Although the average accuracy for the DNN and GBM models is similar, the DNN model exhibits notably better stability. The field experimental results denote that the proposed DNN model can reliably predict corn kernel yield during harvesting operations.