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Machine vision with deep learning is a promising type of automatic visual perception for detecting and segmenting an object effectively; however, the scarcity of labelled datasets in agricultural fields prevents the application of deep learning to agriculture. For this reason, this study proposes weakly supervised crop area segmentation (WSCAS) to identify the uncut crop area efficiently for path guidance. Weakly supervised learning has advantage for training models because it entails less laborious annotation. The proposed method trains the classification model using area-specific images so that the target area can be segmented from the input image based on implicitly learned localization. This way makes the model implementation easy even with a small data scale. The performance of the proposed method was evaluated using recorded video frames that were then compared with previous deep-learning-based segmentation methods. The results showed that the proposed method can be conducted with the lowest inference time and that the crop area can be localized with an intersection over union of approximately 0.94. Additionally, the uncut crop edge could be detected for practical use based on the segmentation results with post-image processing such as with a Canny edge detector and Hough transformation. The proposed method showed the significant ability of using automatic perception in agricultural navigation to infer the crop area with real-time level speed and have localization comparable to existing semantic segmentation methods. It is expected that our method will be used as essential tool for the automatic path guidance system of a combine harvester.
Wan-Soo Kim; Dae-Hyun Lee; Taehyeong Kim; Hyunggun Kim; Taeyong Sim; Yong-Joo Kim. Weakly Supervised Crop Area Segmentation for an Autonomous Combine Harvester. Sensors 2021, 21, 4801 .
AMA StyleWan-Soo Kim, Dae-Hyun Lee, Taehyeong Kim, Hyunggun Kim, Taeyong Sim, Yong-Joo Kim. Weakly Supervised Crop Area Segmentation for an Autonomous Combine Harvester. Sensors. 2021; 21 (14):4801.
Chicago/Turabian StyleWan-Soo Kim; Dae-Hyun Lee; Taehyeong Kim; Hyunggun Kim; Taeyong Sim; Yong-Joo Kim. 2021. "Weakly Supervised Crop Area Segmentation for an Autonomous Combine Harvester." Sensors 21, no. 14: 4801.
This study is focused on the estimation of fuel consumption of the power-shift transmission (PST) tractor based on PTO (power take-off) dynamometer test. The simulation model of PST tractor was developed using the configurations and powertrain of the real PST tractor. The PTO dynamometer was installed to measure the engine load and fuel consumption at various engine load levels (40, 50, 60, 70, 80, and 90%), and verify the simulation model. The axle load was also predicted using tractor’s specifications as an input parameter of the simulation model. The simulation and measured results were analyzed and compared statistically. It was observed that the engine load, as well as fuel consumption, were directly proportional to the engine load levels. However, it was statistically proved that there was no significant difference between the simulation and measured engine torque and fuel consumption at each load level. The regression equations show that there was an exponential relationship between the fuel consumption and engine load levels. However, the specific fuel consumptions (SFC) for both simulation and measured were linear relationships and had no significant difference between them at each engine load level. The results were statistically proved that the simulation and measured SFCs were similar trends. The plow tillage operation could be performed at the gear stage of 7.65 km/h with higher working efficiency at low fuel consumption. The drawback of this study is to use a constant axle load instead of dynamic load. This study can provide useful information for both researchers and manufacturers related to the automated transmission of an agricultural tractor, especially PST tractor for digital farming solutions. Finally, it could contribute to the manufacturers developing a new agricultural tractor with higher fuel efficiency.
Abu Ayub Siddique; Seung-Min Baek; Seung-Yun Baek; Wan-Soo Kim; Yeon-Soo Kim; Yong-Joo Kim; Dae-Hyun Lee; Kwan-Ho Lee; Joon-Yeal Hwang. Simulation of Fuel Consumption Based on Engine Load Level of a 95 kW Partial Power-Shift Transmission Tractor. Agriculture 2021, 11, 276 .
AMA StyleAbu Ayub Siddique, Seung-Min Baek, Seung-Yun Baek, Wan-Soo Kim, Yeon-Soo Kim, Yong-Joo Kim, Dae-Hyun Lee, Kwan-Ho Lee, Joon-Yeal Hwang. Simulation of Fuel Consumption Based on Engine Load Level of a 95 kW Partial Power-Shift Transmission Tractor. Agriculture. 2021; 11 (3):276.
Chicago/Turabian StyleAbu Ayub Siddique; Seung-Min Baek; Seung-Yun Baek; Wan-Soo Kim; Yeon-Soo Kim; Yong-Joo Kim; Dae-Hyun Lee; Kwan-Ho Lee; Joon-Yeal Hwang. 2021. "Simulation of Fuel Consumption Based on Engine Load Level of a 95 kW Partial Power-Shift Transmission Tractor." Agriculture 11, no. 3: 276.
The objective of this study was to develop a model to estimate the axle torque (AT) of a tractor using an artificial neural network (ANN) based on a relatively low-cost sensor. ANN has proven to be useful in the case of nonlinear analysis, and it can be applied to consider nonlinear variables such as soil characteristics, unlike studies that only consider tractor major parameters, thus model performance and its implementation can be extended to a wider range. In this study, ANN-based models were compared with multiple linear regression (MLR)-based models for performance verification. The main input data were tractor engine parameters, major tractor parameters, and soil physical properties. Data of soil physical properties (i.e., soil moisture content and cone index) and major tractor parameters (i.e., engine torque, engine speed, specific fuel consumption, travel speed, tillage depth, and slip ratio) were collected during a tractor field experiment in four Korean paddy fields. The collected soil physical properties and major tractor parameter data were used to estimate the AT of the tractor by the MLR- and ANN-based models: 250 data points were used for developing and training the model were used, the 50 remaining data points were used to test the model estimation. The AT estimated with the developed MLR- and ANN-based models showed agreement with actual measured AT, with the R2 value ranging from 0.825 to 0.851 and from 0.857 to 0.904, respectively. These results suggest that the developed models are reliable in estimating tractor AT, while the ANN-based model showed better performance than the MLR-based model. This study can provide useful results as a simple method using ANNs based on relatively inexpensive sensors that can replace the existing complex tractor AT measurement method is emphasized.
Wan-Soo Kim; Dae-Hyun Lee; Yong-Joo Kim; Yeon-Soo Kim; Seong-Un Park. Estimation of Axle Torque for an Agricultural Tractor Using an Artificial Neural Network. Sensors 2021, 21, 1989 .
AMA StyleWan-Soo Kim, Dae-Hyun Lee, Yong-Joo Kim, Yeon-Soo Kim, Seong-Un Park. Estimation of Axle Torque for an Agricultural Tractor Using an Artificial Neural Network. Sensors. 2021; 21 (6):1989.
Chicago/Turabian StyleWan-Soo Kim; Dae-Hyun Lee; Yong-Joo Kim; Yeon-Soo Kim; Seong-Un Park. 2021. "Estimation of Axle Torque for an Agricultural Tractor Using an Artificial Neural Network." Sensors 21, no. 6: 1989.
The objective of this study is the simulation of the most affected design factors and variables of the clutch pack for the power-shift transmission (PST) of a tractor based measured data. The simulation model, the mathematical model of sliding velocity, a moment of inertia, and clutch engagement pressure of clutch pack were developed using the powertrain and configurations of the real PST tractor. In this study, the sensor fusion method was used to precisely measure the proportional valve pressure by test bench, which was applied to the simulation model. The clutch engagement times were found 1.20 s at all temperatures for determined factors. The engagement pressures have a significant difference at various temperatures (25 to 100 °C) of the hydraulic oils after the 1.20 s but the most affected factors were satisfied with the simulation conditions that ensure the clutch engagement on time. Finally, this sensor fusion method is believed to be helpful in realizing precision agriculture through minimization of power loss and maximum energy efficiency of tractors.
Abu Ayub Siddique; Wan-Soo Kim; Yeon-Soo Kim; Seung-Yun Baek; Seung-Min Baek; Yong-Joo Kim; Seong-Un Park; Chang-Hyun Choi. Simulation of Design Factors of a Clutch Pack for Power-Shift Transmission for an Agricultural Tractor. Sensors 2020, 20, 7293 .
AMA StyleAbu Ayub Siddique, Wan-Soo Kim, Yeon-Soo Kim, Seung-Yun Baek, Seung-Min Baek, Yong-Joo Kim, Seong-Un Park, Chang-Hyun Choi. Simulation of Design Factors of a Clutch Pack for Power-Shift Transmission for an Agricultural Tractor. Sensors. 2020; 20 (24):7293.
Chicago/Turabian StyleAbu Ayub Siddique; Wan-Soo Kim; Yeon-Soo Kim; Seung-Yun Baek; Seung-Min Baek; Yong-Joo Kim; Seong-Un Park; Chang-Hyun Choi. 2020. "Simulation of Design Factors of a Clutch Pack for Power-Shift Transmission for an Agricultural Tractor." Sensors 20, no. 24: 7293.
The spiral bevel gear in a tractor, unlike the other gears in the transmission, is one of the most vulnerable gears in terms of fatigue life, as it is consistently driven throughout the operations of the tractor. Conventional fatigue life tests of transmission gears require expensive equipment and repeated tests, and do not reflect dynamic field loads. The aim of this study is to develop a simulation model which can replace conventional fatigue life tests for actual gears, in order to evaluate the fatigue life of a tractor using dynamic field load data. A transmission simulation model including a spiral bevel gear was developed using commercial software. In order to measure the dynamic load of the tractor according to various field operations, an axle torque measurement system was developed, and field experiments were performed for major agricultural operations occurring in the field. Fatigue life was calculated using Rainflow cycle counting (RFC), the Smith–Watson–Topper (SWT) model, and S–N curves based on torque data measured in the field. The fatigue life under moldboard plow tillage, subsoiler tillage, rotary tillage, and baler operation were 13,599, 285, 278,884, and 525,977 h, respectively. The fatigue life of the tractor, according to subsoiler tillage and baler operation, was 0.104 and 192 times the service life, respectively, where the difference between these two operations was about 1846 times. The fatigue life of the tractor, according to the attached implement type, was significantly different. Therefore, it can be seen that the fatigue life of a tractor can be significantly different, depending on agricultural operation type which the farmer uses most often; this can be used as basic data for tractor design and evaluation. In addition, it is considered that the developed simulation model can be applied to fatigue life evaluation using dynamic field load data.
Wan-Soo Kim; Yong-Joo Kim; Seung-Min Baek; Seok-Pyo Moon; Nam-Gyu Lee; Yeon-Soo Kim; Seong-Un Park; Yong Choi; Young-Keun Kim; Il-Su Choi; Duck-Kyu Choi; Chang-Hyun Choi. Fatigue Life Simulation of Tractor Spiral Bevel Gear According to Major Agricultural Operations. Applied Sciences 2020, 10, 8898 .
AMA StyleWan-Soo Kim, Yong-Joo Kim, Seung-Min Baek, Seok-Pyo Moon, Nam-Gyu Lee, Yeon-Soo Kim, Seong-Un Park, Yong Choi, Young-Keun Kim, Il-Su Choi, Duck-Kyu Choi, Chang-Hyun Choi. Fatigue Life Simulation of Tractor Spiral Bevel Gear According to Major Agricultural Operations. Applied Sciences. 2020; 10 (24):8898.
Chicago/Turabian StyleWan-Soo Kim; Yong-Joo Kim; Seung-Min Baek; Seok-Pyo Moon; Nam-Gyu Lee; Yeon-Soo Kim; Seong-Un Park; Yong Choi; Young-Keun Kim; Il-Su Choi; Duck-Kyu Choi; Chang-Hyun Choi. 2020. "Fatigue Life Simulation of Tractor Spiral Bevel Gear According to Major Agricultural Operations." Applied Sciences 10, no. 24: 8898.
The Internet of Things (IoT) is a network of devices for communicating machine to machine (M2M) based on wired and wireless Internet. IoT in agriculture is a revolutionary technology that can be applied to agricultural production year-round. The aim of this study is to summarize cases of IoT being applied to agricultural automation in the agricultural sector and to discuss the limitations and prospects for expanding the application of IoT technology in Korea. The application of IoT in agriculture was classified and analyzed based on previous data, and the sensors and communication technologies used were compared. Based on the analysis results, the limitations of and prospects for IoT in agriculture were discussed. IoT was widely used in agriculture, such as management systems, monitoring systems, control systems, and unmanned machinery. In addition, the various wireless communication technologies used in agriculture, such as Wi-Fi, long-range wide area network (LoRaWAN), mobile communication (e.g., 2G, 3G, and 4G), ZigBee, and Bluetooth, were also used in IoT-based agriculture. With the development of various communication technologies, such as 5G, it is expected that faster and broader IoT technologies will be applied to various agricultural processes in the future. IoT-based agriculture equipped with a communication system suitable for each agricultural environment can contribute to agricultural automation by increasing crop quality and production and reducing labor.
Wan-Soo Kim; Won-Suk Lee; Yong-Joo Kim. A Review of the Applications of the Internet of Things (IoT) for Agricultural Automation. Journal of Biosystems Engineering 2020, 45, 385 -400.
AMA StyleWan-Soo Kim, Won-Suk Lee, Yong-Joo Kim. A Review of the Applications of the Internet of Things (IoT) for Agricultural Automation. Journal of Biosystems Engineering. 2020; 45 (4):385-400.
Chicago/Turabian StyleWan-Soo Kim; Won-Suk Lee; Yong-Joo Kim. 2020. "A Review of the Applications of the Internet of Things (IoT) for Agricultural Automation." Journal of Biosystems Engineering 45, no. 4: 385-400.
Machine-vision-based crop detection is a central issue for digital farming, and crop height is an important factor that should be automatically measured in robot-based cultivations. Three-dimensional (3D) imaging cameras make it possible to measure actual crop height; however, camera tilt due to irregular ground conditions in farmland prevents accurate height measurements. In this study, stereo-vision-based crop height was measured with compensation for the camera tilt effect. For implementing the tilt of the camera installed on farm machines (e.g., tractors), we developed a posture tilt simulator for indoor testing that could implement the camera tilt by pitch and roll rotations. Stereo images were captured under various simulator tilt conditions, and crop height was measured by detecting the crop region in a disparity map, which was generated by matching stereo images. The measured height was compensated for by correcting the position of the region of interest (RoI) in the 3D image through coordinate transformation between camera coordinates and simulator coordinates. The tests were conducted by roll and pitch rotation around the simulator coordinates. The results showed that crop height could be measured using stereo vision, and that tilt compensation reduced the average error from 15.6 to 3.9 cm. Thus, the crop height measurement system proposed in this study, based on 3D imaging and a tilt sensor, can contribute to the automatic perception of agricultural robots.
Wan-Soo Kim; Dae-Hyun Lee; Yong-Joo Kim; Yeon-Soo Kim; Taehyeong Kim; Seong-Un Park; Sung-Soo Kim; Dong-Hyuck Hong. Crop Height Measurement System Based on 3D Image and Tilt Sensor Fusion. Agronomy 2020, 10, 1670 .
AMA StyleWan-Soo Kim, Dae-Hyun Lee, Yong-Joo Kim, Yeon-Soo Kim, Taehyeong Kim, Seong-Un Park, Sung-Soo Kim, Dong-Hyuck Hong. Crop Height Measurement System Based on 3D Image and Tilt Sensor Fusion. Agronomy. 2020; 10 (11):1670.
Chicago/Turabian StyleWan-Soo Kim; Dae-Hyun Lee; Yong-Joo Kim; Yeon-Soo Kim; Taehyeong Kim; Seong-Un Park; Sung-Soo Kim; Dong-Hyuck Hong. 2020. "Crop Height Measurement System Based on 3D Image and Tilt Sensor Fusion." Agronomy 10, no. 11: 1670.
In order to optimize tractor design and optimize efficiency during tillage operation, it is essential to verify the impact through field tests on factors affecting the tractor load. The objectives of this study were to investigate the effect of tillage depth on power transmission efficiency of 42 kW power agricultural tractor during moldboard plowing. A load measurement system and a tillage depth measurement system were configured for field tests. To analyze the effect of tillage depth on power transmission efficiency and fuel consumption, the data measured in the three-repeated field test were classified according to tillage depth. As the tillage depth increased from 11 cm at the top of the hardpan to 23 cm at the deepest, the required power of the engine increased by approximately 13% from 35.48 kW to 40.11 kW, and the power transmission efficiency also increased significantly from 66% to 95%. Among them, the power transmission efficiency of the rear axle was significantly increased from 38% to 59%, which was the most affected. As the tillage depth increased, the overall power requirement is greatly increased due to the resulting workload, but the fuel consumption and the specific fuel consumption are reduced because the engine speed of the tractor is reduced. As the tillage depth increased from 11 cm to 23 cm, the fuel consumption rate was rather reduced by 13.5% as the engine rotational speed decreased 11.3% due to the increase work load of tractor. In addition, the specific fuel consumption decreased from 302.44 g/kWh to 236.93 g/kWh, showing a fuel consumption saving of up to 21.7% during moldboard plow. In addition, as the tillage depth increased, the ratio of the value excluding the mechanical and hydraulic power requirements has significantly decreased from 34% to 5% as the power transmission efficiency increases. This study considers the soil properties according to the soil depth, as well as the power transmission efficiency and fuel consumption rate. The research results can provide useful information for research on power transmission efficiency and selection of an appropriate power source of agricultural tractor according to tillage depth during moldboard plowing and are expected to be used in various ways as basic studies of digital farming research in agricultural machinery.
Yeon-Soo Kim; Wan-Soo Kim; Abu Ayub Siddique; Seung-Yun Baek; Seung-Min Baek; Su-Hwan Cheon; Sang-Dae Lee; Kyeong-Hwan Lee; Dong-Hyuck Hong; Seong-Un Park; Yong-Joo Kim. Power Transmission Efficiency Analysis of 42 kW Power Agricultural Tractor According to Tillage Depth During Moldboard Plowing. Agronomy 2020, 10, 1263 .
AMA StyleYeon-Soo Kim, Wan-Soo Kim, Abu Ayub Siddique, Seung-Yun Baek, Seung-Min Baek, Su-Hwan Cheon, Sang-Dae Lee, Kyeong-Hwan Lee, Dong-Hyuck Hong, Seong-Un Park, Yong-Joo Kim. Power Transmission Efficiency Analysis of 42 kW Power Agricultural Tractor According to Tillage Depth During Moldboard Plowing. Agronomy. 2020; 10 (9):1263.
Chicago/Turabian StyleYeon-Soo Kim; Wan-Soo Kim; Abu Ayub Siddique; Seung-Yun Baek; Seung-Min Baek; Su-Hwan Cheon; Sang-Dae Lee; Kyeong-Hwan Lee; Dong-Hyuck Hong; Seong-Un Park; Yong-Joo Kim. 2020. "Power Transmission Efficiency Analysis of 42 kW Power Agricultural Tractor According to Tillage Depth During Moldboard Plowing." Agronomy 10, no. 9: 1263.
This paper proposes a novel and efficient patch-based approach for autonomous path detection in semi-structured environments such as orchards. The proposed approach can segment a perspective path area in the frontal scene and is expected to be applicable to various types of area detection tasks in agriculture. In the proposed approach, a patch-based convolutional neural network (CNN) is used for image classification to achieve path area segmentation, which involves cropping image patches from an input image by using a sliding window, generating a path score map by using a four-layer CNN for tree and path classification, path area segmentation, and target path detection by using boundary line determination. Results show that the maximum intersection over union (IoU) is approximately 0.81 for path area localization and the average lateral and angular errors are 0.051 and 7.8°, respectively. The performance of patch-based path detection depends on the patch size of the sliding window. Hence, the path detection performance is evaluated in terms of the patch size, and a patch size of 96 × 96 shows the best performance, with a classification accuracy of 0.93, IoU of 0.75, and processing time of 0.111 s. In addition, the proposed approach was verified by applying the approach to various images including curved paths. The results indicate that the performance of the proposed patch-based approach for path detection is comparable to that of previous approaches. Moreover, an autonomous farm robot can be easily developed using the proposed technique with a simple hardware configuration.
Wan-Soo Kim; Dae-Hyun Lee; Yong-Joo Kim; Taehyeong Kim; Rok-Yeun Hwang; Hyo-Jai Lee. Path detection for autonomous traveling in orchards using patch-based CNN. Computers and Electronics in Agriculture 2020, 175, 105620 .
AMA StyleWan-Soo Kim, Dae-Hyun Lee, Yong-Joo Kim, Taehyeong Kim, Rok-Yeun Hwang, Hyo-Jai Lee. Path detection for autonomous traveling in orchards using patch-based CNN. Computers and Electronics in Agriculture. 2020; 175 ():105620.
Chicago/Turabian StyleWan-Soo Kim; Dae-Hyun Lee; Yong-Joo Kim; Taehyeong Kim; Rok-Yeun Hwang; Hyo-Jai Lee. 2020. "Path detection for autonomous traveling in orchards using patch-based CNN." Computers and Electronics in Agriculture 175, no. : 105620.
In general, the tractor axle torque is used as an indicator for making various decisions when engineers perform transmission fatigue life analysis, optimal design, and accelerated life testing. Since the existing axle torque measurement method requires an expensive torque sensor, an alternative method is required. Therefore, the aim of this study is to develop a prediction model for the tractor axle torque during tillage operation that can replace expensive axle torque sensors. A prediction model was proposed through regression analysis using key variables affecting the tractor axle torque. The engine torque, engine speed, tillage depth, slip ratio, and travel speed were selected as explanatory variables. In order to collect explanatory and dependent variable data, a load measurement system was developed, and a field experiment was performed on moldboard plow tillage using a tractor with a load measurement system. A total of eight axle torque prediction regression models were proposed using the measured calibration dataset. The adjusted coefficient of determination (R2) of the proposed regression model showed a range of 0.271 to 0.925. Among them, the prediction model E showed an adjusted R2 of 0.925. All of the prediction models were verified using a validation set. All of the axle torque prediction models showed an mean absolute percentage error (MAPE) of less than 2.8%. In particular, Model E, adopting engine torque, engine speed, and travel speed as variables, and Model H, adopting engine torque, tillage depth and travel speed as variables, showed MAPEs of 1.19 and 1.30%, respectively. Therefore, it was found that the proposed prediction models are applicable to actual axle torque prediction.
Wan-Soo Kim; Yong-Joo Kim; Seung-Yun Baek; Yeon-Soo Kim; Seong-Un Park. Development of a Prediction Model for Tractor Axle Torque during Tillage Operation. Applied Sciences 2020, 10, 4195 .
AMA StyleWan-Soo Kim, Yong-Joo Kim, Seung-Yun Baek, Yeon-Soo Kim, Seong-Un Park. Development of a Prediction Model for Tractor Axle Torque during Tillage Operation. Applied Sciences. 2020; 10 (12):4195.
Chicago/Turabian StyleWan-Soo Kim; Yong-Joo Kim; Seung-Yun Baek; Yeon-Soo Kim; Seong-Un Park. 2020. "Development of a Prediction Model for Tractor Axle Torque during Tillage Operation." Applied Sciences 10, no. 12: 4195.
This study was conducted to develop a simulation model of a 50 kW class hydro mechanical transmission (HMT) tractor and to verify the model by comparing the measured and simulated data, including the axle torque, rotational speed, and power transmission efficiency. The platform of the HMT was composed of the engine, hydrostatic unit (HSU), compound planetary gear, range shift, spiral bevel gear, and final reduction gear. The HMT had three gear stages and a maximum forward speed of 40 km/h. To evaluate the performance of the HTM, a test bench was installed based on the engine of the HMT platform, and a simulation model was developed using 3D simulation software. To compare the results of the simulation, a bench test using the platform was performed according to the gear stages. The similarities between the measured and simulated data were analyzed using the t-test. As a result, there were no significant differences for the axle torque, rotational speed, and power transmission efficiency. Finally, the power transmission efficiency between the measured and simulated results was compared and analyzed using linear regression analysis to validate the accuracy of the simulation model. The trend of the power transmission efficiency between the measured and simulated results appeared to be similar in all sections, and we obtained a simulation model with the accuracy of an R-squared value of more than 0.97. In conclusion, the measured and simulated results were similar to each other. Considering the results of this study, it will be useful to develop the HMT tractor and to improve the power transmission efficiency for the optimal design.
Seung-Min Baek; Wan-Soo Kim; Yeon-Soo Kim; Yong-Joo Kim. Development of a Simulation Model for HMT of a 50 kW Class Agricultural Tractor. Applied Sciences 2020, 10, 1 .
AMA StyleSeung-Min Baek, Wan-Soo Kim, Yeon-Soo Kim, Yong-Joo Kim. Development of a Simulation Model for HMT of a 50 kW Class Agricultural Tractor. Applied Sciences. 2020; 10 (12):1.
Chicago/Turabian StyleSeung-Min Baek; Wan-Soo Kim; Yeon-Soo Kim; Yong-Joo Kim. 2020. "Development of a Simulation Model for HMT of a 50 kW Class Agricultural Tractor." Applied Sciences 10, no. 12: 1.
This study was conducted to develop a simulation model of a 120 kW class electric all-wheel-drive (AWD) tractor and verify the model by comparing the measurement and simulation results. The platform was developed based on the power transmission system, including batteries, electric motors, reducers, wheels, and a charging system composed of a generator, an AC/DC converter, and chargers on each axle. The data measurement system was installed on the platform, consisting of an analog (current) and a digital part (rotational speed of electric motors and voltage and SOC (state of charge) level of batteries) by a CAN (controller area network) bus. The axle torque was calculated using the current and torque curves of the electric motor. The simulation model was developed by 1D simulation software and used axle torque and vehicle velocity data to create the simulation conditions. To compare the results of the simulation, a driving test using the platform was performed at a ground speed of 10 km/h in off- and on-road conditions. The similarities between the results were analyzed using statistical software and we found no significant difference in axle torque data. The simulation model was considered to be highly reliable given the change rate and average value of the SOC level. Using the simulation model, the workable time of driving operation was estimated to be about six hours and the workable time of plow tillage was estimated to be about 2.4 h. The results showed that the capacity of the battery is slightly low for plow tillage. However, in future studies, the electric AWD tractor performance could be improved through battery optimization through simulation under various conditions.
Seung-Yun Baek; Yeon-Soo Kim; Wan-Soo Kim; Yong-Joo Kim. Development and Verification of a Simulation Model for 120 kW Class Electric AWD (All-Wheel-Drive) Tractor during Driving Operation. Energies 2020, 13, 2422 .
AMA StyleSeung-Yun Baek, Yeon-Soo Kim, Wan-Soo Kim, Yong-Joo Kim. Development and Verification of a Simulation Model for 120 kW Class Electric AWD (All-Wheel-Drive) Tractor during Driving Operation. Energies. 2020; 13 (10):2422.
Chicago/Turabian StyleSeung-Yun Baek; Yeon-Soo Kim; Wan-Soo Kim; Yong-Joo Kim. 2020. "Development and Verification of a Simulation Model for 120 kW Class Electric AWD (All-Wheel-Drive) Tractor during Driving Operation." Energies 13, no. 10: 2422.
This study was conducted to analyze the effects of tillage depth and gear selection on the mechanical load and fuel efficiency of an agricultural tractor during plow tillage. In order to analyze these effects, we developed an agricultural field measuring system consisting of a load measurement part (wheel torque meter, proximity sensor, and real-time kinematic (RTK) global positioning system (GPS)) and a tillage depth measurement part (linear potentiometer and inclinometer). Field tests were carried out using moldboard plows with a maximum tillage depth of 20 cm and three gear selections (M2H, M3L, and M3H) in a rice stubble paddy field for plow tillage. The average travel speed and slip ratio had the lowest M2H and the highest M3L. M3H had the highest theoretical speed, but the travel speed was 0.13 km/h lower than M3L due to the reduction in the axle rotational speed at deep tillage depth. Regarding engine load, the higher the gear, the greater the torque and the lower the axle rotation speed. The front axle load was not significantly affected by the tillage depth as compared to other mechanical parts, except for the M3H gear. The rear axle load generated about twice the torque of the front wheel and overall, it tended to show a higher average rear axle torque at higher gear selection. The rear axle load and fuel rate were found to be most affected by the combination of the tillage depth and gear selection combination. Overall, field test results show that the M3H had the highest fuel efficiency and a high working speed while overcoming high loads at the same tillage depth. In conclusion, M3H is the most suitable gear stage for plow cultivation, and the higher the gear stage and the deeper the tillage depth during plowing, the higher the fuel efficiency. The results of this study will be useful for analyzing mechanical load and fuel efficiency during farm operations. In a future study, we will conduct load analysis studies in other farming operations that consider various soil mechanics factors as well as tillage depths and gear selections.
Yeon-Soo Kim; Wan-Soo Kim; Seung-Yun Baek; Young-Joo Kim; Sang-Dae Lee. Analysis of Tillage Depth and Gear Selection for Mechanical Load and Fuel Efficiency of an Agricultural Tractor Using an Agricultural Field Measuring System. Sensors 2020, 20, 2450 .
AMA StyleYeon-Soo Kim, Wan-Soo Kim, Seung-Yun Baek, Young-Joo Kim, Sang-Dae Lee. Analysis of Tillage Depth and Gear Selection for Mechanical Load and Fuel Efficiency of an Agricultural Tractor Using an Agricultural Field Measuring System. Sensors. 2020; 20 (9):2450.
Chicago/Turabian StyleYeon-Soo Kim; Wan-Soo Kim; Seung-Yun Baek; Young-Joo Kim; Sang-Dae Lee. 2020. "Analysis of Tillage Depth and Gear Selection for Mechanical Load and Fuel Efficiency of an Agricultural Tractor Using an Agricultural Field Measuring System." Sensors 20, no. 9: 2450.
This study aims to develop and evaluate an automated manual transmission (AMT) for agricultural tractors with high efficiency and high convenience by using electric actuators. An AMT system to control manual-type shuttle gearboxes and transmissions for tractors is developed by adding a shuttle shifting actuator, a clutch actuator, and a control system to a conventional manual transmission (MT). The clutch actuator is designed using an electric motor and a reduction gear. The AMT control system is developed and experimental tests are conducted to evaluate the performance of the AMT. The results of the performance of the actuator position control demonstrate that the shuttle shifting actuator and clutch actuator are controlled appropriately, achieving a maximum overshoot of less than 5% and 0%, a settling time of less than 0.500 s and 1.50 s, and a steady-state error of less than 1% and 1%, respectively. The performance of the automatic forward and reverse control demonstrates a shift control time of less than 2.50 s and target revolutions per minute (RPM) reaching time of less than 3.00 s. Thus, AMT systems for tractors can be easily developed by applying shuttle shifting actuators, clutch actuators, and a control system to conventional manual transmissions.
Wan-Soo Kim; Yong-Joo Kim; Yeon-Soo Kim; Seung-Yun Baek; Dae-Hyun Lee; Kyu-Chul Nam; Tae-Bum Kim; Hyo-Jai Lee. Development of Control System for Automated Manual Transmission of 45-kW Agricultural Tractor. Applied Sciences 2020, 10, 2930 .
AMA StyleWan-Soo Kim, Yong-Joo Kim, Yeon-Soo Kim, Seung-Yun Baek, Dae-Hyun Lee, Kyu-Chul Nam, Tae-Bum Kim, Hyo-Jai Lee. Development of Control System for Automated Manual Transmission of 45-kW Agricultural Tractor. Applied Sciences. 2020; 10 (8):2930.
Chicago/Turabian StyleWan-Soo Kim; Yong-Joo Kim; Yeon-Soo Kim; Seung-Yun Baek; Dae-Hyun Lee; Kyu-Chul Nam; Tae-Bum Kim; Hyo-Jai Lee. 2020. "Development of Control System for Automated Manual Transmission of 45-kW Agricultural Tractor." Applied Sciences 10, no. 8: 2930.
This study was conducted to develop a proportional-integral-derivative (PID) control algorithm considering viscosity for the planting depth control system of a rice transplanter using various hydraulic oils at different temperatures and to evaluate the performance of the control algorithm, and compare the performance of the PID control algorithm without considering viscosity and considering viscosity. In this study, the simulation model of the planting depth control system and a PID control algorithm were developed based on the power flow of the rice transplanter (ERP60DS). The primary PID coefficients were determined using the Ziegler-Nichols (Z-N) second method. Routh’s stability criteria were applied to optimize the coefficients. The pole and double zero points of the PID controller were also applied to minimize the sustained oscillations of the responses. The performance of the PID control algorithm was evaluated for three ISO (The International Organization for Standardization) standard viscosity grade (VG) hydraulic oils (VG 32, 46, and 68). The response characteristics were analyzed using statistical method (ANOVA) and Duncan’s multiple range test (DMRT) at a significant level of 0.05 were performed through the statistical software SPSS. The results show that the control algorithm considering viscosity is able to control the pressure of the proportional valve, which is associated with the actuator displacement for various types of hydraulic oils. It was noticed that the maximum pressure was 15.405 bars at 0, 20, 40, 60, 80, and 100 °C for all of the hydraulic oils. The settling time and steady-state errors were 0.45 s at 100 °C for VG 32 and 0% for all of the conditions. The maximum overshoots were found to be 17.50% at 100 °C for VG 32. On the other hand, the PID control algorithm without considering viscosity could not control the planting depth, because the response was slow and did not satisfy the boundary conditions. The PID control algorithm considering viscosity could sufficiently compensate for the nonlinearity of the hydraulic system and was able to perform for any of temperature-dependent viscosity of the hydraulic oils. In addition, the rice transplanter requires a faster response for accurately controlling and maintaining the planting depth. Planting depth is highly associated with actuator displacement. Finally, this control algorithm considering viscosity could be helpful in minimizing the tilting of the seedlings planted using the rice transplanter. Ultimately, it would improve the transplanter performance.
Abu Ayub Siddique; Wan-Soo Kim; Yeon-Soo Kim; Taek-Jin Kim; Chang-Hyun Choi; Hyo-Jai Lee; Sun-Ok Chung; Yong-Joo Kim. Effects of Temperatures and Viscosity of the Hydraulic Oils on the Proportional Valve for a Rice Transplanter Based on PID Control Algorithm. Agriculture 2020, 10, 73 .
AMA StyleAbu Ayub Siddique, Wan-Soo Kim, Yeon-Soo Kim, Taek-Jin Kim, Chang-Hyun Choi, Hyo-Jai Lee, Sun-Ok Chung, Yong-Joo Kim. Effects of Temperatures and Viscosity of the Hydraulic Oils on the Proportional Valve for a Rice Transplanter Based on PID Control Algorithm. Agriculture. 2020; 10 (3):73.
Chicago/Turabian StyleAbu Ayub Siddique; Wan-Soo Kim; Yeon-Soo Kim; Taek-Jin Kim; Chang-Hyun Choi; Hyo-Jai Lee; Sun-Ok Chung; Yong-Joo Kim. 2020. "Effects of Temperatures and Viscosity of the Hydraulic Oils on the Proportional Valve for a Rice Transplanter Based on PID Control Algorithm." Agriculture 10, no. 3: 73.
The objectives of this study were to develop a real-time tillage depth measurement system for agricultural tractor performance analysis and then to validate these configured systems through soil non-penetration tests and field experiment during plow tillage. The real-time tillage depth measurement system was developed by using a sensor fusion method, consisting of a linear potentiometer, inclinometer, and optical distance sensor to measure the vertical penetration depth of the attached implement. In addition, a draft force measurement system was developed using six-component load cells, and an accuracy of 98.9% was verified through a static load test. As a result of the soil non-penetration tests, it was confirmed that sensor fusion type A, consisting of a linear potentiometer and inclinometer, was 6.34–11.76% more accurate than sensor fusion type B, consisting of an optical distance sensor and inclinometer. Therefore, sensor fusion type A was used during field testing as it was found to be more suitable for use in severe working environments. To verify the accuracy of the real-time tillage depth measurement system, a linear regression analysis was performed between the measured draft and the predicted values calculated using the American Society of Agricultural and Biological Engineers (ASABE) standards-based equation. Experimental data such as traveling speed and draft force showed that it was significantly affected by tillage depth, and the coefficient of determination value at M3–Low was 0.847, which is relatively higher than M3–High. In addition, the regression analysis of the integrated data showed an R-square value of 0.715, which is an improvement compared to the accuracy of the ASABE standard prediction formula. In conclusion, the effect of tillage depth on draft force of agricultural tractors during plow tillage was analyzed by the simultaneous operation of the proposed real-time tillage depth measurement system and draft force measurement system. In addition, system accuracy is higher than the predicted accuracy of ± 40% based on the ASABE standard equation, which is considered to be useful for various agricultural machinery research fields. In future studies, real-time tillage depth measurement systems can be used in tractor power train design and to ensure component reliability, in accordance with agricultural working conditions, by predicting draft force and axle loads depending on the tillage depth during tillage operations.
Yeon-Soo Kim; Taek-Jin Kim; Yong-Joo Kim; Sang-Dae Lee; Seong-Un Park; Wan-Soo Kim. Development of a Real-Time Tillage Depth Measurement System for Agricultural Tractors: Application to the Effect Analysis of Tillage Depth on Draft Force during Plow Tillage. Sensors 2020, 20, 912 .
AMA StyleYeon-Soo Kim, Taek-Jin Kim, Yong-Joo Kim, Sang-Dae Lee, Seong-Un Park, Wan-Soo Kim. Development of a Real-Time Tillage Depth Measurement System for Agricultural Tractors: Application to the Effect Analysis of Tillage Depth on Draft Force during Plow Tillage. Sensors. 2020; 20 (3):912.
Chicago/Turabian StyleYeon-Soo Kim; Taek-Jin Kim; Yong-Joo Kim; Sang-Dae Lee; Seong-Un Park; Wan-Soo Kim. 2020. "Development of a Real-Time Tillage Depth Measurement System for Agricultural Tractors: Application to the Effect Analysis of Tillage Depth on Draft Force during Plow Tillage." Sensors 20, no. 3: 912.
This study was conducted to develop a PID control algorithm considering viscosity for the planting depth control system of a rice transplanter using various hydraulic oils at different temperatures and to evaluate the performance of the control algorithm, and compare the performance of the PID control algorithm without considering viscosity and considering viscosity. In this study, the simulation model of the planting depth control system and a PID control algorithm were developed based on the power flow of the rice transplanter (ERP60DS). The primary PID coefficients were determined using the Ziegler–Nichols (Z–N) second method. Routh’s stability criteria were applied to optimize the coefficients. The pole and double zero points of the PID controller were also applied to minimize the sustained oscillations of the responses. The performance of the PID control algorithm was evaluated for three ISO (The International Organization for Standardization) standard viscosity grade (VG) hydraulic oils (VG 32, 46, and 68). The results show that the control algorithm considering viscosity is able to control the pressure of the proportional valve, which is associated with the actuator displacement for various types of hydraulic oils. It was noticed that the maximum pressure was 15.405 bars at 0, 20, 40, 60, 80, and 100 ℃ for all of the hydraulic oils. The settling time and steady-state errors were 0.45 s at 100 ℃ for VG 32, and 0% for all of the conditions. The maximum overshoots were found to be 17.50% at 100 ℃ for VG 32. On the other hand, the PID control algorithm without considering viscosity could not control the planting depth, because the response was slow and did not satisfy the boundary conditions. The PID control algorithm considering viscosity could sufficiently compensate for the nonlinearity of the hydraulic system and was able to perform for any of temperature-dependent viscosity of the hydraulic oils. In addition, the rice transplanter requires a faster response for accurately controlling and maintaining the planting depth. Planting depth is highly associated with actuator displacement. Finally, this control algorithm considering viscosity could be helpful in minimizing the tilting of the seedlings planted using the rice transplanter. Ultimately, it would improve the transplanter performance.
Abu Ayub Siddique; Wan-Soo Kim; Yeon-Soo Kim; Taek-Jin Kim; Chang-Hyun Choi; Hyo-Jai Lee; Sun-Ok Chung; Yong-Joo Kim. PID Control Algorithm Based on Hydraulic Oil Viscosity for the Proportional Valve of the Planting Depth Control System. 2020, 1 .
AMA StyleAbu Ayub Siddique, Wan-Soo Kim, Yeon-Soo Kim, Taek-Jin Kim, Chang-Hyun Choi, Hyo-Jai Lee, Sun-Ok Chung, Yong-Joo Kim. PID Control Algorithm Based on Hydraulic Oil Viscosity for the Proportional Valve of the Planting Depth Control System. . 2020; ():1.
Chicago/Turabian StyleAbu Ayub Siddique; Wan-Soo Kim; Yeon-Soo Kim; Taek-Jin Kim; Chang-Hyun Choi; Hyo-Jai Lee; Sun-Ok Chung; Yong-Joo Kim. 2020. "PID Control Algorithm Based on Hydraulic Oil Viscosity for the Proportional Valve of the Planting Depth Control System." , no. : 1.
The aim of this study was to analyze the effects of the planting distance and depth on the power take-off (PTO) load spectrum of a small riding-type transplanter for the optimal design of the transplanter. To measure load data during actual planting operation, a load measurement system was developed using a torque sensor, a data acquisition system, and an inverter. Field experiments were conducted at four planting distances (26 cm, 35 cm, 43 cm, and 80 cm) and three planting depths (85 mm, 105 mm, and 136 mm) in a field with similar soil conditions. The measured load data were inverted into a load spectrum using rain-flow counting and Smith-Watson-Topper (SWT) methods. The safety factor of a transplanter according to the planting conditions was analyzed using the converted load spectrum and commercial software. The load spectrum for all planting conditions showed torque ratios similar within a high cycle region of 108 to 109. The torque ratio increased when the planting depth increased and planting distance decreased in the low cycle region under less than 108 cycles. The safety factors of the PTO driving gear and the driven gear increased as the planting distance increased at all planting depths. When the planting depth decreased at the same planting distance, the safety factor of the PTO gears increased. The results of this study might provide useful information for a transplanter PTO design considering the working load according to the various planting conditions. Keywords: transplanter, planting distance, planting depth, power take-off, load spectrum, safety factor DOI: 10.25165/j.ijabe.20201302.4187 Citation: Kim W-S, Kim Y-S, Kim T-J, Nam K-C, Kim T-B, Han T-H, et al. Effects of planting distance and depth on PTO load spectrum of a small riding-type transplanter. Int J Agric & Biol Eng, 2020; 13(2): 57–63.
Wan-Soo Kim; Yeon-Soo Kim; Taek-Jin Kim; Kyu-Chul Nam; Tae-Bum Kim; Tae-Ho Han; Ryu-Gap Im; Yong-Hyeon Kim. Effects of planting distance and depth on PTO load spectrum of a small riding-type transplanter. International Journal of Agricultural and Biological Engineering 2020, 13, 57 -63.
AMA StyleWan-Soo Kim, Yeon-Soo Kim, Taek-Jin Kim, Kyu-Chul Nam, Tae-Bum Kim, Tae-Ho Han, Ryu-Gap Im, Yong-Hyeon Kim. Effects of planting distance and depth on PTO load spectrum of a small riding-type transplanter. International Journal of Agricultural and Biological Engineering. 2020; 13 (2):57-63.
Chicago/Turabian StyleWan-Soo Kim; Yeon-Soo Kim; Taek-Jin Kim; Kyu-Chul Nam; Tae-Bum Kim; Tae-Ho Han; Ryu-Gap Im; Yong-Hyeon Kim. 2020. "Effects of planting distance and depth on PTO load spectrum of a small riding-type transplanter." International Journal of Agricultural and Biological Engineering 13, no. 2: 57-63.
본 연구는 트랙터의 동력전달시스템 최적 설계를 위한 기초연구로써 78kW급 농업용 트랙터의 소요 동력을 분석하기 위하여 수행되었다. 트랙터 소요 동력 계측시스템은 주요 동력 소모원인 차축, 동력 취출 장치, 주 유압펌프, 보조 유압펌프에 대하여 구축하였으며, 필드시험은 주요 농작업기 5종에 대하여 수행하였다. 몰드보드 플라우, 디스크 플라우, 로타리, 베일러, 로더 작업 시 총 소요 동력은 각각 53.69, 37.50, 65.61, 36.84, 17.55kW로 나타났다. 본 연구에서 수행한 농작업 중 로타리 경운 작업에서 엔진 정격출력의 약 84%의 동력 소모로 가장 높은 소요 동력을 나타냈으며, 로타리 경운 작업 외 주요 농작업별 평균 소요 동력은 엔진 정격출력의 70% 미만 수준으로 나타났다. 주요 동력 소모원별 소요동력은 차축, PTO 축, 주 유압펌프, 보조 유압펌프에서 각각 13.6, 31.5, 2.01, 1.84kW로 나타났으며, 주요 동력 소모원의 소요 합은 48.9kW로, 정격 출력의 63%를 사용한 것으로 나타났다.
Wansoo Kim; Yeon Soo Kim; Taek Jin Kim; Seong Un Park; Yong Choi; Il Su Choi; Young Keun Kim; Yong Joo Kim. Analysis of Power Requirement of 78 kW Class Agricultural Tractor According to the Major Field Operation. Transactions of the Korean Society of Mechanical Engineers - A 2019, 43, 911 -922.
AMA StyleWansoo Kim, Yeon Soo Kim, Taek Jin Kim, Seong Un Park, Yong Choi, Il Su Choi, Young Keun Kim, Yong Joo Kim. Analysis of Power Requirement of 78 kW Class Agricultural Tractor According to the Major Field Operation. Transactions of the Korean Society of Mechanical Engineers - A. 2019; 43 (12):911-922.
Chicago/Turabian StyleWansoo Kim; Yeon Soo Kim; Taek Jin Kim; Seong Un Park; Yong Choi; Il Su Choi; Young Keun Kim; Yong Joo Kim. 2019. "Analysis of Power Requirement of 78 kW Class Agricultural Tractor According to the Major Field Operation." Transactions of the Korean Society of Mechanical Engineers - A 43, no. 12: 911-922.
The effective crop management is major issue in recent agriculture because the cultivation area per farmer is increasing consistently while the aging-related reductions in the labor force. To manage crop cultivation effectively, it needs automatic monitoring in farmland. This paper presents an image-based field monitoring system for automatically crop monitoring and consists of constructing field monitoring system for periodic capturing of onion field images, training the deep neural network model for detecting the disease symptom, and evaluating performance of the developed system. The field monitoring system was composed of a PTZ camera, a motor system, wireless transceiver, and image logging module. The deep learning model was trained based on weakly supervised learning method that can classify and localize objects only with image-level annotation. It is effective to recognize crop disease symptom which has ambiguous boundary. The model was trained using captured onion images using the filed monitoring system, and 6 classes including the disease symptom were classified. The detected disease symptom was localized from background through thresholding of the class activation map. The 60% of maximum value in class activation map was determined as an Optimal threshold for disease symptom localization. Identification performance of disease symptom was evaluated using mAP metric by IoU. The results show that the mAP at IoU criteria 0.5, which should have over 50% overlap, was the highest in all models from 74.1 to 87.2. The results showed that the developed field monitoring system could automatically detect onion disease symptoms in real-time.
Wan-Soo Kim; Dae-Hyun Lee; Yong-Joo Kim. Machine vision-based automatic disease symptom detection of onion downy mildew. Computers and Electronics in Agriculture 2019, 168, 105099 .
AMA StyleWan-Soo Kim, Dae-Hyun Lee, Yong-Joo Kim. Machine vision-based automatic disease symptom detection of onion downy mildew. Computers and Electronics in Agriculture. 2019; 168 ():105099.
Chicago/Turabian StyleWan-Soo Kim; Dae-Hyun Lee; Yong-Joo Kim. 2019. "Machine vision-based automatic disease symptom detection of onion downy mildew." Computers and Electronics in Agriculture 168, no. : 105099.