• Journal of Drive and Control
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• v.19 no.1
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• pp.16-25
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• 2022

The purpose of this study was to calculate and analyze the engine load factor of major agricultural operations using a 78 kW class agricultural tractor for estimating the emission of air pollutants and greenhouse. Engine load data were collected using controller area network (CAN) communication. Main agricultural operations were selected as plow tillage (PT), rotary tillage (RT), baler operation (BO), loader operation (LO), driving on soil (DS), and driving on concrete (DC). The engine power was calculated using the measured engine load data. A weight factor was applied to load factor for considering usage ratio according to agricultural operations. Weight factors for different agricultural operations were calculated to be 27.4%, 32.9%, 17.5%, 7.7%, 4.5%, and 10.0% for PT, RT, BO, LO, DS, and DC, respectively. As a result of the field test, load factors were 0.74, 0.93, 0.41, 0.23, 0.27, and 0.21 for PT, RT, BO, LO, DS, and DC, respectively. The engine load factor was the highest for RT. Finally, as a result of applying the weight factor for usage ratio of agricultural operations, the integrated engine load factor was estimated to be 0.63, which was about 1.31 times higher than the conventional applied load factor of 0.48. In future studies, we plan to analyze the engine load factor by considering various horsepower and working conditions of the tractor.

• Journal of Drive and Control
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• v.20 no.1
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• pp.16-26
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• 2023

Although the upland field area of Korea is high as 44.8%, the platform optimized for the upland field is insufficient. It is necessary to develop an optimized platform for the upland field because the upland field environment is an irregular environment with many slopes. In addition, due to the characteristic of agricultural operations, the traction power and torque of the platform have to be sufficient. Therefore, in this study, a simulation model that can predict the traction power and driving torque of a crawler-type platform for the upland field was developed and validated using the specifications of the crawler platform. The simulation model was developed using Amesim (19.1, Siemens, Germany). The development of the model was conducted using the specifications of the platform. A measurement system was developed to validate the simulation model. The traction power data of the simulation model was validated with the traction force and vehicle speed. The driving torque data of the simulation model was validated with the torque of the sprocket on the crawler system. As a result of the analysis, the error between measurement and simulation results occurred within 10%, and it was determined that the traction power and driving torque prediction of the crawler platform using this model was possible.

• Journal of Drive and Control
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• v.20 no.3
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• pp.15-24
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• 2023

The aim of this study was to measure and evaluate the exhaust emission factors of agricultural tractors. Engine characteristics and three exhaust emissions (CO, NO_x, PM) were collected under actual agricultural operating conditions. Experiments were performed on idling, driving, plow tillage, and rotary tillage. The load factor (LF) was calculated using the collected engine data, and the emission factor was analyzed using the LF and exhaust emissions. The engine characteristics and exhaust emissions were significantly different for each working condition, and in particular, the LF was significantly different from the currently applied 0.48 LF. The data distribution of exhaust emissions was different depending on the engine speed. In some conditions, the emission factor was higher than the exhaust emission standards. However, since most emission limit standards are values calculated using an engine dynamometer, even if the emission factor measured under actual working conditions is higher, it cannot be regarded as wrong. It is expected that the results of this study can be used for the inventory construction of a calculation for domestic agricultural machinery emissions in the future.

• Journal of Drive and Control
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• v.20 no.3
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• pp.35-41
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• 2023

Radish and Chinese cabbage are the most produced and consumed vegetables in Korea. The mechanization of harvesting operations is necessary to minimize the need for manual labor. This study to develop and evaluate the performance of a multi-purpose driving platform that can apply modular Radish and Chinese cabbage harvesting devices. The multi-purpose driving platform consisted of driving, device control, engine, hydraulic, harvesting, conveying, and loading part. Radish and Chinese cabbage harvesting conducted using the multi-purpose driving platform each harvesting module. The performance of the multi-purpose driving platform was evaluated the field efficiency and loss rate. The total Radish harvesting operation time 34.3 min., including 28.8 min., of harvesting time, 1.9 min., of turning time, and 3.6 min., of replacement time of bulk bag. During Radish harvesting, the field efficiency and average loss rate of the multi-purpose driving platform were 2.0 hr/10a and 3.1 %. Chinese cabbage harvesting operation 49.3 min., including 26.6 min., of harvesting time, 4.6 min., of turning time, and 18.1 min., of replacement time of bulk bag. During Chinese cabbage harvesting, the field efficiency and average loss rate of the multi-purpose driving platform 2.1 hr/10a and 0.1 %. Performance evaluation of the multi-purpose driving platform that harvesting work was possible by installing Radish and Chinese cabbage harvest modules. Performance analysis through harvest performance evaluation in various Radish and Chinese cabbage cultivation environments is necessary.

• Journal of Drive and Control
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• v.20 no.2
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• pp.31-39
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• 2023

The aim of this study was to measure and analyze engine load factor (LF) according to working conditions (operation type and gear stage) of small agricultural multi-purpose cultivator to estimate the emission of air pollutants. To calculate LF, a torque sensor capable of collecting torque and rotational speed was installed on the engine output shaft and DAQ was used to collect data. A field test was conducted with major operation of a cultivator and tillage operations (plow tillage and rotary tillage). Engine power was calculated using engine torque and rotational speed and LF was calculated using real-time power and rated power. In addition, unified LF was calculated using the weight for each operation and the average LF for each operation. As a result, average LF values at 1.87 and 3.10 km/h by plow tillage were 0.50 and 0.69, respectively. Average LF values at 1.87 and 3.10 km/h by rotary tillage were 0.70 and 0.78, respectively. Furthermore, unified LF calculated in consideration of the weight factor showed a value of 0.65, which was 135% higher than the conventional LF (0.48). Results of this study could be used as basic information for realizing LF values in the field of agricultural machinery.

• Journal of Drive and Control
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• v.20 no.2
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• pp.40-46
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• 2023

Fresh corn, one of the main food crops, must be harvested by hand. A harvest mechanization technology is required. In this study, a tractor-attached harvester was designed and manufactured to sequentially perform stem reaping, fresh corn detaching, and collecting. The(harvester was designed so that the main device could operate through a hydraulic pump and a generator could be operated through the tractor's PTO. Factor tests were conducted according to cultivars (Ilmichal, Super sweet corn) and working speed (0.12 m/s, 0.17, 0.22). After the factor test, detached corns ratio, collected corns ratio, and damaged corns ratio were analyzed and harvest performance was evaluated. Harvesting performance was good for super sweet corn. Considering operation efficiency, 0.22 m/s was judged to be an appropriate working speed. It was found that it took two hours to work an area of 10 a.

• Journal of Drive and Control
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• v.20 no.2
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• pp.15-23
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• 2023

In this study, two-dimensional location of crops for auto weeding was detected using deep learning. To construct a dataset for soybean detection, an image-capturing system was developed using a mono camera and single-board computer and the system was mounted on a weeding robot to collect soybean images. A dataset was constructed by extracting RoI (region of interest) from the raw image and each sample was labeled with soybean and the background for classification learning. The deep learning model consisted of four convolutional layers and was trained with a weakly supervised learning method that can provide object localization only using image-level labeling. Localization of the soybean area can be visualized via CAM and the two-dimensional position of the soybean was estimated by clustering the pixels associated with the soybean area and transforming the pixel coordinates to world coordinates. The actual position, which is determined manually as pixel coordinates in the image was evaluated and performances were 6.6(X-axis), 5.1(Y-axis) and 1.2(X-axis), 2.2(Y-axis) for MSE and RMSE about world coordinates, respectively. From the results, we confirmed that the center position of the soybean area derived through deep learning was sufficient for use in automatic weeding systems.

• Journal of Drive and Control
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• v.20 no.4
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• pp.64-70
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• 2023

Garlic is classified as one of the three essential seasoning vegetables in Korea. In 2023, it was reported that the area under garlic cultivation was 24,700 ha, and the production stood at 318,220 tons. Garlic harvesting mechanization currently stands at 43.8%, and garlic is still collected manually after digging out using diggers, so the process is labor intensive. To reduce garlic production costs and enhance competitiveness, it is necessary to develop a high-performance gathering type harvester in place of the digging type harvester. Therefore, in this study, a gathering-type garlic harvester that can dig and collect simultaneously was designed and manufactured, and the harvest performance by factor was analyzed through a harvest performance test. As a result of the performance test, it was analyzed to perform optimally at a driving speed of 0.11m/s and a transfer speed of 85rpm. Work performance was calculated using the results obtained from the factor performance test.

• Journal of Drive and Control
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• v.20 no.4
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• pp.71-79
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• 2023

In the field of agricultural machinery, various on-field tests are conducted to measure design load for optimal design of agricultural equipment. However, field test procedures are costly and time-consuming, and there are many constraints on field soil conditions due to weather, so research on utilizing simulation to overcome these shortcomings is needed. Therefore, this study aimed to model agricultural soils using discrete element method (DEM) software. To simulate draft force, predictions are made according to travel speed and compared to field test results to validate the prediction accuracy. The measured soil properties are used for DEM modeling. In this study, the soil property measurement procedure was designed to measure the physical and mechanical properties. DEM soil model calibration was performed using a virtual vane shear test instead of the repose angle test. The DEM simulation results showed that the prediction accuracy of the draft force was within 4.8% (2.16~6.71%) when compared to the draft force measured by the field test. In addition, it was confirmed that the result was up to 72.51% more accurate than those obtained through theoretical methods for predicting draft force. This study provides useful information for the DEM soil modeling process that considers the working speed from the perspective of agricultural machinery research and it is expected to be utilized in agricultural machinery design research.

• Journal of Drive and Control
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• v.20 no.4
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• pp.27-34
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• 2023

Weeding in orchards is closely associated with productivity and quality. The customary weeding process is both labor-intensive and time-consuming. To solve the problems, there is need for automation of agricultural robots and machines in the agricultural field. On the other hand, orchards have complicated working areas due to narrow spaces between trees and amorphous terrain. Therefore, it is necessary to develop customized robot technology for unmanned weeding work within the department. This study developed a path generation and path control method for unmanned weeding according to the orchard environment. For this, the width of the weeding span, the number of operations, and the width of the weeding robot were used as input parameters for the orchard environment parameters. To generate a weeding path, a weeding robot was operated remotely to obtain GNSS-based location data along the superheated center line, and a driving performance test was performed based on the generated path. From the results of orchard field tests, the RMSE in weeding period sections was measured at 0.029 m, with a maximum error of 0.15 m. In the steering period within row and steering to the next row sections, the RMSE was 0.124 m, and 0.047 m, respectively.