• Journal of the Korean Society for Precision Engineering
• /
• v.31 no.1
• /
• pp.43-50
• /
• 2014

This paper introduces a robot platform which can do weeding while traveling between rice seedlings stably against irregular land surface of a paddy field. Also, an autonomous navigation technique that can track on stable state without any damage of the seedlings in the working area is proposed. Detection of the rice seedlings and avoidance knocking down by the robot platform is achieved by the sensor fusion of a laser range finder (LRF) and an inertial measurement unit (IMU). These sensors are also used to control navigating direction of the robot to keep going along the column of rice seedling consistently. Deviation of the robot direction from the rice column that is sensed by the LRF is fed back to a proportional and derivative controller to obtain stable adjustment of navigating direction and get proper returning speed of the robot to the rice column.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2013.05a
• /
• pp.441-442
• /
• 2013

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2013.05a
• /
• pp.1557-1558
• /
• 2013

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

• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.349-351
• /
• 2007

A concept of the semi-autonomous tele-weeding-system which performs weeding tasks through co-operation of human and machine intelligence is proposed. The tele-weeding system consists of weeding robot, communication networks and operating server. The images of plants taken by the weeding robot are transferred through the communication networks to the human operator. Positions of the weeds are indicated at the operating host by the operator and transferred back to the weeding robot. Such position informations are converted to the world space and the weeding is done based on the robot intelligence. Feasibility of such concept has been tested through development of a laboratory model of the system.

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

• Korean Journal of Weed Science
• /
• v.31 no.1
• /
• pp.8-23
• /
• 2011

Weeds are one of the major constraints to crop production in organic farming systems. This paper reviews major results and techniques achieved with physical, cultural, and biological weed control and their perspectives in organic agriculture. Physical methods includes mechanical, thermal, lighting, electrocution, pneumatic, autonomous robot weeding control techniques. Cultural weed control methods includes mulching, tillage, crop rotation, cover crops and crop competition. Physical and cultural weed control techniques are especially important in organic farming crops where other weed control options are limited or not available without use of herbicides. Biological weed control includes mycoherbicides, innundative biological control, broad-spectrum biological control and allelopathy. Successful weed management in organic farming requires well managed integrated systems of mechanical control using newly developed machines, cultural control and biological control methods. Weed management decision-aid models may also needed to develop to provide greater assurance of achieving profitability and appropriate long-term weed management in organic farming in the future.

• Journal of Biosystems Engineering
• /
• v.28 no.6
• /
• pp.517-524
• /
• 2003

There have been worldwide research and development efforts to automate various processes of bio-production and those efforts will be expanded with priority given to tasks which require high intensive labor or produce high value-added product and tasks under hostile environment. In the field of bio-production capabilities of the versatility and robustness of automated system have been major bottlenecks along with economical efficiency. This paper introduces a new concept of automation based on tole-operation, which can provide solutions to overcome inherent difficulties in automating bio-production processes. Operator(farmer), computer, and automatic machinery share their roles utilizing their maximum merits to accomplish given tasks successfully. Among processes of greenhouse watermelon cultivation tasks such as pruning, watering, pesticide application, and harvest with loading were chosen based on the required labor intensiveness and functional similarities to realize the proposed concept. The developed system was composed of 5 major hardware modules such as wireless remote monitoring and task control module, wireless remote image acquisition and data transmission module, gantry system equipped with 4 d.o.f. Cartesian type robotic manipulator, exchangeable modular type end-effectors, and guided watermelon loading and storage module. The system was operated through the graphic user interface using touch screen monitor and wireless data communication among operator, computer, and machine. The proposed system showed practical and feasible way of automation in the field of volatile bio-production process.