• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.24-24
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• 2023

현대화된 재배법은 작물의 생육을 위해 시설내부의 환경을 제어하고 실시간 센싱 정보를 저장하는 시스템을 구축하고 이를 활용하고 있으나, 작물의 생육·생장에 미치는 직접적인 영향에 대한 생육데이터 취득은 아직까지도 전문 재배사·농민이 수작업을 통해 조사되고 있다. 본 연구는 작물의 생육데이터 자동 취득을 위한 장치를 개발하고 이를 실용화하기 위한 정확도 측정 시험을 진행하였다. 실험을 위한 장치구성은 3D Depth 카메라(Intel D415)와 운용 PC이며 딥러닝 모델을 이용하여 작물의 세부기관을 자동으로 인식하는 모델을 포함한다. 장치는 다양한 재배환경의 작물 생육데이터 취득을 위하여 휴대용, 고정형, 로봇형 3가지 유형으로 개발하였고 측정 정확도 검증은 휴대용 생육측정장치를 활용하여 조사하였다. 이러한 연구를 통해 수작업이 아닌 영상에 의한 생육 데이터수집으로 작물의 생육정보(측정값+이미지)를 확보함으로써 환경데이터와 함께 객관적인 정보에 의한 작물의 생산량, 수확시기 등을 예측하는데 활용될 수 있을것으로 예상된다.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.143-143
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• 2017

산업이 고도화됨에 따라 자동화 기계 및 로봇에 의해 대량 생산 되는 품목과 달리, 작업 절차의 비정형성, 비연속성 등으로 인해 여전히 농업에 많은 인력이 투입되고 있다. 국제노동기구에 따르면, 세계 인력의 절반이 농업 인력에 해당하고 작업 중 부상이나 사망 등으로 인해 가장 위험한 직업군 중 하나에 해당하는 것으로 나타났다. 시설 재배 농업의 경우, 노동집약적인 온실 내 작업 특성상 잘못된 자세로 작업하거나 지나친 작업량 등으로 인해 작업자에게 근골격계 질환이 발생할 수 있다. 근골격계 질환으로 인해 작업효율이 감소하거나 생산비용의 증가로 이어질 수 있으며, 농가 수익에 손실이 발생할 수 있다. 이에 본 연구에서는 현행 시설 재배 농업에서 사용되는 레일 전동 작업차를 이용하여 작업자가 토마토를 수확할 때의 신체에 대한 농작업의 부하를 평가하고자 하였다. 작업차를 이용한 주요 작업 절차는 작물로부터 과실 수확, 과실 상자에 과실 투입, 빈 과실 상자와 가득 찬 과실 상자의 교대, 작업차 위의 과실 상자를 운반용 파레트에 하역하는 순서로 이루어지는 것을 확인하였다. 비디오장비로 촬영된 일련의 농작업 과정을 OWAS, RULA, REBA와 같은 체크리스트형 인간공학적 작업 부하 평가 도구를 이용하여 평가한 결과, 기존 레일 전동 작업차를 이용한 농작업의 근골격계 질환 유발 가능성을 확인하였다. 동작별 위험성을 토대로 근골격계 질환 유발 가능성이 높아 개선이 필요한 농작업 동작을 선정하였다. 선정된 동작은 실험실 내 환경에서 피실험자를 통한 모의 동작의 생체 신호 계측을 통해 신체 부하 정도를 정량적으로 측정할 수 있으며, 보조가 필요한 신체 부위를 특정하거나 안전성 확보가 필요한 동작에 대한 증거가 될 수 있다. 본 연구를 통해 향후 토마토 온실 내 신선도 유지를 위한 레일 전동 작업차의 개발에 작업자의 안전과 효율성 향상을 위한 인간공학적 설계를 적용할 수 있을 것으로 기대한다.

• Journal of Bio-Environment Control
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• v.10 no.3
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• pp.172-180
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• 2001

A grape fruit is required for a lot of labor to harvest in time in Korea, since the fruit is cut and grabbed currently by hand. In foreign country, especially France, a grape harvester has been developed for processing to make wine out of a grape, not to eat a fresh grape fruit. However, a harvester which harvests to eat a fresh grape fruit has not been developed yet. Therefore, this study was designed and constructed to develope a image processing system for a fresh grape harvester. Its development involved the integration of a vision system along with an personal computer and two cameras. Grape recognition, which was able to found the accurate cutting position in three dimension by the end-effector, needed to find out the object from the background by using two different images from two cameras. Based on the results of this research the following conclusions were made: The model grape was located and measured within less than 1,100 mm from camera center, which means center between two cameras. The distance error of the calculated distance had the distance error within 5mm by using model image in the laboratory. The image processing system proved to be a reliable system for measuring the accurate distance between the camera center and the grape fruit. Also, difference between actual distance and calculated distance was found within 5 mm using stereo vision system in the field. Therefore, the image processing system would be mounted on a grape harvester to be founded to the position of the a grape fruit.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.115-123
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• 2021

Pine nuts are Korea's representative nut forest products and profitable crops. However, pine nuts are harvested by climbing the trees themselves, thus the risk is high. In order to solve this problem, it is necessary to harvest pine nuts using a robot or an unmanned aerial vehicle(UAV). In this paper, we propose a deep learning based detection method for harvesting pine nut in UAV aerial images. For this, a video was recorded in a real pine forest using UAV, and a data augmentation technique was used to supplement a small number of data. As the data for 3D detection, Unity3D was used to model the virtual pine nut and the virtual environment, and the labeling was acquired using the 3D transformation method of the coordinate system. Deep learning algorithms for detection of pine nuts distribution area and 2D and 3D detection of pine nuts objects were used DeepLabV3+, YOLOv4, and CenterNet, respectively. As a result of the experiment, the detection rate of pine nuts distribution area was 82.15%, the 2D detection rate was 86.93%, and the 3D detection rate was 59.45%.

• Composites Research
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• v.35 no.6
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• pp.371-377
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• 2022

Triboelectric nanogenerator (TENG) devices have generated a lot of interest in recent decades. TENG technology, which is one of the technologies for harvesting mechanical energy among the energy wasted in the environment, is obtained by the dual effect of electrostatic induction and triboelectric charging. Recently, a multilayer thin film stacking method (or layer-by-layer (LbL) self-assembly technique) is being considered as a method to improve the performance of TENG and apply it to new fields. This LbL assembly technology can not only improve the performance of TENG and successfully overcome the thickness problem in applications, but also present an inexpensive, environmentally friendly process and be used for large-scale and mass production. In this review, recent studies in the accomplishment of LbL-based materials for TENG devices are reviewed, and the potential for energy harvesting devices reviewed so far is checked. The advantages of the TENG device fabricated by applying the LbL technology are discussed, and finally, the direction and perspective of this fabrication technology for the implementation of various ultra-thin TENGs are briefly presented.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.43-53
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• 2024

Tomatoes are rich in nutrients like lycopene, β-carotene, and vitamin C. However, they often suffer from biological and environmental stressors, resulting in significant yield losses. Traditional manual plant health assessments are error-prone and inefficient for large-scale production. To address this need, we collected a comprehensive dataset covering the entire life span of tomato plants, annotated across 5 health states from 1 to 5. Our study introduces an Attention-Enhanced DS-ResNet architecture with Channel-wise attention and Grouped convolution, refined with new training techniques. Our model achieved an overall accuracy of 80.2% using 5-fold cross-validation, showcasing its robustness in precisely classifying the health states of tomato plants.

• Journal of Biosystems Engineering
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• v.26 no.6
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• pp.535-544
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• 2001

This study developed a manipulator for robotic harvester to harvest cucumber. The manipulator was designed and built fur transferring an end-effecter from a fixed point to a specified cucumber. Its development involved the integration of a manipulating system with a PC compatible, DC motors, geared boxes, timing belts, and a motor controller board. Software, written in Quick basic. combined the functions of motor control with various circumstances. In order to move smoothly and rapidly the manipulator, it's shoulder link and elbow link were minimized by using rotational inertial moment without a motor and a geared box. After 30 replications of exercising the manipulator, it was concluded that the precision values of the X, Y and Z axes were less than 0.5mm, 7.25mm and 0.35mm, respectively. The precision data indicated the manipulator was not missing any steps fur the harvester to reach a target cucumber.

• Journal of Biosystems Engineering
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• v.29 no.3
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• pp.281-286
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• 2004

The end-effector is the one of the important factors on development of the cucumber robot to harvester a cucumber. Three end-effectors were designed the single blade end-effector with one blade, the double blade end-effector with two blades and the triple blade end-effector with three blades. Performance tests of the end-effector, the fully integrated system, were conducted to determine the cutting rate by using two different kinds of cucumber. The success rates of cucumber cutting ratio of single end-effector, double end-effector and triple end-effector in laboratory. were 61.7%, 95%, 86.7%, respectively. The cutting rate of single blade or double blade was a little difference with respect to the different diameters of cucumber stem. However, the success cutting rate of the end-effector with triple blade was 61.7% under 29mm diameter of a grabbing stem section. The triple end-effector was not suitable for harvesting a cucumber, but was considered to be suitable for harvesting a grape, an apple and a tomato. The success rate of cucumber cutting ratio of triple end-effectors in greenhouse was 84%. The failure cutting rate was 16% which are due to abnormal shape of cucumber fruit.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.2
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• pp.99-111
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• 2021

The connected farm that agricultural land, agricultural machinery and farmer are connected with an IoT gateway is in the commercialization stage. That has increased productivity, efficiency and profitability by intimate information exchange among those. In order to develop the educational program of intelligent agricultural machinery and the agricultural machinery safety education performance indicator, this study analyzed patent trends of agricultural machine with unmanned technology used in agriculture and efficiency technology applied advanced technologies such as ICT, robots and artificial intelligence. We investigated and analyzed patent trends in agricultural machinery of Korea, the USA and Japan as well as the countries in Europe. The United States is an advanced country in the field of unmanned technology and efficiency technology used in agriculture. Agricultural automation technology in Korea is insufficient compared to developed countries, which means rapid technological development is needed. In the sub-fields of field automation technology, path generation and following technology and working machine control technology through environmental awareness have activated.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.392-399
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• 2023

This paper proposes a deep learning model to determine the region and depth of cabbage cores for robotic automation of the cabbage core removal process during the kimchi manufacturing process. In addition, rather than predicting the depth of the measured cabbage, a model was presented that simultaneously detects and classifies the area by converting it into a discrete class. For deep learning model learning and verification, RGB images of the harvested cabbage 522 were obtained. The core region and depth labeling and data augmentation techniques from the acquired images was processed. MAP, IoU, acuity, sensitivity, specificity, and F1-score were selected to evaluate the performance of the proposed YOLO-v4 deep learning model-based cabbage core area detection and classification model. As a result, the mAP and IoU values were 0.97 and 0.91, respectively, and the acuity and F1-score values were 96.2% and 95.5% for depth classification, respectively. Through the results of this study, it was confirmed that the depth information of cabbage can be classified, and that it can be used in the development of a robot-automation system for the cabbage core removal process in the future.