• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.53-53
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• 2017

연구목적: 국내 과일 산업의 한 단계 도약과 대외 경쟁력을 높이기 위해서는 수확작업의 기계화가 시급함. 이 연구는 국내외 과일 수확 기계화 현황과 문제점을 분석하고, 향후 수확작업 기계화 방향을 제시하고자 수행 수확기계 실용화 현황 및 고찰 (국내) 과일을 직접 수확이 아닌 수확 작업을 보조해주는 고지 작업기(수동형, 모터 진동형)와 고소 작업차가 대부분임. 수동형은 사과 감 등을 수확하지만, 작업능률이 낮고 작업자가 쉽게 피로하여 장시간 작업이 불가능하므로 실질적인 대안이 되지 못함. 진동형은 자체 동력을 이용하여 나무에 진동을 가하여 주로 대추 매실 등 소과류 수확에 이용되고, 수확능률은 우수하나 충격 손상이 많아 개선이 요구됨. 고소작업차는 동력원에 따라 충전식과 엔진식으로 구분되고, 충전식은 엔진식에 비해 진동 소음이 적어 쾌적하지만, 작업시간이 배터리 용량에 제한을 받음. 또한 작업대 작동방식에 따라 리프트형과 붐형으로 구분함. 리프트형은 리프트를 이용하여 작업대를 상하로 구동하는 방식으로 높은 위치의 과실 수확이 어렵고, 작업대 넓이 만큼의 작업 공간(과수간의 거리)이 필요함. 붐형은 필요한 곳으로 접근성이 우수하나 무거운 무게를 지탱하기 어렵기 때문에 본체를 무겁게 하거나 수시로 수확된 과일을 하차시켜야 함. (국외) 수확 후 가공되는 과일류와 포도 올리브 오렌지 매실 등 소과류 수확이 기계화되었지만, 사과 복숭아 등 신선과일은 아직도 외국의 값싼 노동자들에 의존하여 수확되고 있음. 현재 실용화된 수확 기계는 진동식 수확기계와 터널식 수확기계가 대표적임. 진동식은 집게형의 부착기를 나무 줄기에 고정한 후 트랙터 동력원으로 나무에 진동을 가하여 수확하고, 올리브 대추 등과 같은 소과류와 과피가 두꺼운 오렌지 등에 적용되고, 수확 작업능률이 매우 높으나 과일의 낙하 상처를 피할 수 없는 단점이 있음. 터널형은 규격화(과수 크기 및 형태, 재식거리)된 과수원에 잘 적응하도록 설계 제작되어, 과수 위를 지나가면서 내부에 설치된 진동장치와 컨테이너로 과일을 수확하고, 와인용 포도 수확기가 대표적임. 기계수확이 가능하도록 과수원 조성단계에서부터 재배양식(과수 좌우 및 전후 거리)을 기계의 제원(바퀴 간격, 작업부 간격 등)에 맞추어 재배함. 과일 수확로봇에 관한 연구는 활발하고 일부에서 실증시험단계에 있음. 결론: 구체적인 추진방향을 제시하면, 단기적으로는 과일 수확작업자의 작업편이성과 노동강도를 줄일 수 있도록 소형 저가 범용성이 우수한 보조기구/기계의 보급을 확대하고, 중장기적으로는 수확기계/수확로봇 개발을 위한 연구개발비 투자를 늘리는 동시에, 기계/로봇이 과수원에 잘 적응할 수 있도록 수형 재식거리 등 재배양식의 표준화가 추진되어야 함.

• Smart Media Journal
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• v.12 no.10
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• pp.19-28
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• 2023

In the fruit harvesting field, interest in automatic robot harvesting is increasing due to various seasonality and rising harvesting costs. Accurate apple detection is a difficult problem in complex orchard environments with changes in light, vibrations caused by wind, and occlusion of leaves and branches. In this paper, we introduce a dataset and an adaptive heatmap regression model that are advantageous for robot automatic apple harvesting. The apple dataset was labeled with not only the apple location but also the visibility. We propose a method to detect the center point of an apple using an adaptive heatmap regression model that adjusts the Gaussian shape according to visibility. The experimental results showed that the performance of the proposed method was applicable to apple harvesting robots, with MAP@K of 0.9809 and 0.9801 when K=5 and K=10, respectively.

• Journal of Drive and Control
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• v.21 no.2
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• pp.65-71
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• 2024

In this study, we suggested a key-point detection method for robot harvesting of oriental melon. Our suggested method could be used to detect the detachment part and major composition of oriental melon. We defined four points (harvesting point, calyx, center, bottom) based on tomato with characteristics similar to those of oriental melon. The evaluation of estimated key-points was conducted by pixel error and PDK (percentage of detected key-point) index. Results showed that the average pixel error was 18.26 ± 16.62 for the x coordinate and 17.74 ± 18.07 for the y coordinate. Considering the resolution of raw images, these pixel errors were not expected to have a serious impact. The PDK score was found to be 89.5% PDK@0.5 on average. It was possible to estimate oriental melon specific key-point. As a result of this research, we believe that the proposed method can contribute to the application of harvesting robot system.

• Journal of Bio-Environment Control
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• v.12 no.2
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• pp.57-62
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• 2003

In this study, a robotic manipulator for harvesting cucumber was developed. The objective of this research was to design and to construct a robotic manipulator specifically tailored to harvest cucumber in the greenhouse. The system was consisted of an integrated end-efffctor, an image processing system and a controlling system. Especially, the image processing system detected the quality of cucumber within each plant in order for the computer to furnish harvest instructions to the manipulator. In all tests of cucumber, the success rate for cucumber harvest was 84％ in the greenhouse. End-effector, image processing system and controlling system showed good performance. Based on the results of this research the following recommendations are made for further study. Besides harvesting cucumbers, the oldest leaves, creepers and the youngest small side leaves need to be removed.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.270-278
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• 2022

This study was conducted using deep learning technology to classify for 'Mihwang' peach maturity with RGB images and fruit quality attributes during fruit development and maturation periods. The 730 images of peach were used in the training data set and validation data set at a ratio of 8:2. The remains of 170 images were used to test the deep learning models. In this study, among the fruit quality attributes, firmness, Hue value, and a^* value were adapted to the index with maturity classification, such as immature, mature, and over mature fruit. This study used the CNN (Convolutional Neural Networks) models for image classification; VGG16 and InceptionV3 of GoogLeNet. The performance results show 87.1% and 83.6% with Hue left value in VGG16 and InceptionV3, respectively. In contrast, the performance results show 72.2% and 76.9% with firmness in VGG16 and InceptionV3, respectively. The loss rate shows 54.3% and 62.1% with firmness in VGG16 and InceptionV3, respectively. It considers increasing for adapting a field utilization with firmness index in peach.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.