• 한국농공학회:학술대회논문집
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• 한국농공학회 2003년도 학술발표논문집
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• pp.231-234
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• 2003

The Closed Control System, which uses exclusive network and protocol, have been adopted in TM/TC systems. However, the Closed Control System is known that it is not able to support the integration in the water management automation. There are two methods in integration solution in different types of TM/TC System. One solution is hardware system integration that is very expensive and impractical. The other solution is software system integration that uses OLE for Process Control(OPC). In this study, we recommend OPC solution, for KARICO water management, that is the practical and efficient. KARICO is using OPC technology in MMI(Man machine Interface) and water management program. Through this technology, the real-time status of reservoir, pumping station and canal can be achieved without difficulty.

• Journal of Biosystems Engineering
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• 제23권2호
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• pp.157-166
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• 1998

In this study. a traveling control system was developed to transfer a machine without an operator in the working zone. The dimension of the system was modelized to design and construct smaller than that of real configuration of a greenhouse. For this system, the fixed path type was used to detect exact position during operating a manless machine. and the X-Y table actuator type to escape a unique path, which had the disadvantage in a fixed path type environment. Based on the results of this research the following conclusions were made : 1. This system used two screws to move toward horizontal direction, and a Plate to reach at any points in the working zone. 2. The software combined the functions of path selection and motor operation to control into one program. The path selection program was a menu driven program written in Visual Basic, and the motor operation program was written in Borland C++ for actuating motors. 3. The path-select mode of the program was used by selecting the desired paths, and the user path-create mode by selecting a random path in the path-selection program. 4. The system proved to be a reliable system for operating a manless machine, since accuracy and precision to reach the positions were less than 1%.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1997년도 하계 학술대회 논문집
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• pp.238-246
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• 1997

This study was conducted to develop a robotic transplanter for bedding plants. The robotic transplanter consisted of machine vision system, a manipulator, a gripper and plug tray transfer system. The performance of the robotic transplanter was tested and compared by two different transplanting methods, which were to consider the leaf orientation of seedlings and not to. Results of this study were as follows. (1) A cartesian coordinate manipulator for a robotic transplanter with 3 degree of freedom was constructed. The accuracy of position control was $\pm$1 mm. (2) The robotic transplanter with the machine vision system, the manipulator, the gripper and the transfer system was developed and tested with a shovel-type finger. Without considering the orientation of leaves, the success rates of transplanting healthy cucumber seedlings in 72-cell and 128-cell plug-trays were 95.5% and 94.5% respectively. Considering the orientation of leaves, the success rates of transplanting healthy cucumber seedling in 72-cell and 128-cell plug-trays were 96.0% and 95.0% respectively.

• 생물환경조절학회지
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• 제9권4호
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• pp.237-243
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• 2000

현재 온실에서 농작업을 작업기는 사람이 운전하고 있다. 온실 안은 무척이나 무덥고 열악한 작업조건이다. 그렇기 때문에 앞으로 무더운 온실내의 작업기 개발은 사람이 직접 운전하지 않고, 자동화에 의한 무인화 될 것으로 전망된다. 온실에서 자동화 및 무인화의 작업을 돕기 위하여 온실내의 경로(길)에 따라 자동적으로 이동할 수 있는 시스템을 개발하게 되었다. 이 시스템은 작업기의 자동화와 무인화를 위한 것이며, 이를 위하여 온실의 천장에 가이드를 연결한 후 이를 따라 시스템이 자동으로 이동할 수 있는 경로 자동변환 시스템이다. 이 시스템은 연마봉으로 만들어진 경로를 따라 선회 및 직선운동이 가능하며, 이 시스템은 경로 변경을 위한 리미트 스위치와 소프트웨어로 구성되어 있다. 이 시스템의 작동여부를 실험하기 위하여 시테핑모터를 가진 마이크로 마우스를 이용하였다. 견고한 실험실의 평면 위에서의 작동실험은 시스템 작동이 100% 성공률을 나타내었지만 모래위나 다른 조건에서는 성공률이 80% 이하로 낮게 나타났다. 실제 온실에서도 충분한 강도를 가진 연마봉을 이용하여 처짐에 대한 부분을 고려하여 사용한다면 경로를 따라 주행하는 이 시스템은 잘 작동할 수 있을 것으로 기대된다.

• Journal of Biosystems Engineering
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• 제32권3호
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• pp.160-165
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• 2007

This study was conducted to evaluate beef using a color machine vision system. The machine vision system has an advantage to measure larger area than a colorimeter and also could measure other quality factors like distribution of fats. However, the machine vision measurement is affected by system components. To measure the beef color with the machine vision system, the effect of color balancing control was tested and calibration model was developed. Neural network for color calibration which learned reference color patches showed a high correlation with colorimeter in L*a*b* coordinates and had an adaptability at various measurement environments. The trained network showed a very high correlation with the colorimeter when measuring beef color.

• Journal of Biosystems Engineering
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• 제31권6호
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• pp.514-523
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• 2006

Production of green pepper has been increased due to customer's preference and a projected ten-year boom in the industry in Korea. This study was carried out to develop an automatic grading and sorting system for green pepper using machine vision. The system consisted of a feeding mechanism, segregation section, an image inspection chamber, image processing section, system control section, grading section, and discharging section. Green peppers were separated and transported using a bowl feeder with a vibrator and a belt conveyor, respectively. Images were taken using color CCD cameras and a color frame grabber. An on-line grading algorithm was developed using Visual C/C++. The green peppers could be graded into four classes by activating air nozzles located at the discharging section. Length and curvature of each green pepper were measured while removing a stem of it. The first derivative of thickness profile was used to remove a stem area of segmented image of the pepper. While pepper is moving at 0.45 m/s, the accuracy of grading sorting for large, medium and small pepper are 86.0%, 81.3% and 90.6% respectively. Sorting performance was 121 kg/hour, and about five times better than manual sorting. The developed system was also economically feasible to grade and sort green peppers showing the cost about 40% lower than that of manual operations.

• 농업과학연구
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• 제46권2호
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.71-78
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• 2022

Electronic measuring devices have an important role in agricultural projects and in various fields. Electronic measuring devices play a vital role in controlling and saving soil information. They are designed to measure the temperature, acidity and moisture of the soil. In this paper, a new methodology to manage irrigation and soil fertility using an electronic system is proposed. This is designed to operate the electronic irrigation and adds inorganic fertilizers automatically. This paper also explains the concept of remote management and control of agricultural projects using electronic soil measurement devices. The proposed methodology is aimed at managing the electronic irrigation process, reading the moisture percentage, elements of soil and controlling the addition of inorganic fertilizers. The system also helps in sending alert messages to the user when an error occurs in measuring the percentage of soil moisture specified for crop and a warning message when change happens to the fertility of soil as many workers find difficulty in daily checking of soil and operating agricultural machines such as irrigation machine and soil fertilizing machine, especially in large projects.

• 농업과학연구
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• 제47권3호
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• pp.633-644
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• 2020

Rice paper is an element of Vietnamese cuisine that can be used to wrap vegetables and meat. Rice and starch are the main ingredients of rice paper and their mixing ratio is important for quality control. In a commercial factory, assessment of food safety and quantitative supply is a challenging issue. A rapid and non-destructive monitoring system is therefore necessary in commercial production systems to ensure the food safety of rice and starch flour for the rice paper wrap. In this study, fluorescence hyperspectral imaging technology was applied to classify grain flours. Using the 3D hyper cube of fluorescence hyperspectral imaging (fHSI, 420 - 730 nm), spectral and spatial data and chemometric methods were applied to detect and classify flours. Eight flours (rice: 4, starch: 4) were prepared and hyperspectral images were acquired in a 5 (L) × 5 (W) × 1.5 (H) cm container. Linear discriminant analysis (LDA), partial least square discriminant analysis (PLSDA), support vector machine (SVM), classification and regression tree (CART), and random forest (RF) with a few preprocessing methods (multivariate scatter correction [MSC], 1^st and 2^nd derivative and moving average) were applied to classify grain flours and the accuracy was compared using a confusion matrix (accuracy and kappa coefficient). LDA with moving average showed the highest accuracy at A = 0.9362 (K = 0.9270). 1D convolutional neural network (CNN) demonstrated a classification result of A = 0.94 and showed improved classification results between mimyeon flour (MF)1 and MF2 of 0.72 and 0.87, respectively. In this study, the potential of non-destructive detection and classification of grain flours using fHSI technology and machine learning methods was demonstrated.

• Agricultural and Biosystems Engineering
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• 제1권1호
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• pp.22-29
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• 2000

The development of bio-response feedback control system known as the speaking plant approach has been a challenging task for plant production engineers and scientists. In order to achieve the aim of developing such a bio-response feedback control system, the primary concern should be to develop a practical non-invasive technique for monitoring plant growth. Those who are skilled in raising plants can sense whether their plants are under adequate water conditions or not, for example, by merely observing minor color and tone changes before the plants wilt. Consequently, using machine vision, it may be possible to recognize changes in indices that describe plant conditions based on the appearance of growing plants. The interpretation of image information of plants may be based on image features extracted from the original pictorial image. In this study, the performance of a finite element retina was optimized by a genetic algorithm. The optimized finite element retina was evaluated based on the performance of neural plant growth monitor that requires input data given by the finite element retina.