• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.54-60
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• 2018

In this paper, we analyze the trends of deep-learning based plant data processing technologies. In recent years, the deep-learning technology has been widely applied to various AI tasks, such as vision (image classification, image segmentation, and so on) and natural language processing because it shows a higher performance on such tasks. The deep-leaning method is also applied to plant data processing tasks and shows a significant performance. We analyze and show how the deep-learning method is applied to plant data processing tasks and related industries.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.952-965
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• 1993

A simulation modeling is necessary for the optimal design of a rice processing plant, which consists of a facility (a silo system) of rice drying and storage and a rice mill plant. In a rice processing plant, the production scheduling and the decision on capcity of each unit based on a queuing theory is very important and difficult. In this study a process-oriented simulation model was developed for the design of a rice drying and storage system with SLAMSYSTEM. The simulation model is capable of simulating virtually all the processing activities and provides work schedules which minimize total processing time , mean flow time and bottleneck of the plant system and estimate drying time for a batch in a drying silo. Model results were used for determination the size and capacity of each processing unit and for analyzing the performance of the plant . The developed model was actually applied to construct a grain silo system for rice drying and storage.

• Journal of Biosystems Engineering
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• v.21 no.3
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• pp.315-324
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• 1996

This study was one of a series of studies on application of machine vision and image processing to extract the geometrical features of plants and to analyze plant growth. Several algorithms were developed to measure morphological properties of plants and describing the growth development of in-situ lettuce(Lactuca sativa L.). Canopy, centroid, leaf density and fractal dimension of plant were measured from a top viewed binary image. It was capable of identifying plants by a thinning top viewed image. Overlapping the thinning side viewed image with a side viewed binary image of plant was very effective to auto-detect meaningful nodes associated with canopy components such as stem, branch, petiole and leaf. And, plant height, stem diameter, number and angle of branches, and internode length and so on were analyzed by using meaningful nodes extracted from overlapped side viewed images. Canopy, leaf density and fractal dimension showed high relation with fresh weight or growth pattern of in-situ lettuces. It was concluded that machine vision system and image processing techniques are very useful in extracting geometrical features and monitoring plant growth, although interactive methods, for some applications, were required.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.8
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• pp.1045-1056
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• 1995

The purpose of multiple alarm processing is to give the operator the correct information and perception of the malfunction present in the plant. In this thesis, an APS(Alarm Processing System) is studied for fossil power plants. This APS is based on a cause-consequence trees in the knowledge representation aspect for alarm and plant and adapts alarm filtering methods using fired time information in the decision aspect. Through the cause-consequence trees and filtering methods, the Alarm Processing System finds the cause alarm among the fired multiple alarms and calculates the cause degree which represents the possibility of a fault occurring in the instruments of the plant with the information of fired alarm. The knowledge base is built via interviews and questionaries with the expert operators on the Seoul power plant unit 4. Finally, the validity of the studied APS is shown via simulations.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2003.04a
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• pp.65-66
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• 2003

Korean ginseng (Panax ginseng C. A. Meyer) is an representative medicinal herb. It is classified as an adaptogen, helping the body to adapt to stress, improving stamina and concentration, and providing a normalizing and restorative effect. However, cultivation and breeding of the plant is very difficult because it requires at least 4-year cultivation from seed germination to root harvest.(중략)

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2005.11a
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• pp.284-284
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• 2005

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2003.10a
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• pp.89-89
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• 2003

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2003.10a
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• pp.90-90
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• 2003

• Proceedings of the Plant Resources Society of Korea Conference
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• 2005.11a
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• pp.179-179
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• 2005

• Journal of Biosystems Engineering
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• v.40 no.1
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• pp.89-93
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• 2015

Purpose: Machine vision-based image processing methods can be useful for estimating the fresh weight of plants. This study analyzes the ability of two different image processing methods, i.e., morphological and pixel-value analysis methods, to measure the fresh weight of lettuce grown in a closed hydroponic system. Methods: Polynomial calibration models are developed to relate the number of pixels in images of leaf areas determined by the image processing methods to actual fresh weights of lettuce measured with a digital scale. The study analyzes the ability of the machine vision- based calibration models to predict the fresh weights of lettuce. Results: The coefficients of determination (> 0.93) and standard error of prediction (SEP) values (< 5 g) generated by the two developed models imply that the image processing methods could accurately estimate the fresh weight of each lettuce plant during its growing stage. Conclusions: The results demonstrate that the growing status of a lettuce plant can be estimated using leaf images and regression equations. This shows that a machine vision system installed on a plant growing bed can potentially be used to determine optimal harvest timings for efficient plant growth management.