초록

제 1 장 연구개발 과제의 개요
1.1 연구 개발 목적
◦ 국내 시설재배의 선진화를 위하여 작물 생육 모델 및 ICT기반 온실 환경제어 지원 기술을 개발한다

1.2 연구 개발의 필요성
◦ 시설농가 측정데이터가 실시간으로 수집 저장되나 영농 활용이 미흡
◦ 시설작물의 생육 진단 및 수확량 예측을 위한 생육 모델이 없음
◦ 생산성을 올리기 위해 스마트 온실에서 작물별 최적 환경 및 생육 관리 S/W가 필요함

( 출처 : 본론 제 1 장 연구개발 과제의 개요 21p )

Abstract ▼

1. [제1세부] 시설작물의 최적 환경관리 및 제어모델 개발
◦ The protected cultivation involves artificially constructing and controlling cultivating conditions suitable for the growth of crops and it provides a merit of managing the environment to the optimal in which crops are best suited for growth. Complex environmen

1. [제1세부] 시설작물의 최적 환경관리 및 제어모델 개발
◦ The protected cultivation involves artificially constructing and controlling cultivating conditions suitable for the growth of crops and it provides a merit of managing the environment to the optimal in which crops are best suited for growth. Complex environmental control means the ability to control environmental elements within the facility using control devices based on environmental data and growth data. However, the complex environment controller is currently increasing the inconvenience of farming households due to the complicated methods of use and maintenance without effective support. Therefore, in order to cover these shortcomings, it is necessary to develop an optimum growth management model for crops in the facility.

◦ The purpose of this study is to establish the Optimal Environmental Control Settings Table for the control decision system, which is a part of the development of the integrated complex environment controller. This standard includes environmental settings for optimum growth of tomatoes, such as temperature, light and CO2 concentration. It was developed to assist the use of the complex environment controller by the farming households.

◦ Appropriate guidelines are required for each crop to operate a complex controller and it should consider the environment, such as the size of each farm facility. Currently,however, the vast majority of farm households are cultivating their crops without being provided with proper guidelines, despite the lack of unified environmental facilities. Therefore, this study suggested the methods and decision supporting modules to derive optimal environmental control data from these conditions.

◦ The decision supporting module suggested in this study utilizes the environmental data and growth data provided by each farmer's facility to apply the best optimal environment control data and regulates the amount and timing of forwarding. It also provides information on diseases and pests, physiological injury and environmental problems, and builds a database for professional know-how. However, in order to derive optimum environmental data and maximize productivity, each farmer needs many years of collection of environmental control data and the initial data requires verification from experts and professional organizations

◦ The intelligence system for protected cultivation was developed to support efficient use of controllers in households that use complex environmental controllers. The system also incorporates a growth model to simulate the estimated growth resulting from variation of environmental control values.

◦ In conjunction with a complex environment controller, it monitors the current status within the greenhouse and provides alarm information for emergencies. Furthermore, it is possible to analyze the history of cultivation to enhance the environmental control technology. Farmers can inquire the disease and pest information base on symptoms by linking the growth diagnostic system.

2. [제2세부] 최적 환경 및 제어 시스템 개발
◦ The omplex environmental control system currently used in the green house is operated under general control rules, not based on DB (knowledge-based) control.
Therefore, it is necessary to build a database of growth information (expert advice, actual operational data, documents, etc.) for targeted crops and to deliver growth information and control based on the growth status of the crop.

◦ Moreover, it is needed to develop a growth control model using the correlation analysis between the dynamic growth amount of the facility crops and complex environmental factors, and a decision system to generate the optimal environmental conditions based on the model. Therefore, we want to develop and improve quality and productivity by developing a complex control technology for the greenhouse environment based on the developed ICT in Korea.

◦ In this study, various decision support systems were examined, and the necessity,characteristics and examples of decision support systems in the field of agriculture were studied. The basic structure of the decision support system is composed of the user, database, model bases, knowledge base, and method bases. The agriculture decision support system provides information that enables the agricultural producers to make various decisions suitable for cultivation conditions, and consists of three large axes : a agricultural intelligence system, agricultural production models, and agricultural expert systems.


◦ This study introduced the basic structure of the decision support system and detailed description of the model base, which is a characteristic of the decision support systems in the field of agriculture. Also, we compared various domestic and foreign decision support systems for agriculture and discussed the need for developing a decision support system to optimize the industrial structure of agriculture.

◦ The decision support system provides information that allows unfamiliar farmers to use complex environmental controllers more effectively, and the accumulated cultivation experiences are stored in the form of knowledge base and support users. The decision support system was developed based on the Java-based implementation environment, the main technology elements are spring framework, hibernate, Ajax, JSON and Amchart. The decision support system searches the knowledge base that has been developed in the Task Team No 1 with the real-time collection of environments and control information and determine whether controls are relevant or not and provides users with control information.

3. [제1협동] 생육 진단 및 일반 예측 모델 개발
◦ Crop growth simulation model would be employed as a useful tool for decision-making in greenhouse crop production. Simulation of crop growth using a crop model would be useful to optimize control of climate conditions within a greenhouse. The objective of this research project is to develop crop growth models for greenhouse tomato and chrysanthemum.

◦ The mechanistic tomato growth and development model were developed to simulate the growth of stem, leaf, root and fruit, and the development of leaves, leaf area,flowers and fruits by using the weather data inside the greenhouse. The model for growth and development of tomato consists of modules for calculation of radiation transfer, energy balance within a greenhouse as well as photosynthesis and phenology.
In simulation of transpiration, aerodynamic resistance was calculated using empirical equation. For simulation of photosynthesis, leaf temperature obtained from energy balance equation was used. Using air temperature within the greenhouse, growing periods and flowing timing were also determined. The model was designed and programmed in JAVA script to provide the simulation outputs through memory in order to interface with a controller of greenhouse for optimum conditions, e.g.,ventilation, shading or warming. Objective oriented programming approach was used to design a simple tomato model. The model developed were proved to simulate the growth and development of tomato very realistically in a greenhouse climatic condition. However, the model could not simulate tomato growth and development when management options such as defoliate, flower thinning were conducted. This results indicated that further calibration would be needed to improve accuracy of the model.

◦ Chrysanthemum is a typical short day plant of which floral initiation and development is sensitive to photoperiod. We developed a model to predict phenological development and leaf appearance of chrysanthemum (cv. Baekseon) using daylength (including civil twilight period), air temperature, and management options like light interruption and ethylene treatment as predictor variables. Chrysanthemum development stage (DVS) was divided into juvenile (DVS=1.0), juvenile to budding (DVS=1.33), and budding to flowering (DVS=2.0) phases for which different strategies and variables were used to predict the development toward the end of each phenophase. The juvenile phase was assumed to be completed at a certain leaf number which was estimated as 15.5 and increased by ethylene application to the mother plant before cutting and the transplanted plant after cutting. After juvenile phase, development rate (DVR) before budding and flowering were calculated from temperature and day length response functions, and budding and flowering were completed when the integrated DVR reached 1.33 and 2.0,respectively. In addition the model assumed that leaf appearance terminates just before budding. This model predicted budding date, flowering date, and leaf appearance with acceptable accuracy and precision not only for the calibration data set but also for the validation data set which are independent of the calibration data set.

4. [제2협동] 생육 진단 관리기 및 수량 예측 시뮬레이터 개발
◦ It is not easy to decide to the growth state of the products by the farmers. In case of the disease, pest and poor growth, they are needed the expert knowledge to decide. If they dose not take measures, the production will be decreased.

◦ In this paper, we studied and developed the expert system to assist on the decision about the growth state to the products. There are many kinds of growth state such as, disease, pest, poor growth, vegetation status, and reproductive status. Firstly we made a model about the growth diagnosis system for tomato. For the diagnosis, we defined the key index which affect to the growth of the tomato, Using the key index, we can make a diagnosis the status of the growth and take action to tomato. The index consist of Measure Index(MI) which is used to confirm the status of the tomato using the continuous growth check and Period Index(PI) which decide to the step whether vegetation period or reproductive growth period of the tomato.

◦ Another system, we developed the expert system about the disease pest, vegetation disturbance, reproductive disturbance in tomato and chrysanthemum.

◦ As a result of the system, we constructed 20 disease DB, 7 pest DB, 6 vegetation disturbance DB, 22 reproductive disturbance DB in tomato. also we constructed 9 disease DB, 8 pest DB, 11 vegetation disturbance DB, 11 reproductive disturbance DB. Using the system, farmers can decide the exact status of the products and take measures of the diagnosis prescription.

5. [제3협동] 시설채소 최적 생육관리 모델 검정
◦ The purposes of this research are establishment of optimal environment management guide for vegetable greenhouse. The greenhouse management guide such as tomato, paprika, and strawberry are shown as literature survey of temperature, light and monitoring of vegetable growth. Moreover, parameter values of tomato growth model [GreenTom] were changed for accuracy enhancement. The algorithms based on physiological response were made to properly control greenhouse of tomato.

6. [제4협동] 시설화훼 최적 생육관리 모델 검정
◦ The purposes and contents are establishment of optical environment management guide for facilities. The results is shown the monitoring the growth of chrysanthemum according to environmental factors in facility including environmental survey of temperature, light and monitoring of chrysanthemum growth in facility. Moreover, testing parameter value of chrysanthemum growth mode. The expected contribution according to monitoring and testing these survey is that establishment of environment management guide of facility chrysanthemum to achieve efficiency of environmental management, high-quality timely shipment and cultivation technology by developing the optimum environment management guide for facilities chrysanthemum and increase income and export of cultivated farm households and raise national competitiveness.

( 출처 : SUMMARY 5p )

목차 Contents

• 표지 ... 1
• 연구개발 최종보고서(평가용) ... 3
• S U M M A R Y ... 5
• 목차 ... 10
• 표목차 ... 14
• 그림목차 ... 16
• 제 1 장 연구개발 과제의 개요 ... 20
• 1.1 연구 개발 목적 ... 21
• 1.2 연구 개발의 필요성 ... 21
• 1.2.1 연구개발기술의 경제적·산업적 중요성 ... 21
• 1.2.2 시설작물 최적 생육관리 모델 개발의 필요성 ... 23
• 1.3 연구 개발 범위 ... 23
• 1.3.1 1차년도 ... 23
• 1.3.2 2차년도 ... 24
• 1.3.3 3차년도 ... 25
• 1.3 국내외 기술개발 현황 ... 27
• 1.3.1 국내 기술개발 현황 ... 27
• 1.3.2 국외 기술개발 현황 ... 27
• 1.3.3 국내외 연구현황 비교 및 필요 연구 분야 ... 28
• 제 2 장 연구수행내용 및 결과(1) : [제1세부] 시설작물의 최적 환경관리 및 제어모델 개발 ... 29
• 2.1 서 론 ... 30
• 2.2 국내외 기술개발 현황 ... 32
• 2.2.1 국내 기술개발 현황 ... 32
• 2.2.2 국외 기술 수준 및 시장 현황 ... 35
• 2.3 연구 개발 수행 내용 및 결과 ... 36
• 2.3.1 시설작물의 최적 환경관리 및 제어모델 개발 ... 36
• 제 3 장 연구수행내용 및 결과(2) : [제2세부] 최적 환경 및 제어 시스템 개발 ... 54
• 3.1 서 론 ... 55
• 3.2 국내외 기술개발 현황 ... 57
• 3.3 연구 개발 수행 내용 및 결과 ... 61
• 3.3.1 최적 환경 및 제어 시스템 개발 ... 61
• 제 4 장 연구수행내용 및 결과(3) : [제1협동] 생육 진단 및 일반 예측 모델 개발 ... 68
• 4.1 서 론 ... 69
• 4.2 국내외 기술개발 현황 ... 71
• 4.2.1 국내 기술개발 현황 ... 71
• 4.2.2 국외 기술 수준 및 시장 현황 ... 71
• 4.3 연구 개발 수행 내용 및 결과 ... 72
• 4.3.1 시설 토마토 생육 및 수량 예측 모델 개발 ... 72
• 4.3.2 시설 국화의 생장 및 개화기 예측 모델 개발 ... 85
• 제 5 장 연구수행내용 및 결과(4) : [제2협동] 생육 진단 관리기 및 수량 예측 시뮬레이터 개발 ... 104
• 5.1 서 론 ... 105
• 5.2 국내외 기술개발 현황 ... 106
• 5.2.1 국내 기술개발 현황 ... 106
• 5.2.2 국외 기술 수준 및 시장 현황 ... 110
• 5.3 연구 개발 수행 내용 및 결과 ... 114
• 5.3.1 토마토 생육 진단 기술 연구 ... 114
• 5.3.2 국화 생육 진단 서비스 ... 180
• 5.3.3 생육진단전문가 시스템 구축 ... 199
• 제 6 장 연구수행내용 및 결과(5) : [제3협동] 시설채소 최적 생육관리 모델 검정 ... 201
• 6.1 서 론 ... 202
• 6.1.1 연구 개발 목적 ... 202
• 6.1.2 연구 개발의 필요성 ... 202
• 6.1.3 연구 개발 범위 ... 202
• 6.2 국내외 기술개발 현황 ... 203
• 6.2.1 국내 연구 현황 ... 203
• 6.2.2 국외 연구 현황 ... 203
• 6.3 연구 수행 내용 및 결과 ... 205
• 6.3.1 연구개발 수행 내용 ... 205
• 6.3.2 연구 결과 ... 206
• 제 7 장 연구수행내용 및 결과(6) : [제4협동] 시설화훼 최적 생육관리 모델 검정 ... 219
• 7.1 서 론 ... 220
• 7.1.1 연구 개발 목적 ... 220
• 7.1.2 연구 개발의 필요성 ... 220
• 7.1.3 연구 개발 범위 ... 220
• 7.2 국내외 기술개발 현황 ... 221
• 7.2.1 국내 연구 현황 ... 221
• 7.2.2 국외 연구 현황 ... 221
• 7.3 연구 수행 내용 및 결과 ... 222
• 7.3.1 연구개발 수행 내용 ... 222
• 7.3.2 연구 결과 ... 224
• 제 8 장 연구개발목표 달성도 및 활용계획 ... 243
• 8.1 연구개발목표 달성도 및 대외기여도 ... 244
• 8.1.1 목표대비 대외달성도 ... 244
• 8.1.2 정량적 성과 ... 246
• 8.2 연구개발결과의 활용계획 ... 251
• 8.3 기타사항 ... 254
• 별 첨. 참고문헌 ... 256
• 끝페이지 ... 261