초록

○ 스마트팜농가에서 발생하는 스마트팜 빅데이터를 수집하는 체계를 구축
○ 시설채소 데이터의 성격을 정의하고 실제 각 지역별 주산 시설채소 품목의 환경, 생육, 생산량, 병해충과 같은 이미지 등의 데이터를 수집
○ 수집된 데이터를 활용하여 생산성 향상 모델, 병해충 이미지를 통한 분석 모델 및 농업인이 사용할 수 있는 분석 방법 등을 개발
○ 시설채소 빅데이터의 중요성을 높이고 그에 따른 활용도를 제고

(출처 : 요약서 4p)

Abstract ▼

Purpose&Contents
○ The development and standardization of investigation of horticultural vegetable smart farm
○ The development of utilization in horticultural vegetable bigdata and upgrade
○ The collecting of image data in horticultural vegetable smartfarm
〇 Development of automatic pes

Purpose&Contents
○ The development and standardization of investigation of horticultural vegetable smart farm
○ The development of utilization in horticultural vegetable bigdata and upgrade
○ The collecting of image data in horticultural vegetable smartfarm
〇 Development of automatic pest control technology for pest insects using image information of facilities greenhouse crops
〇 Development and standardization of facilities for vegetable Smart Farm Big data survey item
〇 Collection of facility image data and DB establishment
〇 Data collection and consulting to solve difficulties
〇 Productivity improvement consulting based on data analysis result
〇 Facility Vegetable Smart Farm Big Data Utilization Precision management technology development, maximization of product value and optimization of farm profitability
〇 Productivity improvement consulting on smart farm for greenhouse vegetables by development and collection of whole cycle big data
〇 Development and collection of whole cycle big data in smart farm for greenhouse vegetables
- Subject vegetables: tomato, strawberry, paprika, outdoor vegetables (onion and garlic)
- Main contents: Development and collection of big data which influence the productivity
〇 Consulting on environmental management and control methods by big data analysis, and annual business performance analysis
〇 Gathering big data of facilities vegetable during the growing period in Chungnam and analyzed to conduct consulting for productivity improvement.
〇 to build smart farm bigdata to develop productivity improvement model
〇 to identify factors that best explain productivity and quality by using data built through Big Data Processing
〇 to develope a model that expresses the productivity of the crops by understanding the basic information about the crops and the nature of the collected data
〇 the quality optimization of crops using the productivity development model
〇 development of precise management technology for cultivation using
Smart Farm Big Data and maximization of product value and optimization of farm profitability
〇 facilities Vegetable Smart Farm Big data analysis technique and optimization study
- to develope of Smart Farm Big data processing technology and productivity improvement model using the developed Big data analysis technique
- optimization Study using Smart Farm Big data analysis
〇 development of productivity improvement model through Smart Farm Big data analysis
- development of productivity and quality enhancement prediction model through Smart Farm Big data analysis
- quality optimization study using productivity improvement development model
- model evaluation and model enhancement
〇 farmers profitability optimization study using Smart farm Big data
〇 The purpose of research
- facility vegetable big data collection cycle of smart farm
〇 The contents of research
- Improvement-productivity (increase quantity, quality improvement) a big data collectors
- A big data collectors: environment (temperature, insolation etc.), Growth(leaf number, stem thickness etc.), management (quantity, quality etc.)
- management performance analysis of the consulting-farmer
〇 Facility Vegetables Smart Farm Whole Period Big Data Excavation and Collection
- Target composition: Tomato, strawberry, yellow pepper
〇 Collect and secure preemptive data for smart farm farmers to solve difficulties
〇 Big data-based growth and environmental data analysis consulting field demonstration
〇 Searching and collection to the big data of greenhouse vegetable smart farms during growth period
〇 Consulting of smart farms to use the big data analytics results
〇 Collection of cultivation images of vegetables and fruits grown in greenhouse
〇 Data treatment and development of deep learning algorism for application in smart farming
〇 Excavation and collection of big data of facility vegetable smart farm.
〇 Facility Vegetables Smart Farm Whole Period Big Data Excavation and Collection
- Target composition: cherry tomato, strawberry, garlic, Chinese cabbage
〇 Collect and secure preemptive data for smart farm farmers to solve difficulties
〇 Big data-based growth and environmental data analysis consulting field demonstration

Results
〇Convolutional neural network system were developed to perform plant disease detection and diagnosis using simple leaves images of healthy and diseased plants, through deep learning methodologies in this paper.
Training of the model structure was performed with the use of an open database of 87,848 images, containing 25 different plants in a set of 58 distinct classes of [plant, disease, insect pest] combinations, including healthy plants. Several model system architectures were trained, with the best performance reaching a 99.53% success rate in identifying the corresponding [plant, disease, insect pest] combination (or healthy plant).
The significantly high success rate makes the model system a very useful advisory or early warning tool, and an approach that could be further expanded to support an integrated plant disease and insect identification system to operate in real cultivation conditions. On the other hand, the excessive iron content in groundwater for hydroponics induced toxicity and physiological damage to paprika crops. Especially, at high concentration of iron(Fe) 20ppm, it decreased in all parts such as the dry weight and the weight of leaves and stems of plant decreased significantly. In addition, photosynthetic rate and transpiration rate of paprika leaf after overfeeding were affected from the growth point to the ninth leaf. At this time, brown spots were sporadic between leaf veins in excess of leaf supply during the .
○ The development and standardization of method in investigation of horticultural vegetable smart farm
○ The development of utilization in horticultural vegetable bigdata and upgrade
○ The collecting method of image data in horticultural vegetable smartfarm
〇 Collection of image information according to indoor growing conditions
〇 Facilities Vegetables Smart Farm Major items Big data survey item development and collection criteria setting
- Environmental information: measurement of 61 items including temperature, humidity and CO2 inside and outside
- Growth information: 25 items such as growth length, leaf length, leaf width, leaf number, yield,
〇 Facilities Vegetables Smart Farm Whole Period Big Data Excavation and Collection
- Target : Tomato (mini Tomato), strawberry, paprika, mushroom
· Finding and collecting big data on productivity improvement (quantity increase, quality improvement, price, etc.)
〇 On-site consulting utilizing Smart Farm's full-time data analysis results
- Big data analysis of information gathering farmhouse
- Consulting on environment management and control method using analysis result
〇 dBASE establishment of environment and cultivation information for tomato, strawberry, and paprika
〇 Analysis of effects on the quality of collected big data utilization and growth
- Established model of smart farm environment for Daphnis tomato
- Factor analysis to affect the production amount of tomatoes in South Gyungsang area by breeds
〇 The methods of investigating the growth of cherry tomatoes grown in smart farms for the data gathering was determined, and data on the growth and yield and the cultivation environment of 27 farms were gathered and converted into a database. After analyzing the gathered data, we consulted to improve the problem management methods.
- Technical aspects
· Establishing precise management standards to improve the quality of data produced in ICT greenhouses
- Economic and industrial aspects
· Establishing a consulting support system based on data, thereby increasing the number of items per crop, improving quality, and reducing costs, thereby having competitiveness with developed countries
〇 Establishment of environment and growth information DB of strawberry, tomato, yellow pepper
〇 Comparison of Growth and Environmental Management by Farm Production Level
〇 Promoting farmers' consulting using Big Data
〇 Searching and collection to the big data of Korean melon, strawberry, tomato, cucumber etc. 54 households
〇 Consulting of Korean melon and strawberry smart farm(6 households) to use the big data analytics results
〇 Established strawberry firmness system
〇 Collected highland summer strawberry growth images
〇 Collected highland summer strawberry firmness, sweetness and acidity
〇 Greenhouse Smartfarm Bigdata Discovery and Collection
- Collection period: 2018. 1. ~ 12.
- Collection of big data Number of farmers and crops
. farm : No.32
. crops : cucumbers, tomatoes, Anthurium, chrysanthemums, mushrooms, peppers, Chinese cabbage, etc.
- Main content
. Facility vegetables Smartfarm bigdata excavation
. Collection of bigdata : environment (temperature, solar radiation etc.), growth (Number of leaves, stem diameter etc.) Management (quantity, quality etc)
〇 Field consulting using Smartfarm Bigdata
- Target farmer: Cucumber farmhouse No. 2
- Main content
. Consulting on environment management and control method through bigdata analysis
. Consulting on management method of environment by growth stage
〇 Established smart farm cherry tomato, strawberry environment and growth information DB
〇 Outdoor vegetable garlic, cabbage environment and growing information DB
〇 Promoting farmers' consulting using Big Data

Expected Contribution
○ Increasing the utilization of smartfarm bigdata from industry, farmer and researcher
〇 Development of rapid monitoring technology on the damage of plant abnormal symptoms in local hydroponic system
〇 Monitoring technology of the insect pest using sex pheromone and lighting-emitting trap for moths attracting in smart farm greenhouse
〇 Establishment of control standards for greenhouse pest control for hydroponic cultivation system
〇 Improvement of growth rate and yield productivity using collected big data in smart farm
〇 Diagnosis technology of pest and growth disorder using image information in greenhouse management syste
〇 Diagnosis technology of pests and physiological disorders using smartphone
〇 Publishing manuals for facility greens Smart Farm and Environment Big Data Utilization Model
〇 Establishment of environment and growth information DB of tomato, strawberry, paprika
〇 Improve productivity by collecting and utilizing big data in smart farm cultivation greenhouse
〇 Consulting of rural promotion business by constructing consulting system under data base
〇 Increase the number of tomatoes (ripe, drips), paprika, And cost savings (using farming)
〇 Environment, Growth and Big Data Relation of Farm House / Union by Facilities and Vegetables Improved productivity through analysis
〇 Creation of jobs in the agri-food sector by providing drivers of profitability improvement of new agriculture Establishing a close network system between farm support organizations and farmers to ensure production stability

〇 Utilization plans
- Increase the frequency of data utilization in smart farms and develop the consulting system of management control
- Analysis of economic efficiency by improvements of environment for tomatoes, strawberries, and paprikas
- dBASE development of environment and cultivation information for tomatoes, strawberries, and paprikas
- Estimate supply of sensitive vegetables by data collection of outdoor vegetables including onions and garlics

〇 Expected outcomes
- Preparation of basis for collection, management and sharing of whole cycle big data in smart farm for greenhouse vegetables
- Expansion of smart farm by the developments of success cases using whole cycle big data in smart farm
- Development of efficient big data collection system to enhance the quantity and quality of tomatoes, strawberries, and paprikas

〇 Using the collected data, smart farm management technology can be developed and established consulting system to improve productivity and quality, to increase competitiveness through reducing production cost, and to more smart farms can be deployed.
- to provide farm management methods to increase the yield by using data
- to identify the key factors and optimal level of environmental management and growth environment for each stage of growth using data analysis method

〇 Take advantage of research and development results to the plan
- ICT technology and smart farm farmhouse farming data based decision support consulting system

〇 expected results
- smart farm I dig the big give to take advantage of best practices of data through promoting the smart farm spread
- data-based farmers, increase the crops quantity into the promotion Farmhouse consulting , a consulting quality improvement, cost competitiveness in the smart farm farmhouse

〇 Utilization plan
- Provide environmental control system operation and data utilization technology
- Improvement of smart farm operation through feedback of growth information by farmers
- Established professional consulting system for environmental management based on data
- It is used as basic data for productivity increase and forecasting of supply /

〇 Expected effect
- Realization of precision farming by improving the operation control system of environmental control system
- Increase the yield by controlling greenhouse environment suitable for growing crops -
- Fostering smart agricultural experts by establishing a crop consulting system and increasing data utilization farms
- Improvement of the quality of life of farmers due to increased incomes and supply and demand instability of green pepper farmers
〇 Build of effective collection system to the big data for quantity and quality improvement of Korean melon, strawberry
〇 A bond of sympathy to the importance of big data collection for productivity improvement of Korean melon and strawberry farms
〇 Development of deep learning algorism
〇 Secure the competitiveness of Smartfarm farmers with consulting on Smartfarm Bigdata base
〇 Provide environmental control system operation and data utilization technology
〇 Improvement of smart farm operation through feedback of growth information by farmers
〇 Established professional consulting system for environmental management based on data
〇 Use of consulting materials for decision making of data-based farming
〇 Realization of precision farming by improving the operation control system of environmental control system
〇 Increase the yield by controlling greenhouse environment suitable for growing crops -
〇 Fostering smart agricultural experts and increasing data utilization farms

(출처 : Summary 10p)

목차 Contents

• 표지 ... 1
• 제출문 ... 2
• 보고서 요약서 ... 4
• 국 문 요 약 문 ... 5
• Summary ... 10
• 목차 ... 17
• 제 1 장 연구 개발 과제의 개요 ... 18
• 제 2 장 연구 수행 내용 및 결과 ... 25
• (1세부) 시설채소 스마트팜 전주기 빅데이터 활용 기준 및 매뉴얼 개발 ... 25
• 제2협동_시설채소 생리장해･병해충 이미지 빅데이터 기준 및 매뉴얼 개발 ... 43
• 제3협동_전남 시설채소 스마트팜 전주기 빅데이터 수집 및 현장 컨설팅 ... 69
• 제4협동_전북 시설채소 스마트팜 전주기 빅데이터 수집 및 현장 컨설팅 ... 97
• 제5협동_경북 시설채소 스마트팜 전주기 빅데이터 수집 및 현장 컨설팅 ... 174
• 제6협동_경남 시설채소 스마트팜 전주기 빅데이터 수집 및 현장 컨설팅 ... 193
• 제7협동_충남 시설채소 스마트팜 전주기 빅데이터 수집 및 현장 컨설팅 ... 216
• 제8협동_강원 시설채소 스마트팜 전주기 빅데이터 수집 및 현장 컨설팅 ... 236
• 제9협동_시설채소 품목별 빅데이터 활용 생산성 향상 모델 개발 및 수익성 최적화 연구 ... 283
• 제10협동_딥러닝 기반 시설채소 생장 및 병해충 진단 기술 개발 ... 303
• 제11협동_경기 시설채소 스마트팜 전주기 빅데이터 수집 및 현장 컨설팅 ... 316
• 제12협동_충북 시설채소 스마트팜 전주기 빅데이터 수집 및 현장 컨설팅 ... 330
• 제13협동_제주 시설채소 스마트팜 전주기 빅데이터 수집 및 현장 컨설팅 ... 356
• 제 3 장 목표달성도 및 관련분야 기여도 ... 361
• 제 4 장 연구 결과의 활용 계획 ... 366
• 제 5 장 연구 개발 결과의 보안 등급 ... 367
• 제 6 장 국가과학기술지식정보서비스에 등록한 연구시설·장비 현황 ... 367
• 제 7 장 연구개발과제의 대표적 연구실적 ... 367
• 제 8 장 기타사항 ... 369
• 제 9 장 참고문헌 ... 372
• 끝페이지 ... 375