농업기술 이동 평가 : 개발, 성과, 향후 전망(2010-2020년 및 2020-2030년)

Agriculture Technology Transition Assessment

The agriculture technology transition assessment examines agricultural technologies' developments, achievements, and future prospects from 2010 to 2020 and 2020 to 2030. This study encompasses crop production, livestock, aquaculture, and forestry, focusing on the industry's production, development, distribution, and maintenance of advanced agricultural technologies. It evaluates how innovations in precision farming, livestock management systems, aquaculture monitoring, and forestry management tools have transformed agricultural practices. The assessment aims to provide a comprehensive overview of technological advancements, their impact on productivity and sustainability, and projections for future trends and opportunities in the agricultural sector.

Industrial Impact

Technology adoption in agriculture has revolutionized crop production, livestock management, aquaculture, and forestry. In crop production, precision farming tools enhance yields and reduce input wastage. Livestock management benefits from automated monitoring systems, improving animal health and productivity. Aquaculture utilizes advanced sensors and data analytics for efficient feeding and water quality management. Forestry operations are optimized with remote sensing and GIS technologies, aiding in sustainable forest management. These advancements lead to increased productivity, resource efficiency, and environmental sustainability across all sectors. Technology adoption is crucial for meeting global food demand and addressing challenges such as climate change and resource scarcity.

Developments in the Agriculture Technology Transition:

• The period from 2010 to 2020 witnessed transformative developments in crop management software. Technologies such as FMIS, GIS, remote sensing, and IoT have become integral to modern agriculture, enabling precision farming and sustainable practices. As these technologies continue to evolve, they promise to enhance further agricultural productivity and resilience in the face of growing global challenges.
• Between 2010 and 2020, precision agriculture witnessed remarkable technological advancements that significantly transformed crop production. From the early adoption of GPS for field mapping to integrating sophisticated farm management systems, these technologies have enabled farmers to optimize input use, enhance productivity, and improve sustainability.
• In the period between 2016-2020, the integration of wireless networks and IoT enabled continuous monitoring and data collection. This data could be analyzed in real-time, allowing immediate action based on sensor readings, enhancing crop health and yield.

How can this report add value to an organization?

Innovation Strategy: This provides valuable insights into the methods and innovations shaping agriculture's shift toward technology-driven sustainability. By conducting a thorough agriculture technology transition assessment, stakeholders can evaluate the challenges and opportunities that come with adopting smart agriculture solutions. Such an assessment enables policymakers, agribusinesses, and farmers to understand the environmental, economic, and operational impacts of transitioning to technologies that support efficient resource use, reduce emissions, and enhance crop yields.

Growth/Marketing Strategy: The transition to advanced agriculture technology has led to developments in the sector, as key players utilize assessments to guide product innovation, market expansion, and strategic alliances. Companies have increasingly conducted thorough assessments of technology integration to meet sustainability goals, enhance efficiency, and support regulatory compliance. Partnerships, collaborations, and joint ventures with technology providers and research institutions enable firms to implement cutting-edge solutions, from precision farming and AI-driven analytics to automated machinery.

Competitive Strategy: The agriculture technology transition assessment provides an in-depth smart agriculture analysis, highlighting the unique challenges and opportunities in each area. This assessment offers a comprehensive comparative analysis of the technologies in the market. Stakeholders gain a clear perspective on the competitive landscape by evaluating factors such as infrastructure readiness, regulatory frameworks, and technology adoption rates. This enables agribusinesses, policymakers, and investors to identify strategic areas for growth, align with global sustainability goals, and make informed decisions in adapting to a rapidly evolving agricultural technology market.

Research Methodology

Primary Data Sources

The primary sources involve the smart agriculture industry experts and stakeholders such as platform developers and service providers. Respondents such as vice presidents, CEOs, marketing directors, and technology and innovation directors have been interviewed to verify this research study's qualitative and quantitative aspects.

The key data points taken from primary sources include:

• validation and triangulation of all the numbers and graphs
• understanding the competitive landscape of different technologies

Secondary Data Sources

This research study involves the usage of extensive secondary research, directories, company websites, and annual reports. It also makes use of databases, such as Hoovers, Bloomberg, Businessweek, and Factiva, to collect useful and effective information for an extensive, technical, market-oriented, and commercial study of the global market. In addition to the aforementioned data sources, the study has been undertaken with the help of other data sources and websites, such as www.fao.org and www.worldbank.org.

Secondary research was done to obtain crucial information about the industry's value chain, revenue models, the market's monetary chain, the total pool of key players, and the current and potential use cases and applications.

The key data points taken from secondary research include:

• qualitative insights into various aspects of the market, key trends, and emerging areas of innovation
• quantitative data for mathematical and statistical calculations

Table of Contents

Executive Summary

Scope and Definition

1 Historical Overview: 2010-2020

• 1.1 Major Technological Developments in Crop Production
  • 1.1.1 Precision Agriculture
    • 1.1.1.1 Global Positioning System (GPS)
    • 1.1.1.2 Variable Rate Technology (VRT)
    • 1.1.1.3 Soil and Crop Sensors
    • 1.1.1.4 Remote Sensing and Imagery
    • 1.1.1.5 Automated Machinery and Robots
    • 1.1.1.6 Integrated Farm Management Systems (IFMS)
  • 1.1.2 Genetically Modified Organisms (GMOs) and Hybrid Seeds
    • 1.1.2.1 Genetically Modified Organisms
      • 1.1.2.1.1 Technological Developments in GMOs, 2010-2020
        • 1.1.2.1.1.1 Herbicide-Resistant Crops:
        • 1.1.2.1.1.2 Insect-Resistant Crops:
        • 1.1.2.1.1.3 Drought-Tolerant Crops:
        • 1.1.2.1.1.4 Nutritionally Enhanced Crops:
        • 1.1.2.1.1.5 Virus-Resistant Crops:
    • 1.1.2.2 Hybrid Seeds
      • 1.1.2.2.1 Technological Developments in Hybrid Seeds, 2010-2020:
        • 1.1.2.2.1.1 High-Yielding Hybrid Varieties:
        • 1.1.2.2.1.2 Disease-Resistant Hybrids:
        • 1.1.2.2.1.3 Stress-Tolerant Hybrids:
        • 1.1.2.2.1.4 Improved Nutritional Quality:
        • 1.1.2.2.1.5 Short-Duration Hybrids:
  • 1.1.3 Automated Machinery and Equipment
    • 1.1.3.1 Technological Developments in Automated Machinery and Equipment, 2010-2020
      • 1.1.3.1.1 Automated Tractors
      • 1.1.3.1.2 Drones
      • 1.1.3.1.3 Harvesting Robots
      • 1.1.3.1.4 Seeding and Planting Robots
      • 1.1.3.1.5 Irrigation Systems
      • 1.1.3.1.6 Weeding Robots
      • 1.1.3.1.7 Spraying Drones
      • 1.1.3.1.8 Soil Health Monitoring Robots
      • 1.1.3.1.9 Automated Guided Vehicles (AGVs)
  • 1.1.4 Crop Management Software
    • 1.1.4.1 Technologies Within Crop Management Software, 2010-2020:
      • 1.1.4.1.1 Farm Management Information Systems (FMIS)
      • 1.1.4.1.2 Geographic Information Systems (GIS)
      • 1.1.4.1.3 Remote Sensing
      • 1.1.4.1.4 Variable Rate Technology (VRT)
      • 1.1.4.1.5 Decision Support Systems (DSS)
      • 1.1.4.1.6 Internet of Things (IoT)
      • 1.1.4.1.7 Cloud Computing
      • 1.1.4.1.8 Big Data Analytics
      • 1.1.4.1.9 Blockchain Technology
• 1.2 Technological Advancements in Livestock Production
  • 1.2.1 Animal Health Monitoring Systems
    • 1.2.1.1 Technological Advancements in Livestock Production:
      • 1.2.1.1.1 Wearable Sensors
      • 1.2.1.1.2 Automated Milking Systems (AMS)
      • 1.2.1.1.3 Radio Frequency Identification (RFID)
      • 1.2.1.1.4 Drones and Aerial Imaging
      • 1.2.1.1.5 Internet of Things (IoT)
      • 1.2.1.1.6 Machine Learning and Predictive Analytics
  • 1.2.2 Genetic Engineering and Breeding Innovations
    • 1.2.2.1 CRISPR-Cas9 Gene Editing
    • 1.2.2.2 Marker-Assisted Selection (MAS)
    • 1.2.2.3 Genomic Selection
    • 1.2.2.4 Cloning and Somatic Cell Nuclear Transfer (SCNT)
    • 1.2.2.5 Embryo Transfer and In Vitro Fertilization (IVF)
    • 1.2.2.6 Artificial Insemination
  • 1.2.3 Livestock Management Software
    • 1.2.3.1 Automated Data Collection and Analysis
    • 1.2.3.2 Precision Feeding Systems
    • 1.2.3.3 Health Monitoring and Disease Detection
    • 1.2.3.4 Genetic and Breeding Management
    • 1.2.3.5 Farm Management Systems Integration
    • 1.2.3.6 Blockchain
• 1.3 Progress in Aquaculture Technology
  • 1.3.1 Aquaculture Monitoring Systems
    • 1.3.1.1 Remote Sensing and Satellite Imaging
    • 1.3.1.2 Internet of Things (IoT) and Smart Sensors
    • 1.3.1.3 Biofloc Technology
  • 1.3.2 Breeding and Genetics
    • 1.3.2.1 Selective Breeding
    • 1.3.2.2 Genetic Engineering
    • 1.3.2.3 Genomic Selection
  • 1.3.3 Water Quality Management
    • 1.3.3.1 Sensor Technology
      • 1.3.3.1.1 Dissolved Oxygen Sensors
      • 1.3.3.1.2 pH and Temperature Sensors
      • 1.3.3.1.3 Multiparameter Probes
    • 1.3.3.2 Biofiltration Systems
      • 1.3.3.2.1 Biological Filters
      • 1.3.3.2.2 Constructed Wetlands
      • 1.3.3.2.3 Algal Turf Scrubbers
    • 1.3.3.3 Automated Water Quality Control Systems
      • 1.3.3.3.1 Automated Monitoring Systems
      • 1.3.3.3.2 Smart Feeding Systems
      • 1.3.3.3.3 AI and Machine Learning Algorithms
• 1.4 Forestry Technology Innovations
  • 1.4.1 Remote Sensing and GIS
    • 1.4.1.1 Remote Sensing in Forestry
      • 1.4.1.1.1 High-Resolution Satellite Imagery
      • 1.4.1.1.2 Light Detection and Ranging (LiDAR)
      • 1.4.1.1.3 Hyperspectral Imaging
    • 1.4.1.2 GIS in Forestry
      • 1.4.1.2.1 Spatial Data Integration
      • 1.4.1.2.2 Real-Time Data Analysis
      • 1.4.1.2.3 Mobile GIS Applications
  • 1.4.2 Sustainable Forestry Practices
    • 1.4.2.1 Precision Forestry
    • 1.4.2.2 Sustainable Harvesting Techniques
  • 1.4.3 Timber Harvesting Equipment
    • 1.4.3.1 Harvester and Forwarder Automation
    • 1.4.3.2 Precision Forestry (GPS and GIS Integration)
    • 1.4.3.3 Sustainable Harvesting Practices

2 Current State: 2020-2030

• 2.1 Technological Developments in Crop Production
  • 2.1.1 Advancements in Precision Agriculture
    • 2.1.1.1 Remote Sensing and Satellite Imaging
    • 2.1.1.2 Internet of Things (IoT) and Sensor Networks
    • 2.1.1.3 Drones and UAVs
    • 2.1.1.4 Precision Irrigation Systems
    • 2.1.1.5 Artificial Intelligence (AI) and Machine Learning (ML)
    • 2.1.1.6 Blockchain Technology
  • 2.1.2 Innovations in Crop Genetics
    • 2.1.2.1 CRISPR-Cas9 Gene Editing
    • 2.1.2.2 RNA Interference (RNAi)
    • 2.1.2.3 Genomic Selection
    • 2.1.2.4 Synthetic Biology
    • 2.1.2.5 Marker-Assisted Selection (MAS)
  • 2.1.3 Integration of IoT and AI
    • 2.1.3.1 Precision Farming
    • 2.1.3.2 Smart Irrigation Systems
    • 2.1.3.3 Pest and Disease Detection
    • 2.1.3.4 Soil Health Monitoring
    • 2.1.3.5 Autonomous Farming Machinery
    • 2.1.3.6 Predictive Analytics
• 2.2 Livestock Production Technologies
  • 2.2.1 Smart Animal Health Monitoring
    • 2.2.1.1 Wearable Sensors
    • 2.2.1.2 Biosensors and Biotelemetry
    • 2.2.1.3 RFID and GPS Tracking
    • 2.2.1.4 Machine Learning and Artificial Intelligence
    • 2.2.1.5 Automated Health Management Systems
    • 2.2.1.6 Drone Technology
  • 2.2.2 Precision Livestock Farming
    • 2.2.2.1 Smart Sensors
    • 2.2.2.2 Wearable Devices
    • 2.2.2.3 Robotic Systems
    • 2.2.2.4 Automated Feeding Systems
    • 2.2.2.5 Genomic Tools
    • 2.2.2.6 Data Analytics and AI
  • 2.2.3 Advances in Livestock Genetics
    • 2.2.3.1 CRISPR-Cas9 Gene Editing
    • 2.2.3.2 Genomic Selection
    • 2.2.3.3 Embryo Transfer and In Vitro Fertilization (IVF)
    • 2.2.3.4 Marker-Assisted Selection (MAS)
    • 2.2.3.5 Epigenetic Modifications
    • 2.2.3.6 Whole-Genome Sequencing (WGS)
• 2.3 Advances in Aquaculture
  • 2.3.1 IoT and AI in Aquaculture
    • 2.3.1.1 IoT-Based Monitoring Systems
    • 2.3.1.2 AI-Driven Data Analytics
    • 2.3.1.3 Robotics and Automation
    • 2.3.1.4 Blockchain for Supply Chain Transparency
    • 2.3.1.5 Virtual Reality (VR) and Augmented Reality (AR) for Training and Education
  • 2.3.2 Sustainable Feed and Nutrition
    • 2.3.2.1 Alternative Protein Sources
    • 2.3.2.2 Biofloc Technology
    • 2.3.2.3 Precision Nutrition
    • 2.3.2.4 Probiotics and Prebiotics
    • 2.3.2.5 Integrated Multi-Trophic Aquaculture (IMTA)
  • 2.3.3 Innovations in Breeding Techniques
    • 2.3.3.1 Genomic Selection and Marker-Assisted Selection (MAS)
    • 2.3.3.2 CRISPR-Cas9 and Gene Editing
    • 2.3.3.3 Synthetic Biology
    • 2.3.3.4 Epigenetic Modifications
    • 2.3.3.5 Integrated Multi-Trophic Aquaculture (IMTA) and Selective Breeding
• 2.4 Forestry Technology Trends
  • 2.4.1 Precision Forestry
    • 2.4.1.1 Remote Sensing and GIS
    • 2.4.1.2 Drones
    • 2.4.1.3 AI and Machine Learning
    • 2.4.1.4 IoT and Sensor Networks
  • 2.4.2 Advanced Harvesting Technologies
    • 2.4.2.1 Automated Harvesting Machines
    • 2.4.2.2 Precision Forestry Technologies
    • 2.4.2.3 Sustainable Logging Practices
    • 2.4.2.4 Biomass Harvesting Technologies

3 Comparative Analysis: 2010-2020 vs. 2020-2030

• 3.1 Technological Progress in Crop Production
  • 3.1.1 Precision Agriculture
  • 3.1.2 GMOs and Hybrid Seeds
  • 3.1.3 Automated Machinery and Equipment
  • 3.1.4 Crop Management Software
  • 3.1.5 Integration of IoT and AI
• 3.2 Innovations in Livestock Production
  • 3.2.1 Animal Health Monitoring Systems
  • 3.2.2 Genetic Engineering and Breeding Innovations
  • 3.2.3 Livestock Management Software
  • 3.2.4 Smart Animal Health Monitoring
  • 3.2.5 Precision Livestock Farming
  • 3.2.6 Advances in Livestock Genetics
• 3.3 Advancements in Aquaculture
  • 3.3.1 Aquaculture Monitoring Systems
  • 3.3.2 Breeding and Genetics
  • 3.3.3 Water Quality Management
  • 3.3.4 IoT and AI in Aquaculture
  • 3.3.5 Sustainable Feed and Nutrition
  • 3.3.6 Innovations in Breeding Techniques
• 3.4 Evolution of Forestry Technology
  • 3.4.1 Remote Sensing and GIS
  • 3.4.2 Sustainable Forestry Practices
  • 3.4.3 Timber Harvesting Equipment
  • 3.4.4 Precision Forestry

4 Achievements and Impacts: Case Study

• 4.1 Crop Yield Improvements
• 4.2 Enhanced Livestock Productivity
• 4.3 Aquaculture Efficiency Gains
• 4.4 Sustainable Forestry Practices

5 Challenges and Opportunities

• 5.1 Barriers to Technology Adoption
  • 5.1.1 Crop Production
  • 5.1.2 Livestock
  • 5.1.3 Aquaculture
  • 5.1.4 Forestry
• 5.2 Government Initiatives and Impacts
• 5.3 Investment and Funding Trends

6 Recommendation and Conclusion

• 6.1 Recommendations
  • 6.1.1 Recommendation for Policymakers
  • 6.1.2 Recommendation for Technology Providers
  • 6.1.3 Recommendations for End Users

7 Research Methodology

• 7.1 Data Sources
  • 7.1.1 Primary Data Sources
  • 7.1.2 Secondary Data Sources