Sustainable Agriculture Applications Using Large Language Models examines how AI-powered tools can enhance decision-making, resource optimization, and knowledge dissemination across diverse agricultural contexts. Spanning topics from crop management and pest control to water conservation and market analysis, the book features a multidisciplinary approach including real-world applications and case studies.
It highlights how LLMs can support sustainable practices by analyzing agricultural data, improving communication with farmers, and enabling predictive insights. Special attention is given to ethical considerations and the responsible deployment of AI tools in rural and technologically evolving regions.
Key Features:
- Demonstrates how LLMs support sustainable agriculture practices
- Showcases AI-driven approaches in crop, pest, and water management
- Presents real-world applications and case studies across regions
- Explores ethical considerations and responsible AI deployment in agriculture
- Analyzes LLM integration in agricultural education, communication, and policies
Table of Contents
CHAPTER 1 LEVERAGING ARTIFICIAL INTELLIGENCE IN INDIA'S FOOD PROCESSING INDUSTRIES: ADVANCING SUSTAINABLE AGRICULTURE THROUGH LARGE LANGUAGE MODELS
- INTRODUCTION
- REVIEW OF LITERATURE
- Research Gap
- Theoretical Background
- TECHNOLOGY REVOLUTION
- AI in FPIs at the Global Levels
- Strategic Framework for AI development in India
- Opportunity: Economic Impact of AI
- AI for Greater Good: Social Development and Inclusive Growth
- AI Garage for 40% of the World
- AI in FPIs of India
- Utilisation of Technology in Food Parks for the Growth of Agribusiness
- GOVERNMENT SCHEMES FOR THE DEVELOPMENT OF FOOD PROCESSING INDUSTRIES
- SCOPE FOR AI IN FOOD PROCESSING INDUSTRIES (FPIs)
- Segregation of Raw Agri and Horticultural Products
- Smart Farming
- Personal Hygiene
- AI Helps Supply Chain Management in FPIs
- Government’s Support
- CHALLENGES OF AI IN THE FPIs
- CONCLUSION
- REFERENCES
CHAPTER 2 SUSTAINABLE WATER MANAGEMENT STRATEGIES
- INTRODUCTION
- CASE STUDIES AND APPLICATIONS
- Flood Forecasting and Control
- Water Quality Monitoring
- Agricultural Water Management
- CHALLENGES AND LIMITATIONS
- Data Quality and Availability
- Inconsistent and Incomplete Data
- Data Sparsity
- Outdated Data
- Computational Resources
- High Power of Computation
- Accessibility Issues
- Ethical and Privacy Issues
- Data Privacy
- Bias and Fairness
- Interpretability and Transparency
- Nature of Black Box
- Explainability
- FUTURE DIRECTIONS
- Advances in AI and Machine Learning
- Interaction with Other Technologies
- Policy and Regulatory Environments
- CONCLUSION
- REFERENCES
CHAPTER 3 REMOTE SENSING AND GIS APPLICATIONS IN AGRICULTURE
- INTRODUCTION
- Agricultural Applications of Remote Sensing Technologies
- Satellite Remote Sensing
- Aerial Remote Sensing
- Unmanned Aerial Vehicles (UAVs)
- Data Acquisition and Preprocessing
- Data Acquisition
- Data Preprocessing
- GIS Uses in Agriculture
- GIS in Agricultural Mapping and Analysis
- GIS Data Layers of Relevance to Agriculture
- GIS Tools and Software
- Large Language Models (LLMs) and their Applications
- Fine-Tuning of Pre-Trained LLM for Specific Applications
- LLM Capabilities and Limitations in Analyzing Information
- Capabilities of LLMs
- Limitations of LLMs
- Integrating LLMs with Remote Sensing and GIS
- Data Fusion Technique
- Multi-Source Data Integration
- NLP-Based Data Interpretation Model
- Case Studies and Applications
- Yield Prediction
- Pest and Disease Detection
- Case Study 1
- Crop Monitoring and Yield Prediction
- Methodology
- Results and Discussions
- Case Study 2
- Pests and Disease Monitoring
- Methodology
- Applications in Agriculture
- Crop Monitoring and Yield Prediction
- Remote Sensing for Crop Monitoring
- Yield Prediction Models
- NDVI Calculation
- Soil Health and Nutrient Management
- Nutrient Management
- Pest and Disease Detection
- Remote Sensing for Pest and Disease Detection
- LLMs in Pest Management
- Enterprise-wide AI: Precisive Agriculture and Decision Support Systems
- Precision Agriculture Techniques
- Decision Support Systems
- CONCLUSION
- REFERENCES
CHAPTER 4 TECHNOLOGICAL INTEGRATION AND ECONOMIC SUSTAINABILITY IN AGRICULTURE: A SYSTEMATIC LITERATURE REVIEW
- INTRODUCTION
- METHOD
- Step 1: Data Search and Collection Process
- Step 2: Inclusion/exclusion Criteria
- Step 3: Review the Method Adopted to Examine the Data
- THEMATIC EVALUATIONS OF PAST RESEARCH
- Adoption and Diffusion of Technologies
- Blockchain Technology and Transparency
- Precision Farming and Sustainability
- Farmer Information Needs and Adoption
- Innovation Networks and Sustainability
- Community Supported Agriculture (CSA) and Digital Platforms
- Participatory Action Research and Agroecosystem Restoration
- Circular Economy in Agriculture
- CONCEPTUAL FRAMEWORK
- Outcomes
- Predictors
- Role of Mediators
- Role of Moderators
- RESEARCH GAPS AND FUTURE SCOPE
- CONCLUSION
- REFERENCES
CHAPTER 5 AUTOMATING AGRICULTURE: ROBOTICS AND AI FOR A GREENER FUTURE
- INTRODUCTION
- The Role of Agriculture in the Global Economy
- ROBOTICS IN AGRICULTURE
- Autonomous Tractors and Machinery
- Robotic Harvesters
- Precision Weed Control
- AI IN AGRICULTURE
- Predictive Analytics for Crop Management
- Smart Irrigation Systems
- Real-Time Crop Health Monitoring
- THE IMPACT OF ROBOTICS AND AI ON SUSTAINABILITY
- Reducing Chemical Usage
- Minimizing Water Waste
- Improving Soil Health
- Reducing Carbon Footprint
- Enhancing Biodiversity
- CHALLENGES AND BARRIERS TO ADOPTION
- High Initial Costs
- Lack of Infrastructure
- Technical Integration
- FUTURE PROSPECTS AND INNOVATIONS IN ROBOTICS AND AI FOR AGRICULTURE
- Next-Generation Autonomous Machinery
- AI-Powered Drones and Aerial Systems
- AI-Driven Genetic Engineering and Crop Breeding
- AI-Powered Vertical Farming and Controlled Environment Agriculture
- POLICY SUPPORT AND GLOBAL INITIATIVES
- Government Incentives and Subsidies
- Regulatory Frameworks for AI and Robotics
- International Cooperation and Knowledge Sharing
- CASE STUDIES: REAL-WORLD APPLICATIONS OF ROBOTICS AND AI IN SUSTAINABLE AGRICULTURE
- Case Study 1: Blue River Technology - Precision Weed Control
- Case Study 2: Naïo Technologies - Autonomous Robots for Sustainable Farming
- Case Study 3: John Deere - AI and Robotics for Autonomous Tractors
- Case Study 4: The Netherlands - AI and Robotics in Vertical Farming
- CONCLUSION: A GREENER FUTURE THROUGH ROBOTICS AND AI IN AGRICULTURE
- REFERENCES
CHAPTER 6 ADVANCEMENTS IN AGRICULTURAL TECHNOLOGY
- INTRODUCTION
- Timeline Highlighting Major Agricultural Milestones
- SMART FARMING: THE FUTURE OF AGRICULTURE
- Internet of Things (IoT) in Agriculture
- IoT Application
- AI and Machine Learning in Agriculture
- ROBOTICS AND AUTOMATION: REVOLUTIONIZING FARM OPERATIONS
- Robot Type
- Agri Drones: Enhancing Aerial Surveillance and Precision Farming
- Robotics in Harvesting and Crop Management
- PRECISION AGRICULTURE: ENHANCING EFFICIENCY AND SUSTAINABILITY
- Big Data and Analytics in Precision Agriculture
- Controlled Environment Agriculture: The Rise of Indoor Farming
- AGRI-TECH STARTUPS AND THEIR ROLE IN INNOVATION
- AGRICULTURAL BIOTECHNOLOGY: ENHANCING CROP RESILIENCE
- Genetically Modified Crops (GMOs)
- CRISPR Technology and the Future of Crop Breeding
- REGENERATIVE AGRICULTURE: A HOLISTIC APPROACH TO FARMING
- Soil Health and Carbon Sequestration
- Biodiversity and Ecosystem Services
- The Role of Technology in Supporting Regenerative Agriculture
- CONNECTIVITY TECHNOLOGY: BRIDGING THE DIGITAL DIVIDE
Author
- Raghavendra M. Devadas
- Vani Hiremani
- Praveen Gujjar Jagannath
- Lubna Ambreen
