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ویرایش: نویسندگان: U. Kose, V. Prasath, M. Mondal, P. Podder, S. Bharati سری: ISBN (شابک) : 2022000641, 9781003299059 ناشر: سال نشر: 2022 تعداد صفحات: 319 زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 4 Mb
در صورت تبدیل فایل کتاب Artificial Intelligence and Smart Agriculture Technology به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب هوش مصنوعی و فناوری کشاورزی هوشمند نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Cover Half Title Title Page Copyright Page Table of Contents Foreword Preface Acknowledgements About the Editors Contributors Chapter 1 Smart Farming Using Artificial Intelligence, the Internet of Things, and Robotics: A Comprehensive Review 1.1 Introduction 1.2 The Role of Artificial Intelligence in Advanced Farming 1.2.1 The Fundamentals of AI Technologies Involved in Agriculture 1.2.2 AI in Crop Or Seed Selection 1.2.3 AI in Crop Management Practices 1.2.4 AI in Yield Prediction 1.2.5 AI in Pest and Weed Management 1.2.6 AI in Storing and Marketing Products 1.3 The Role of the Internet of Things in Advanced Farming 1.3.1 IoT-Based Soil Sampling 1.3.2 IoT-Based Disease and Pest Monitoring 1.3.3 IoT-Based Fertilization 1.3.4 IoT-Based Yield Monitoring 1.3.5 IoT-Based Irrigation 1.3.6 IoT-Based Food Safety and Transportation 1.4 The Role of Robotics in Advanced Farming 1.4.1 Robotics in Planting 1.4.2 Robotics in Weed Control and Spraying 1.4.3 Robotics in Field Inspection and Data Collection 1.4.4 Robotics in Harvesting 1.5 The Challenges and Recommendations of Indulging Technologies in Advanced Farming 1.6 Conclusion References Chapter 2 Towards the Technological Adaptation of Advanced Farming Through Artificial Intelligence, the Internet of … 2.1 Introduction 2.2 Technology in Advanced Farming 2.2.1 AI in Advanced Farming 2.2.2 IoT in Advanced Farming 2.2.3 Robotics in Advanced Farming 2.3 Challenges in Adoption of Technology 2.4 Conclusion References Chapter 3 Artificial Intelligence and the Blockchain in Smart Agriculture: Emergence, Opportunities, and Challenges 3.1 Introduction 3.2 Literature Review 3.2.1 Overview 3.2.2 Artificial Intelligence in Agriculture 3.2.3 Blockchain in Agriculture 3.3 Case Study: AgroChain – A Blockchain-Powered Transparent Marketplace 3.4 AI and Blockchain for Smart Agriculture: Future Research Dimensions 3.5 The Limitations of AI and the Blockchain in Smart Agriculture 3.6 Conclusion References Chapter 4 Artificial Intelligence and Internet of Things Enabled Smart Farming for Sustainable Development: The Future ... 4.1 Introduction 4.1.1 Challenges in Traditional Farming 4.2 Smart Farming 4.3 Smart Agriculture for Sustainable Development 4.4 AI in Agriculture 4.4.1 AI for Field Condition Management 4.4.2 AI for Crop Management 4.4.3 AI for Livestock Management 4.4.4 AI for Precision Agriculture 4.4.5 AI for Weather Forecasting 4.4.6 AI for Better Decision-Making 4.4.7 AI for Cost Savings 4.5 Machine Learning in Agriculture 4.5.1 Management of Species 4.5.2 Management of Field Conditions 4.5.3 Crop Management 4.5.4 Livestock Management 4.5.5 Models Behind 4.6 How Data Analytics Is Transforming Agriculture 4.6.1 Predictive Analytics 4.6.2 Recommendation System 4.6.3 Data Mining 4.7 Agriculture’s Data Analytics Benefits 4.8 The Challenges of AI in Agriculture 4.9 The IoT and Sensors in Agriculture 4.9.1 The Need for the IoT 4.9.2 Applications of the IoT 4.9.3 The Role of Sensors in the IoT 4.9.4 Sensors in Smart Farming 4.9.5 Architectural Design 4.9.6 ATmega328 Arduino Microcontroller 4.9.7 GSM Module 4.9.8 Supporting Technologies for Smart Farming 4.9.8.1 Zigbee 4.9.8.2 Bluetooth 4.9.8.3 Smartphones 4.9.8.4 Cloud Computing 4.9.9 How Is Data Collected From Sensors? 4.10 Drones in Agriculture 4.10.1 Drone Components 4.10.1.1 Types of Drones 4.10.1.2 UAVs in Smart Agriculture Uses for Agricultural Drones 4.10.1.3 Steps for Capturing Data From an Agriculture Drone 4.10.2 Benefits of Drone Technology 4.11 Challenges and Future Opportunities in Farming 4.12 Conclusion References Chapter 5 A Science, Technology, and Society Approach to Studying the Cumin Revolution in Western India 5.1 Introduction 5.2 Methodology 5.3 Cumin Cultivation in Salt and Water Stress Areas of Patan 5.3.1 Desert Development Programmes and Cumin Cultivation in Patan 5.4 Climate Change and Its Affect On Cumin Cultivation 5.5 Socio-Economic Status of Patan Farmers 5.6 Need for Artificial Intelligence-Based Meteorological Developments in Rural Farming Practices 5.7 Conclusion and Future Perspective 5.8 Limitations of the Study References Chapter 6 The Role of Big Data in Agriculture 6.1 Introduction 6.2 Recent Study and Survey On Global Urbanization 6.3 What Role Does Big Data Play in Agriculture? 6.3.1 The Top Four Big Data Applications at the Farm 6.3.2 Challenges Presented By Implementing Big Data Solutions in Agriculture 6.4 Big Data in Precision Agriculture 6.4.1 Farmer’s Suitability for and Use of Meteorological Data 6.4.1.1 Creating Irrigation Schedules for the Farm 6.4.1.2 Amount of Renewable Energy That the Farm Will Receive 6.4.1.3 Assists in the Safe Handling of the Farm 6.4.2 Weather Forecasting Through Satellite 6.4.3 Forecasting Schedules Created Just for You 6.4.4 Weather Factors That Have an Impact On Farm Planning and Operations 6.5 Forecasting Floods 6.5.1 Flood Monitoring and Forecasting Are Difficult Tasks 6.6 What Role Do Automation and Big Data Play in Feeding the World? 6.6.1 The Benefits of Hydroponic Food Production 6.6.2 What Role Do Big Data and Automation Play in Hydroponics? 6.6.3 The Challenges of Automated Food Production 6.7 Conclusion References Chapter 7 Blockchain-Based Agri Manufacture Industry 7.1 Introduction 7.2 Background 7.3 Expand Manufacturing 7.4 Agri-Blockchain 7.4.1 Blockhain-Based Food Chain 7.4.2 Transactions 7.4.3 Crop Insurance 7.4.4 Traceability 7.5 Shifts in Manufacturing 7.6 Research Framework 7.7 Development of Agri-Blockchain 7.8 Research Process 7.9 Research Hypotheses 7.9.1 To the Body of Knowledge 7.9.2 To the Potential Clients 7.9.3 To the Stakeholders 7.9.4 Novel Theories/New Findings/Knowledge 7.10 Conclusion References Chapter 8 Agricultural Data Mining and Information Extraction 8.1 Introduction: Agriculture and Data Mining 8.2 Data Mining Techniques in Farming 8.2.1 Classification 8.2.2 Clustering 8.2.3 Association Analysis 8.2.4 Prediction 8.2.5 Data Mining With Other Methods 8.3 Case Studies in Agricultural Data Mining 8.3.1 Yield Prediction 8.3.2 Identification of Diseases 8.3.3 Identification of Weeds/Wildflowers 8.3.4 Crop Quality 8.3.5 Gathering of Species 8.3.6 Soil Management 8.4 Discussion 8.5 Research Challenges of Data Mining in Farming 8.5.1 Confidentiality 8.5.2 Quality and Accuracy of Information 8.5.3 Significance of Spatial Information 8.5.4 Inclusion of Farming Field Experience in Data Mining 8.5.5 Scalability of Data Mining Techniques 8.6 Conclusion and Future Scope References Chapter 9 Machine Learning and Its Application in Food Processing and Preservation 9.1 Introduction 9.2 Introduction to Machine Learning 9.3 Machine Learning Techniques and Algorithms 9.4 Machine Learning Algorithms 9.4.1 Naive Bayes 9.4.2 Support-Vector Machine 9.4.3 Neural Network 9.4.4 K-Nearest Neighbour 9.4.5 Decision Tree 9.5 Machine Learning Application to Food Processing and Preservation 9.5.1 Grading and Sorting of Fruits Using Artificial Intelligence 9.5.2 Grading and Sorting of Fruits Using Machine Learning 9.5.3 Grading and Sorting of Fruits Using Support-Vector Machine 9.5.4 Fruit Grading and Sorting Using Artificial Neural Network 9.5.5 Coffee Fruit Using Artificial Neural Network 9.5.6 Dragon Fruit Using Artificial Neural Network 9.5.7 Dates Using Artificial Neural Network 9.5.8 Oil Palm Fruits Using Hyperspectral and Machine Learning 9.5.9 Papaya Fruits Using Machine Learning 9.5.10 Orange Classification and Grading Using Machine Learning 9.5.11 Machine Learning for Automatically Detecting and Grading Multiple Fruits 9.6 Drying of Fruit and Vegetables 9.7 Detection of Quality of Oil 9.7.1 Detection of Quality of Olive Oil 9.7.2 Detection of Extra Virgin Olive Oil Quality 9.7.3 Detection of Quality of Edible Oils 9.7.4 Detection of Quality of Peanut, Soybean, and Sesame Oils 9.7.5 Detection of Quality of Sesame Oil 9.7.6 Detection of Sesame Oil Quality With Sunflower Oil, Hazel Oil, and Canola Oil 9.8 Food Recognition and Classification 9.9 Food Adulteration 9.10 Sensometric and Consumer Science 9.11 Production and Prediction of Bioactive Compounds in Plants 9.11.1 Bioactive Compounds in Tomatoes 9.11.2 Bioactive Compounds of Grape Skins 9.11.3 Artificial Neural Network for Prediction of Bioactive Constituents in Plants 9.12 Food Contamination and Spoilage 9.13 Conclusion References Chapter 10 Study of Disruptive Technologies for Sustainable Agriculture 10.1 Introduction 10.2 Disruptive Technologies in Sustainable Agriculture 10.2.1 Artificial Intelligence and Machine Learning 10.2.2 Big Data Analytics 10.2.3 Geographic Information System 10.2.4 Robotics 10.2.5 Drone Technology 10.2.6 Remote Sensing 10.2.7 Digital Image Processing 10.2.7.1 Image-Based Insight Generation 10.2.8 Cloud Computing 10.2.9 Internet of Things 10.2.10 Blockchain Technology 10.2.10.1 Blockchain in Agriculture 10.2.10.2 Decision Support System 10.3 Framework of Agriculture 4.0 10.4 Applications of Sustainable Agriculture 10.5 Challenges 10.6 Cloud-Based IoT Architecture 10.7 Conclusion References Chapter 11 Role of Dimensionality Reduction Techniques for Plant Disease Prediction 11.1 Introduction 11.2 Dimensionality Reduction Techniques 11.2.1 Principal Component Analysis 11.2.2 Kernel Principal Component Analysis 11.2.3 Singular Value Decomposition 11.2.4 Locality Preserving Projection 11.2.5 Locally Linear Embedding 11.2.6 Isomap 11.2.7 Multidimensional Scaling 11.2.8 T-Stochastic Neighbour Embedding 11.3 Role of Dimensionality Reduction Techniques for Plant Disease Prediction 11.4 Opportunities and Challenges of Applying DRTs for Plant Disease Prediction 11.5 Conclusion References Chapter 12 A Review of Deep Learning Approaches for Plant Disease Detection and Classification 12.1 Introduction 12.2 Major Crops and Their Disease Detection Using Deep Learning in India 12.2.1 Cereal Crop Disease Detection Using Deep Learning Methods 12.2.2 Oilseed Crop Disease Detection Using Deep Learning Methods 12.2.3 Cash Crop Disease Detection Using Deep Learning Methods 12.3 Deep Learning Architectures and Models for Crop Disease Detection 12.4 Standard Datasets Used for Crop Disease Detection 12.4.1 PlantVillage Dataset 12.4.2 PlantDoc Dataset 12.4.3 Cropped-PlantDoc Dataset 12.4.4 Plant Disease Symptoms Image Database (PDDB) 12.4.5 Northern Corn Leaf Blight (NCLB) Dataset for Maize 12.4.6 New Plant Diseases Dataset (Augmented) 12.4.7 Rice Leaf Diseases Dataset 12.4.8 Image Set for Deep Learning 12.4.9 UCI Plant Dataset 12.4.10 Michalski’s Soybean Disease Database 12.4.11 Arkansas Plant Disease Database 12.4.12 One-Hundred Plant Species Leaves Dataset 12.5 Performance of Different Deep Learning Algorithms Used for Crops Disease Detection 12.5.1 Confusion Matrix 12.5.2 Classification Accuracy 12.5.3 Precision 12.5.4 Recall 12.5.5 F1-Score 12.6 Conclusion References Chapter 13 Cyber Threats to Farming Automation 13.1 Introduction 13.2 Farming Automation 13.3 Security in Farm Automation 13.3.1 Confidentiality 13.3.2 Integrity 13.3.3 Availability 13.4 Types of Cyber Threat in Farming Automation-Based Systems 13.4.1 Data Attacks 13.4.1.1 Ill-Intentioned Employee Data Leakage 13.4.1.2 Phishing Attack 13.4.1.3 False Data Injection Attack 13.4.2 Networking and Equipment Attacks 13.4.2.1 Radio Frequency Jamming Attack 13.4.2.2 Malware Injection Attack 13.4.2.3 Denial-Of-Service Attack 13.4.2.4 Side-Channel Attack 13.5 Artificial Intelligence and Machine Learning-Based Cybersecurity Use Cases 13.5.1 User Behaviour Modelling 13.5.2 Network Threat Identification 13.5.3 Email Monitoring 13.6 Future of Artificial Intelligence in Cybersecurity 13.7 Conclusion References Chapter 14 Prospects of Smart Farming as a Key to Sustainable Agricultural Development: A Case Study of India 14.1 Introduction 14.2 Smart Farming Tools for Future of Agriculture 14.3 Technological Advancements 14.4 Climate-Smart Agriculture 14.5 Evolution of Cutting-Edge Technologies That Are Revolutionizing the Agriculture Industry in India 14.5.1 Drones for Agriculture 14.5.2 Artificial Intelligence and Information Technology 14.5.3 Agricultural Mechanization 14.5.4 Agriculture Financing (AgriFin) Technology 14.5.5 Technology for Post-Harvesting 14.5.6 Animal Agriculture With Insight 14.5.7 Food and Agriculture Nanotechnology 14.5.8 Nanotechnology-Based Smart Pesticide Formulations 14.5.9 Microbe-Based Climate Smart Agriculture 14.5.10 Agriculture Smart Water Management Platform 14.5.11 Using High-Efficiency Sun Drying for Smart Agriculture 14.5.12 Cloud-Based Platform: Internet of Agriculture Things (IoAT) 14.5.13 Smart Multi-Sensor Platform in Agriculture Support for Analysis and Social Decision 14.5.14 Automation in the Agriculture Sector 14.6 How Can One Use Technology to Create Their Ideal Farmhouse? 14.6.1 Obtaining Weather Information 14.6.2 The National Agriculture Market (ENAM) 14.6.3 Unified Farmer Service Platform 14.6.4 Farmers’ Database 14.6.5 Benefits of the IoT in the Agricultural Sector 14.7 Smart Farming’s Obstacles 14.8 Some Examples of Smart Farming Applications 14.8.1 Aquaculture 14.8.2 Potatoes and Water Conservation 14.8.3 Lettuces That Can Benefit People With Renal Illness 14.9 Future Scopes and Challenges 14.9.1 Scopes 14.9.2 Challenges 14.10 Conclusion References Index