Agriculture 5.0: Artificial Intelligence, IoT and Machine Learning

Description

Agriculture 5.0: Artificial Intelligence, IoT & Machine Learning provides an interdisciplinary, integrative overview of latest development in the domain of smart farming. It shows how the traditional farming practices are being enhanced and modified by automation and introduction of modern scalable technological solutions that cut down on risks, enhance sustainability, and deliver predictive decisions to the grower, in order to make agriculture more productive. An elaborative approach has been used to highlight the applicability and adoption of key technologies and techniques such WSN, IoT, AI and ML in agronomic activities ranging from collection of information, analysing and drawing meaningful insights from the information which is more accurate, timely and reliable.It synthesizes interdisciplinary theory, concepts, definitions, models and findings involved in complex global sustainability problem-solving, making it an essential guide and reference. It includes real-world examples and applications making the book accessible to a broader interdisciplinary readership. This book clarifies hoe the birth of smart and intelligent agriculture is being nurtured and driven by the deployment of tiny sensors or AI/ML enabled UAV’s or low powered Internet of Things setups for the sensing, monitoring, collection, processing and storing of the information over the cloud platforms. This book is ideal for researchers, academics, post-graduate students and practitioners of agricultural universities, who want to embrace new agricultural technologies for Determination of site-specific crop requirements, future farming strategies related to controlling of chemical sprays, yield, price assessments with the help of AI/ML driven intelligent decision support systems and use of agri-robots for sowing and harvesting. The book will be covering and exploring the applications and some case studies of each technology, that have heavily made impact as grand successes. The main aim of the book is to give the readers immense insights into the impact and scope of WSN, IoT, AI and ML in the growth of intelligent digital farming and Agriculture revolution 5.0.The book also focuses on feasibility of precision farming and the problems faced during adoption of precision farming techniques, its potential in India and various policy measures taken all over the world. The reader can find a description of different decision support tools like crop simulation models, their types, and application in PA. Features: Detailed description of the latest tools and technologies available for the Agriculture 5.0. Elaborative information for different type of hardware, platforms and machine learning techniques for use in smart farming. Elucidates various types of predictive modeling techniques available for intelligent and accurate agricultural decision making from real time collected information for site specific precision farming. Information about different type of regulations and policies made by all over the world for the motivation farmers and innovators to invest and adopt the AI and ML enabled tools and farming systems for sustainable production. Cover Half Title Title Page Copyright Page Contents Authors List of Abbreviations 1. Introduction to Precision Agriculture 1.1. History of Precision Agriculture and Its Global Adoption 1.2. Precision Agriculture - Introduction 1.2.1. Foreign Perspective 1.2.2. Indian Perspective 1.3. Need and Scope of Precision Agriculture 1.4. Components of Precision Agriculture 1.4.1. Information 1.4.2. Technology 1.4.3. Management 1.5. Tools and Techniques 1.5.1. Global Positioning System (GPS) 1.5.2. Geographic Information System (GIS) 1.5.3. Wireless Sensor Networks 1.5.4. Agricultural Drones and Robots 1.5.5. Satellites 1.5.6. Precision Irrigation System 1.5.7. Software 1.5.8. Yield Monitoring 1.5.9. Online Platforms 1.5.10. Remote Sensing 1.6. Site-Specific Crop Management (SSCM) 1.7. Variable Rate Application (VRA) and Variable Rate Technology (VRT) 1.8. Adoption of Smart Precision Agriculture 1.8.1. Scope of the Adoption of Precision Agriculture in India 1.8.2. Strategy for the Adoption of Precision Agriculture in India 1.9. Some Misconceptions about Precision Agriculture 1.9.1. Hurdles Faced by Farmers in Adopting Precision Agriculture in India 1.9.2. Present Status of PA in India 1.9.2.1. Some Important Functions of PFDCs are Mentioned below: 1.9.3. Status of Precision Farming in Some Developing Countries 1.10. Conclusion References 2. Smart Intelligent Precision Agriculture 2.1. Modern Day Agriculture 2.2. Digitization of Agriculture - Digital Farming 2.3. Transition to Smart Intelligent Precision Agriculture 2.4. Benefits of Smart Intelligent Precision Agriculture 2.4.1 Effective Crop Management 2.4.2 Excellent Soil Management 2.4.2.1 Easy Remote Monitoring the Farm 2.4.3 Smart Intelligent Irrigation System and Water Quality Management 2.4.4 Intelligent Agricultural Robots 2.4.5 High Accuracy in Disease Prediction, Detection, and Control 2.4.6 Labor Challenge Mitigated 2.4.7 Leads Way from Precision Agriculture to Agriculture 5.0 2.4.8 Smart Intelligent Greenhouse 2.5. Conclusion References 3. Adoption of Wireless Sensor Network (WSN) in Smart Agriculture 3.1. Sensors and Wireless Sensor Network 3.1.1 Power Subsystem 3.1.2 Computation Subsystem 3.1.3 Communication Subsystem 3.1.4 Sensor Subsystem 3.1.5 Multimedia WSNs 3.1.6 Mobile WSNs 3.2. Evolution of Wireless Sensor Networks 3.3. Introduction of WSN in Agriculture 3.4. Features of Agriculturally Based Sensors 3.4.1. Communication Standards and Protocols 3.4.1.1 WiFi 3.4.1.2 Bluetooth 3.4.1.3 GPRS/3G/4G 3.4.1.4 WiMAX 3.4.2. Specific Hardware Requirements 3.4.3. Specific Software Requirements 3.5. Types of Sensors Used for WSN Agricultural System 3.5.1. Optical or Light Sensors 3.5.2. Electro-Chemical Sensors 3.5.3. Electro-Mechanical Sensors 3.5.4. Location or Proximity Sensor 3.5.5. Weather and Moisture Sensor 3.5.6. Vision and Imaging Sensors 3.5.7. Smartphone-Based Sensors 3.6. Intelligent Sensors versus Smart Sensors 3.7. Impact of the Wireless Sensors on Traditional Agriculture 3.8. Sensor Based Variable Rate Application 3.9. Applications of WSN in Precision Agriculture 3.9.1 Soil Analysis and Characteristics 3.9.2 Yield Sensing 3.9.3 Weed Management 3.9.4 Disease Detection and Classification 3.9.5 Irrigation Management 3.9.6 Greenhouse Management 3.9.7 Weather Monitoring 3.10. Security Issues and Challenges for WSN Implementation 3.11. Conclusion References 4. IoT (Internet of Things) Based Agricultural Systems 4.1. Introduction 4.1.1. Internet of Things (IoT) 4.1.1.1. What "THING" Refers to in an IoT 4.1.2. IoT Devices and Smart Objects 4.2. Architecture of IoT 4.2.1. Simplified Reference Model of IoT 4.2.2. Four-Stage Internet of Things Architecture 4.2.3. IoT Architecture Implemented in Agriculture 4.3. Brief Overview of IoT Network 4.3.1. ISO/OSI Model and Simplified ISO/OSI Model 4.3.2. Simplified ISO/OSI Model Layers 4.3.3. Standardization Bodies 4.3.4. Some IoT Network Technologies and Standards 4.4. Characteristics of Internet of Things 4.4.1. Various IoT Platforms for Smart Agriculture 4.4.2. The Hardware 4.4.3 Operating Systems 4.5. Inter-Operability Challenges 4.6. Applications of IoT in Smart Agriculture 4.7. Challenges for the Implementation of IoT in Smart Farming 4.8. Security and Privacy Issues of an IoT 4.8.1. Threat Types 4.9. Fusion of Cloud Platform with IoT 4.9.1. Integration of Big DATA into Smart Agriculture 4.9.2. Cloud Platform for Agricultural Big Data Storage 4.10. Conclusion References 5. AI (Artificial Intelligence) Driven Smart Agriculture 5.1. Artificial Intelligence (AI) - Introduction 5.2. Categories of AI 5.2.1. Type I (Based on Embedded Level of Intelligence) 5.2.2. Type II (Based on Functionalities) 5.3. Subsets of AI 5.3.1 Machine Learning 5.3.2 Deep Learning 5.3.3 Natural Language Processing 5.3.4 Expert System 5.3.5 Robotics 5.3.6 Machine Vision 5.3.7 Speech Recognition 5.4. Life Cycle of an Artificial Intelligence-Based Model 5.5. Prerequisites for Building an ML/AI-Based Agricultural Model 5.6. Advantages of AI in Agriculture 5.7. Conclusion References 6. Machine Learning (ML) Driven Agriculture 6.1. Cognitive Technologies 6.2. Introduction to Machine Learning 6.2.1. ML in Agriculture 6.2.2. ML in WSN and IoT 6.3. Types of ML 6.3.1. Supervised Learning 6.3.1.1. Decision Trees 6.3.1.2. Support Vector Machines (SVM) 6.3.1.3. Neural Networks 6.3.1.4. K-Nearest Neighbor (k-NN) 6.3.1.5. Bayesian Learners 6.3.2. Unsupervised Learning 6.3.2.1. Principal Component Analysis 6.3.2.2. K-Means Clustering 6.3.3. Semi-Supervised Learning 6.3.4. Reinforcement Learning 6.4. Artificial Neural Networks and Deep Learning 6.5. General Applications of Machine Learning 6.6. Scope of Artificial Intelligence and Machine Learning in Agriculture 6.7. Applications of AI and ML in Agriculture 6.7.1. Soil Management 6.7.2. Smart Irrigation System 6.7.3. Weather Forecasting 6.7.4. Agricultural Drones 6.7.5. Agricultural Robots 6.7.6. Tackling the Labor Challenge 6.7.7. Driverless Tractors 6.7.8. Crop Sowing 6.7.9. Crop Monitoring Systems 6.7.10. Deciding the Minimum Support Price (MSP) 6.7.11. Precision Agriculture to Agriculture 5.0 6.7.12. Greenhouse 6.8. Conclusion References 7. Data-Driven Smart Farming 7.1. Introduction 7.2. Collection and Management of Real-Time Agricultural Big Data 7.3. Transforming Field Data into Meaningful Insights 7.4. Processing and Predictive Analysis of Agricultural Data 7.4.1. Predictive Analysis Life Cycle and Types 7.4.1.1. Traditional Approach 7.4.1.2. Statistical Approach 7.4.1.3. Data Mining Approach 7.4.1.4. Classification and Regression Techniques 7.4.1.5. AI- and ML-Based Approach 7.5. Predictive Modeling 7.6. Conclusion References 8. Decision-Making and Decision-Support Systems 8.1. Introduction 8.2. Intelligent Agricultural Decision Support Systems (ADSS) 8.3. Features and Workings of an Intelligent Agricultural Decision Support System (ADSS) 8.4. Intelligent Decision-Making Using AI, ML, and IoT for Farmers 8.4.1. The Right Information at the Right Time for the Right Decision 8.4.2. Some Common Agricultural DSS 8.5. Conclusion References 9. Agriculture 5.0 - The Future 9.1. Introduction to Agriculture 4.0 9.2. Nanotechnology and Smart Farming 9.2.1 Applications of Nanotechnology in Agriculture 5.0 9.3. Blockchain-Securing the Agriculture Value Chain 9.3.1 Possible Applications of Blockchain in Agriculture 5.0 9.4. Edge-Fog Computing for Smart Farming 9.5. Role of Big Data in Agriculture 9.5.1. Introduction to Big Data 9.5.1.1. Defining Big Data 9.5.1.2. Big Data Life Cycle 9.5.2. Characteristics of Big Data (6 V's) 9.5.3. Types of Big Data 9.5.3.1. Some Other Types of Big Data 9.5.4. Advantages of Big Data 9.5.5. Few Applications of Big Data 9.5.6. Agricultural Big Data 9.5.7. Uses of Agricultural Big Data 9.6. Transition to Agriculture 5.0 9.7. Conclusion References 10. Social and Economic Impacts 10.1. Societal and Economic Impact of AI, ML, and IoT in Intelligent Precision Farming 10.2. Existence of Forums for Innovation and Commercialization of Intelligent Precision Farming Technology (IPFT) 10.2.1. Cost-Benefit Analysis of IPFT 10.2.2. Likeliness of Farmers towards the Technology ICAR-NAARM Policy 10.2.2.1. Farmers Perception and Concern 10.3. Conclusion References 11. Environmental Impact and Regulations 11.1. Potential Impact on the Environment with Different IPFT 11.2. Policy Making and Governance 11.2.1. Current Policy Trends and Regulation in India 11.2.2. Research and Development Needed in India 11.3. Conclusion and Future Perspective References Index