
Smart Technologies for Transforming Next-Generation Agriculture: Deep Learning, IoT and Blockchain
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Content
- Intro
- CONTENTS
- FOREWORD
- PREFACE
- List of Contributors
- Harvesting Innovation in Agriculture through IoT and Blockchain Technology
- Vipin Kumar1,*
- INTRODUCTION
- Overview of IoT and Blockchain technology
- The Role of Technology in Agricultural Innovation
- OBJECTIVES
- APPLICATIONS OF IOT AND BLOCKCHAIN IN AGRICULTURE
- Smart Irrigation System
- Crop Monitoring and Management
- Livestock Monitoring and Tracking
- Supply Chain Traceability
- Provenance Verification
- Smart Contracts for Agreements and Payments
- Data Management and Sharing
- BENEFITS OF IOT IN AGRICULTURE
- Improved Efficiency and Productivity
- Reduction in Resource Consumption
- Enhanced Crop Quality and Yield
- BLOCKCHAIN TECHNOLOGY FOR AGRICULTURE
- Blockchain and Distributed Ledger Technology
- Smart Contracts and Supply Chain Management
- INTEGRATION OF IOT AND BLOCKCHAIN IN AGRICULTURE
- CASE STUDIES OF IOT AND BLOCKCHAIN IMPLEMENTATION IN AGRICULTURE
- Case Study 1: Smart Farming with IoT and Blockchain
- Data Collection using Internet of Things
- Data Processing and Analysis
- Secure Storage on Blockchain
- Environmental Sustainability
- Case Study 2: Blockchain-based Food Traceability System
- Challenges in the Management of the Food Supply Chain at present
- The Solution from Blockchain
- Real-time bidding platform architecture
- Case Study 3: IoT-enabled Agricultural Finance Monitoring and Management
- Challenges in Agriculture finance Management
- Blockchain Solutions
- Case Study 4: Controlling Weather Crisis
- Objective
- Challenges
- Collaborative Platforms and Data Sharing in Agriculture
- CONCLUDING REMARKS
- REFERENCES
- Harvesting Innovation: Exploring Challenges, Risks, and Ethical Pathways in the Integration of Smart Technologies with Agriculture
- Pawan Kumar Mall1,*, Anuj Gupta2, Satyam Kumar Sainy1, Swapnita Srivastava1 and Vipul Narayan3
- INTRODUCTION
- TECHNOLOGICAL INNOVATIONS IN SMART AGRICULTURE
- Precision Farming
- Sensor Networks and IoT Devices
- Drones and Aerial Images
- Robotics and Automation
- ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
- CHALLENGES IN INTEGRATING SMART TECHNOLOGIES
- HIGH INITIAL COSTS
- TECHNICAL COMPLEXITY
- OTHER ISSUES ADDRESS BY RESEARCHERS
- RISKS ASSOCIATED WITH SMART AGRICULTURE
- Cybersecurity Threats
- Security Aspects of SF and PA
- Classification of Attacks on SF and PA
- ETHICAL PATHWAYS FOR RESPONSIBLE INTEGRATION
- Ensuring Inclusivity
- Promoting Data Ethics
- Sustainable Practices
- Fostering Collaboration
- Continuous Education and Training
- CONCLUDING REMARKS
- REFERENCES
- Revolutionizing Crop Disease Detection with Computational Deep Learning-based Techniques
- Ajaypal Singh1, Sukhdeep Singh1 and Kanu Goel1,*
- INTRODUCTION
- BACKGROUND
- Overview of Crop Diseases
- LITERATURE REVIEW
- DL-based Approaches
- ML-based Approaches
- Hybrid Approaches
- PRACTICAL APPLICATION: CROP DISEASE CLASSIFICATION
- Dataset Description
- Dataset Preprocessing
- Image Enhancement
- Image Augmentation
- MODEL ARCHITECTURES
- DenseNet121
- MobileNet
- EfficientNetB7
- Training Procedures
- Evaluation Metrics
- RESULTS AND DISCUSSION
- Performance Evaluation
- Comparative Analysis
- DISCUSSION
- CONCLUDING REMARKS
- REFERENCES
- Nanotechnology in Agriculture: A New Frontier
- Neeraj Dhariwal1, Meenu Chahar2, Preety Yadav1, Akanksha1 and Vinod Kumar1,*
- INTRODUCTION
- NANOTECHNOLOGY AND SMART AGRICULTURE
- Zinc Oxide Nanoparticles
- Silver Nanoparticles
- Silica Nanoparticles
- Titanium Dioxide
- NANO SENSORS
- Electrochemical Sensor
- Optical Sensor
- E-NOSE AND E-TONGUE
- NANO FERTILIZERS
- Macro Nanofertilizers
- Micronutrient Nanofertilizers
- Nano Pesticides
- Nanomaterials as Active Ingredients
- Nanoparticles as Biopesticides
- Nanoparticles as Fungicides
- CONCLUDING REMARKS
- REFERENCES
- Harnessing Smart Technology for Optimized Livestock Farming: A Comprehensive Overview
- Jaspal Kaur Saini1,* and Sanjay Madan2
- INTRODUCTION
- LITERATURE REVIEW
- SIGNIFICANCE OF LIVESTOCK FARMING
- TECHNOLOGIES FOR LIVESTOCK FARMING
- ROLE OF IOT AND MACHINE LEARNING IN LIVESTOCK FARMING
- IoT and Livestock Farming
- Machine Learning and Livestock Farming
- Machine Learning Algorithms for Optimised Livestock Farming
- RESEARCH CHALLENGES
- CONCLUSION
- REFERENCES
- Blockchain and IoT for Smart Agriculture: Enhancing Food Security and Sustainability
- Ruchi Mittal1,*
- INTRODUCTION
- Internet of Things (IoT) in Industry 4.0
- Blockchain and Explainable AI in Smart Agriculture
- LITERATURE REVIEW
- IoT and Blockchain in the Food Industry
- LAYERS OF IOT IN THE FOOD INDUSTRY
- Real-Life Case Studies in Smart Agriculture
- Challenges in IoT-Based Smart Agriculture
- Hardware Challenges
- Interoperability Challenges
- Networking and Energy Management
- Educational Challenges
- Cost Challenges
- Security Challenges
- CONCLUDING REMARKS
- REFERENCES
- Enhancing the Tomato and Potato Crop Health: A Novel Approach for Ensemble Deep Learning based Disease Classification
- Ekamdeep Singh1,* and Tamanna Sood1
- INTRODUCTION
- BACKGROUND
- Overview of Plant Diseases
- Literature Review
- Potential Diseases in Potatoes
- Potential Diseases in Tomatoes
- MATERIAL AND METHODS
- Dataset Description
- Dataset Composition
- Proposed Methodology
- Data Pre-processing and Data Augmentation
- Classification Models
- Proposed Ensemble Model
- Implementation Details and Evaluation Metrics
- Implementation Details
- Evaluation Metrics
- RESULTS AND DISCUSSION
- RESULTS
- DISCUSSION
- CONCLUSION
- REFERENCES
- The use of Big Data, Deep learning, IoT, and Blockchain in Livestock Management
- Indu Sounkhla1,* and Renu Dhir1
- INTRODUCTION
- Overview of Livestock Management Practices
- Significance of Integration of Technology in Livestock Management
- Objectives of Chapter
- BIG DATA IN LIVESTOCK MANAGEMENT
- Understanding Big Data in Agriculture
- Data Collection Techniques in Livestock Farming
- Utilizing Big Data Analytics for Improved Livestock Management
- Deep Learning Applications in Livestock Management
- Introduction to Deep Learning (DL) and Neural Networks
- Deep Learning Models for Predictive Analysis in Livestock Health
- Implementation of Deep Learning in Livestock Behavior Monitoring
- IOT IN LIVESTOCK FARMING
- IoT Devices and Sensors for Livestock Monitoring
- Real-time Data Collection and Analysis of Environmental Conditions and Animal Health
- IoT-Enabled Solutions for Precision Livestock Farming
- BLOCKCHAIN TECHNOLOGY IN LIVESTOCK MANAGEMENT
- Introduction to Blockchain and its Features
- Application of Blockchain in Livestock Traceability
- Enhancing Transparency and Trust in Livestock Supply Chains with Blockchain
- Integration of Big Data, Deep Learning, IoT, and Blockchain in Livestock Management
- Case Studies Demonstrating the Integration of Advanced Technologies in Livestock Management
- Case Study 1: The Netherlands' Intelligent Dairy Farming
- Case Study 2: Australia's Integrated Livestock Management
- CHALLENGES AND CONSIDERATIONS IN LIVESTOCK FARMING
- CONCLUSION AND FUTURE DIRECTIONS
- REFERENCES
- Next-Gen Precision Agriculture: Integrating AI, IoT, and D2D Communications
- Bharti Sandhu1,* and Prashant Kumar1
- INTRODUCTION
- TECHNOLOGICAL BACKGROUND
- Overview of AI, IoT, and D2D Technologies
- Artificial Intelligence in Agriculture
- Internet of Things (IoT) in Agriculture
- Device-to-Device (D2D) Communication Basics
- Interplay Between AI, IoT, and D2D
- CURRENT APPLICATIONS AND CASE STUDIES
- AI-Driven Monitoring Systems
- IOT NETWORKS FOR REAL-TIME DATA COLLECTION
- Sensor Networks
- Data Acquisition and Management
- D2D Communication for Enhanced Connectivity
- Case Studies: Successful Applications
- ENHANCING REAL-TIME RESPONSE AND DECISION-MAKING
- INTEGRATION CHALLENGES AND SOLUTIONS
- Interoperability and Compatibility Issues
- Security and Privacy Concerns
- Resource Constraints
- Proposed Architectural Solutions
- Framework for Integration
- Standardization and Best Practices
- FUTURE DIRECTIONS AND EMERGING TECHNOLOGIES
- The Role of 5G and Beyond in Agriculture
- Advanced AI Techniques for Predictive Analytics
- Machine Learning Models
- Deep Learning Applications in Precision Agriculture
- CONCLUSION AND FUTURE WORK
- REFERENCES
- The Role of Big Data and Deep Learning in Crop Management
- Narendra Kumar1,*, Shefali Arora1 and Shashank Gupta1
- INTRODUCTION
- Big Data in Crop Management
- Data Collection Techniques
- Data Integration and Management
- Role of Big Data in Crop Management
- Role of Deep Learning in Crop Management
- Objectives of the Chapter
- FOUNDATIONS OF BIG DATA AND DEEP LEARNING IN CROP MANAGEMENT
- SUSTAINABILITY WITH BIG DATA AND DEEP LEARNING
- APPLICATION OF BIG DATA AND DEEP LEARNING IN CROP MANAGEMENT
- CHALLENGES AND CONSIDERATIONS OF BIG DATA AND DEEP LEARNING IN CROP MANAGEMENT
- POST-HARVEST AND SUSTAINABILITY ASPECTS
- Post-Harvest Management:
- Sustainability Aspects
- SECURITY AND VULNERABILITIES IN BIG DATA AND DEEP LEARNING FOR CROP MANAGEMENT
- CASE STUDY
- Case Study 1: "Crop Management Using Big Data in Semi-Arid Environ-ments" (Sayad et al., 2015 [2])
- Objective
- Implementation
- Results
- CONCLUSION AND FUTURE WORK
- REFERENCES
- AI-Driven Cybersecurity in Agriculture: The Future of Farming
- Shikha Gupta1,*, Nishi Gupta2 and Lakshay Aggarwal3
- INTRODUCTION
- THE PRESENT PANORAMA OF AGRICULTURE AND CYBERSECURITY
- Common Cyber Threats in the Agriculture Sector
- Distributed Denial of Service (DDoS) Attacks [11]
- Phishing Attacks [12]
- Man-in-the-Middle (MitM) Attacks
- Spoofing Attacks
- Unauthorized Access
- ROLE OF AI IN CYBERSECURITY
- Approaches of AI in Cyber Security
- Cybersecurity Challenges in AI
- Vulnerabilities of Cyber-Physical Systems
- Risks in the Supply Chain and Ecosystem
- Ensuring the Accuracy of Data and Handling Data Manipulation
- Impacts on the Economy
- Compliance with Regulations and Legal Hurdles
- Mistakes Made by Individuals and Risks Posed by Insiders
- The Rise of Cyber-Risks
- Industry Focused Solutions
- Cybersecurity Skill Gap
- AI-DRIVEN SOLUTIONS FOR CYBER SECURITY IN AGRICULTURE
- Analyzing Data to Anticipate and Prevent Potential Risks
- Automated Incident Response
- Improved Methods of Verifying Identity and Restricting Entry
- Detecting Anomalies in Internet of Things (IoT) Networks
- Using Machine Learning to Identify Malicious Software
- AI to Ensure the Accuracy of Data
- Training in Cybersecurity Powered by Artificial Intelligence
- Utilising Blockchain and AI to Guarantee Secure Transactions
- Adaptive and Flexible Security Frameworks
- CURRENT STATE OF SECURITY IN SMART AGRICULTURE
- CONCLUDING REMARKS
- REFERENCES
- Subject Index
Harvesting Innovation in Agriculture through IoT and Blockchain Technology
Vipin Kumar1, *
1 Lovely Professional University, Phagwara, Punjab, India
Abstract
Innovative technologies like IoT and blockchain transform businesses and other agricultural work. Emerging technologies like artificial intelligence and big data have unlocked new possibilities for operational efficiency and achieved the highest level of productivity through informed decision-making. Using the Internet of Things technologies, sensors transmit real-time data regarding the environment's state, crops' growth, and animals' well-being. Consequently, the information they gain facilitates them in making better decisions, where resources can be utilized to their maximum and output raised without compromising waste. Blockchain technology provides a solution for building agricultural supply chains with integrity and transparency. In this chapter, we explore how integrating blockchain technology with IoT in agriculture can unlock unexplored proficiency, sustainability, and robustness. It utilizes case studies and examines innovative irrigation systems, crop monitoring, and animal management applications. This chapter will explore the various methods of data collection from Internet of Things sensors to analyze and gain insight for more accurate decision-making processes. By examining the data accumulated over several years, business management can develop goals that aim to improve production measures, such as efficiency and effectiveness. The analysis of various aspects of the agricultural industry and the application of blockchain technology demonstrates how it can improve processes by streamlining operations and making the industry more transparent. It examines the identification and effectiveness of data-driven decisions in crop management. The yield data analysis from production is the basis for continuing the growth of the agricultural industry.
Keywords: Agriculture innovation, Blockchain technology, Capital monitoring, Crops management, Internet of things, Smart farming.* Corresponding author Vipin Kumar: Lovely Professional University, Phagwara, Punjab, India; E-mail: vipin.17730@lpu.co.in
INTRODUCTION
Innovative technologies like IoT and blockchain, which are relatively new, have been used to modernize the agriculture industry. As a result, a new era of agricultural transformation has emerged. This way of farming utilizes innovative
breakthroughs, such as advanced accuracy, transparency, and efficiency, from the fields through processing plants to the end consumer. Therefore, farming techniques have been revolutionized for generations. The Internet of Things (IoT) enables sensors in fields, machinery, and livestock to provide us with real-time data on humidity, crop health, and livestock health. Through this provision of a coherent system, the farmer's decisions are informed, hence optimizing the available resources. Blockchain technology ensures that the integrity and traceability of agricultural goods from obtaining them from the farms to the consumer's table is preserved, thus cultivating trust and responsibility in the food supply chains. The agricultural revolution shall create more abilities that meet users' and the planet's needs by applying the combined forces of the Internet of Things (IoT) and Blockchain (BC) technologies. Through this, the future holds a highly informed, unambiguous, and fair agricultural sector. Scratching the paper-based agricultural bookkeeping approach, an old method of recording farm activities, is merely a child's game with this approach. The fact that this is the most dynamic stage of agricultural industry development almost deprives me of control. First, the Internet of Things (IoT) and the blockchain will play a crucial role in the current technological revolution. Undoubtedly, the past decade of technology has been a testament to its success, as it has turned all constraints to dust and touched every segment of society. Now, these two innovations will lay the foundation for a bright future in technology. Regardless of whether it's in engineering, health industries, computer science, or anything at all, their distinctive impressions pervade every sphere in which they are active [1].
Overview of IoT and Blockchain technology
The introduction of new technology, integrating IoT and blockchain, in the agricultural sector has resulted in the creation of various approaches to farming processes. Sensors, drones, and intelligent machines make age-old agriculture technologies contemporary by acquiring data instantaneously about soil moisture, temperature, crop health, and livestock. This data allowed them to drive more precise decisions and resources and significantly reduce the amount of waste and the negative environmental impact [2].
The Internet of Things (IoT) has always been the fastest-discussed and searched trend in the present era. The IoT foundation serves as the "connection" between various IT sector devices within the IoT vision. Furthermore, dimension one points us to intelligent communication and smart metering. Accepting this notion's vision would create room for the development of a significant agricultural subsidiary, which would imply change and additional possibilities for innovation and expansion. With contributions from IoT and resilient sensors that can accumulate large amounts of data, enabling the optimization of production processes, the prosperity of agriculture is advancing rapidly toward a greener and cleaner solution. Automated systems can create vast amounts of high-dimensional information to gain a complete understanding of our system. For instance, Uncrewed Aerial Vehicles (UAVs) can capture images that provide real-time information on crop growth. We can pinpoint particular components of the area that may require more attention. These devices can record enormous amounts of data, which, if comprehended, can result in various desirable outcomes. The data extracted from the Internet of Things typically consists of a substantial volume of information, necessitating the utilization of specific methods to be of any use [3].
As technologies continue to advance and people utilize them more, they have enormous potential to transform agriculture and positively impact farmers' lives worldwide. Smart devices and connectivity inculcation can allegedly be achieved in two ways concerning the Internet of Things in agriculture. For example, you can download weather information from the internet and have intelligent IoT devices implement it locally, update it, and automatically adjust the watering schedule. In this regard, robot technologies intended to observe crops and deliver information on the consumption of chemicals, such as pesticides or fertilizers, through the IoT network will operate autonomously. Eliminating the possibility of human error and providing data input with 100% accuracy is a fantastic result of the standard machine learning ability to teach algorithms how to process image data. One phrase can summaries all these: Boosting the product while consuming less water [4].
Blockchain technology is a genius solution for transparency, traceability, and security in the agricultural supply chain, as it helps maintain a record of every possible movement and transfer of farm products. These records are immutable, as illustrated in Fig. (1). This is demonstrated by the fact that customers can find the source of their food and track its quality. It is a distributed database shared among a network and uses encryption to ensure security. Blockchains are becoming increasingly popular. Its design does not allow any changes to be made to the data it stores, making it highly secure and auditable. The cryptocurrency known as Bitcoin, which was initially invented, has the potential to be utilised in a broad range of various businesses.
Fig. (1))Blockchain and block data.
The role of blockchain technology in existing agricultural systems makes it vital for us to choose it to produce the best results in the problem-solving work that farmers face daily, particularly in terms ofinancial issues [5].
The Role of Technology in Agricultural Innovation
The Internet of Things (IoT) and Blockchain technology are an impetus to taking agricultural innovation to the next level. Enabling IoT devices in agricultural production has launched possibilities for tracing all critical indicators, including soil moisture, crop health, and weather conditions, in real-time. This information allows farmers to make more reasonable decisions and optimize resource and power consumption, creating more yields with less waste. Also, blockchain technology sustains the availability of information, security, and traceability in the agricultural shipping system by ensuring transaction records and product movements cannot be changed [2]. This levels the playing field and conveys reliable information to stakeholders, permitting customers to trace the source and evaluate the quality of farm produce. Leveraging IoT and blockchain functionalities becomes another powerful driver for...
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