AI, Edge and IoT-based Smart Agriculture (Intelligent Data-Centric Systems: Sensor Collected Intelligence)

✍ Scribed by Ajith Abraham (editor), Sujata Dash (editor), Joel J.P.C. Rodrigues (editor), Biswaranjan Acharya (editor), Subhendu Kumar Pani (editor)

• Publisher: Academic Press
• Year: 2021
• Tongue: English
• Leaves: 531
• Edition: 1
• Category: Library

✦ Synopsis

AI, Edge, and IoT Smart Agriculture integrates applications of IoT, edge computing, and data analytics for sustainable agricultural development and introduces Edge of Thing-based data analytics and IoT for predictability of crop, soil, and plant disease occurrence for improved sustainability and increased profitability. The book also addresses precision irrigation, precision horticulture, greenhouse IoT, livestock monitoring, IoT ecosystem for agriculture, mobile robot for precision agriculture, energy monitoring, storage management, and smart farming. The book provides an overarching focus on sustainable environment and sustainable economic development through smart and e-agriculture.

Providing a medium for the exchange of expertise and inspiration, contributions from both smart agriculture and data mining researchers around the world provide foundational insights. The book provides practical application opportunities for the resolution of real-world problems, including contributions from the data mining, data analytics, Edge of Things, and cloud research communities working in the farming production sector. The book offers broad coverage of the concepts, themes, and instruments of this important and evolving area of IOT-based agriculture, Edge of Things and cloud-based farming, Greenhouse IOT, mobile agriculture, sustainable agriculture, and big data analytics in agriculture toward smart farming.

✦ Table of Contents

Front matter
Copyright
Contributors
Internet of things (IoT) and data analytics in smart agriculture: Benefits and challenges
Introduction
Understanding AI
IoT ecosystem in agriculture
Management techniques/systems (IoT and big data)
Smart information systems (SIS) in agriculture
Benefits of IoT in agriculture
Remote sensing as a major tool in agriculture
Weather forecasting as a prime IoT in agriculture
Agriculture drones
Crop monitoring
Smart irrigation
Greenhouse monitoring and automation system
Open issues and key challenges in the adoption of IoT in agriculture
Reliability
Data privacy protection and issues of ownership
Autonomy foreseeability and causation
Control
Opaque research and development
Legal issues in regulating AI in agriculture
Torts and contracts
Crimes
Law relating to accidents, health, and safety
Accidents and negligence
Environmental laws
Conclusion
References
Edge computing-Foundations and applications
Introduction
Edge computing
Applications of edge computing
Future trends of edge computing
Conclusions
References
IoT-based fuzzy logic-controlled novel and multilingual mobile application for hydroponic farming
Introduction
Literature review
Methodology
Proposed method
Results and discussion
Conclusion
References
Functional framework for IoT-based agricultural system
Introduction
Overview of the cases
Challenges, opportunities, and use of IoT applications in agriculture
Challenges
Software complexity
Security
Technical skill requirement
Lack of supporting infrastructure
Opportunities allied with the solicitation of IoT in agriculture
Low-power wireless sensor (LPS)
Better connectivity
Operational efficiency
Remote control management
The architecture of a smart farm monitoring system
Energy-saving technologies
Security mechanisms
Advantages of IoT in agriculture system
Climate conditions or agility
Precision farming
Smart greenhouse
Data analytics
Agricultural drones
Limitations of the existing proposed model
Methodology
Block diagram of proposed model
Flow diagram of controlling process of motor using sensors
IoT with transmitter and receiver wireless sensor model
Experimental results and discussion
Experimental work
Thingspeak cloud server
Results
Measurements at 14:30 when soil is dry
Measurements on May 14, 2020; time varies when soil is wet
Measurement in night, when soil is dry
Measurement in night, when soil is wet
Discussion
Conclusion and future scope
Future scope
References
Functional framework for edge-based agricultural system
Introduction
Relevant technologies
Edge computing in agricultural sectors
Role of edge computing in multiple facets of agriculture
Smart farming
Aquafarming
Livestock
Dairy farming
Hydroponics
Edge computing framework design in agriculture
Communication
Low range wide area network protocol (LoraWan)
Message Queue-Telemetry Transport Protocol (MQTT)
Radio Frequency Identification (RFID)
SigFox
Zigbee
WiFi
Bluetooth
Worldwide Interoperability for Microwave Access (WiMAX)
Routing Protocol for Low-Power and Lossy Networks (RPL)
Processing/computation
Analytics
Storage
Local
Edge/Cloudlet
Cloud
Actuation
Sensing
Edge computing implementation
Hardware implementation
Data communication technologies
Data processing implementation
Experimental set-up of edge-based agricultural system
Edge node 1
Edge node 2
Edge server
Cloud server
Conclusion
References
Precision agriculture: Weather forecasting for future farming
Introduction
Terminologies employed in precision agriculture
Application map
Class post mapping
Georeferencing
Geographical information systems (GIS)
Global positioning systems (GPS)
Grid sampling
Kriging
Management zone
``On-the-go´´ sensing
Pixel
Precision farming
Remote sensing
Scouting
Smoothing
Spatial resolution
Variable rate technology (VRT)
Yield map
Yield monitor
Connection between precision agriculture and traditional agriculture
Information
Technology
Decision support
Weather and climate
Weather
Climate
Tropical climate
Arid climate
Mediterranean climate
Humid climate
Arctic climate
Highland climate
Agricultural implications of climate change
Reducing the burden of agriculture on climate change
Exploring the climate change influence as an influential element in agricultural productivity
Modern tools and techniques for precision agriculture
Internet of Things (IoT)
Sensor technology
Unmanned aerial vehicles (UAVs)
Unmanned ground vehicles (UGVs)
Robots
Smartphone
Autoguidance equipment (AGE)
Variable rate technology
Grid sampling
Conclusion
References
Crop management system using IoT
Introduction
Background and related works
Proposed model
Methodology
Performance analysis
Future research direction
Conclusion
References
Smart irrigation and crop security in agriculture using IoT
Introduction
Overview
Applications
Motivation
Objectives
Methodology
Basic building blocks of an IoT device
Components used
Node MCU
PIR motion sensor
Buzzer
Raspberry Pi camera
Algorithms
Design flow
Implementation
System process
Testing and results
Conclusion and future scope
References
The Internet of Things in agriculture for sustainable rural development
Introduction
Literature survey
Present scenario
National perspective
International perspective
Background details
Internet of Things
History of the IoT
IoT devices
IoT in agriculture for rural development
Significance in agriculture
Benefits of IoT in agriculture
Applications of IoT in agriculture
IoT in agriculture: Use cases
Case studies: IoT-based agriculture for sustainable rural development
Slashing water consumption in avocado
Smart dairy farming
Impact of IoT on food sustainability and socioeconomic uplift
Food sustainability
Socioeconomic uplift
Challenges and opportunities
Unstable Internet connection in farms
Disrupted connectivity to cloud servers
Costly hardware
Conclusion
References
Internet of Things (IoT) in agriculture toward urban greening
Introduction
IOT architectures
Definitions of IoT
G-IoT
Architecture of IoT
G-IOT application
Green tags
Green sensing networks
Green Internet technologies
IOT applications
Smart industrial plants and machine-to-machine communications
Smart plant monitoring
Smart data collection
Smart sensing
Smart sports
Smart social networks
Smart agriculture
Smart waste
Smart environment
Smart grid
G-IOT challenges and opportunities
Green infrastructure
Green spectrum management
Green communication
Green security and management
Conclusion
References
Smart e-agriculture monitoring systems
Introduction
Need for smart e-monitoring system for agriculture
System architecture
WSN-based architecture
IoT-Cloud based architecture
IoT and data analytics in agriculture
Devices deployed
Data acquisition
Data processing
Data analytics
Different types of solutions available
Botanicalls
Parrot flower power
HarvestGeek
Open garden
Automated hydroponics: Bitponics
Edyn
Koubachi
Research challenges
Case study on IoT-based monitoring systems
Case study 1: IoT-based greenhouse crop production
Case study 2: IoT-based plant disease prediction
Case study 3: IoT-based vineyard monitoring
Case study 4: IoT-based irrigation management
Open research issues
Conclusion
References
Smart agriculture using renewable energy and AI-powered IoT
Introduction
Background and related work
VAWT
Data analytics platform
IoT devices
Single board computer
Microcontrollers
Sensors
Smartphone application
Concept
Suburban set-up
Rural set-up
Architecture and system design
Components of the system
Source power unit
Auxiliary system
Single board computer-master controller
Microcontroller-slave to the SBC
Automated irrigation
Wireless sensor network
Lighting provisions
System management application
Concept model
Renewable energy
IoT
Cloud technology
Data analytics
Machine learning
User operability
Ideal scenario
Input from the user
Output to the user
Application
Farming analytics
Monitoring systems
Automated irrigation
Advantages
Availability
Economical
Renewable source
Efficiency of crop growth
Holistic supply chain management
Limitations
Structural implementation
Safety concerns
Government support
Connectivity
Maintenance requirement
References
Smart irrigation-based behavioral study of Moringa plant for growth monitoring in subtropical desert climatic
Introduction
Moringa oleifera as a miracle plant
Properties of Moringa oleifera
Medicinal value and health benefits
Moringa oleifera leaves
Moringa oleifera seeds
Moringa oleifera root
Moringa oleifera flower
Moringa oleifera as animal fodder
Moringa oleifera in water purification
Favorable climatic condition
Growth pattern in arid and semiarid areas
Challenges in subtropical climatic conditions
Motivation and challenges
Plant and smart technology
Technology used
Arduino UNO
Relay
Soil moisture sensor
Water pump
Methodology
Flowchart
Limitations and area of improvement
Conclusion
References
Surveying smart farming for smart cities
Introduction
Smart farming history
Smart farming and future trends
Conclusions
References
Farm automation
Introduction
Current trends in smart farming automation systems
Architecture of edge computing and IoT (E-IoT) platform
FAR-edge RA
Edge computing reference architecture 2.0
Industrial Internet Consortium reference architecture
INTELSAP reference architecture
Global edge computing reference architecture
Applications of E-IoT in farm automation
E-IoT in weed detection
Smart irrigation system with E-IoT
E-IoT in livestock management
Farm security solution with E-IoT
Data security and privacy
Authentication
Authorization
Denial of Service (DOS)
E-IoT in food safety
Discussion
Future challenges
Conclusion
References
A fog computing-based IoT framework for prediction of crop disease using big data analytics
Introduction
Fog computing
Fog computing vs cloud computing
IoT in agriculture
Smart crop disease prediction
Benefits of IoT in agriculture
Agility
Increased efficiency
High productivity
Automation
The role of fog computing in IoT
IoT-fog integration in crop disease prediction
IOT in crop disease
IoT agricultural framework
Device layer
Network layer
Service layer
Application layer
A fog computing-based IOT framework for predicting crop disease
Proposed model
Information needed to predict disease accurately
Map-reduce based prediction model
Conclusion and future work
References
Agribots: A gateway to the next revolution in agriculture
Introduction
Specific examples of how agribots could be integrated into a regional IoT-enabled single window for improving collecti ...
Conclusion
References
SAW: A real-time surveillance system at an agricultural warehouse using IoT
Introduction
Issues and challenges with a traditional monitoring system in agriculture
The possibilities of IoT as an alternate to conventional agriculture
Components used with specifications and applications for IoT-enabled agriculture system
Temperature sensor and humidity sensor
Flame sensor and smoke sensor
Main controller
NRF24L01 transceiver module
GSM communication module
Earthquake sensor
Buzzer
IoT-enabled autonomous agriculture model (SAW)
Performance analysis
Conclusion
References
The predictive model to maintain pH levels in hydroponic systems
Introduction
Hydroponics system discussion
Macronutrients
Micronutrients
NFT channels
DWC hydroponics
Drip system
Aeroponics
pH management automation
Data collection
Correlation analysis
Dataset
Model generation
Final set-up
Hydroponics automation
Automated factors
Light and darkness availability
TDS level
pH level
Oxygen level in water
Major advantages of hydroponics
Major disadvantages of hydroponics
Conclusion and future scope
References
A crop-monitoring system using wireless sensor networking
Introduction
Background and related works
Proposed model
NodeMCU (node microcontroller unit)
Passive infrared sensor (PIR sensor)
Temperature and humidity sensor
pH sensor
UAV
RGB-D sensor
ThingSpeak
Methodology
Performance analysis
Future research direction
Conclusion
References
Integration of RFID and sensors in agriculture using IOT
Introduction
Background and related works
System design and architecture
Methodology
Future research direction
Conclusion
References
Prediction of crop yield and pest-disease infestation
Introduction
Crop yield forecasting models
Time series models
Linear and nonlinear time series models
Linear time series models
ARIMA models
ARIMAX
Exponential smoothing models
Nonlinear time series models
ARCH models
GARCH models
Artificial neural networks
Support vector machine
Wavelet-based models
Hybrid models
Brock-Dechert-Scheinkman test
McLeod and Li test
Hybrid methodology
Bayesian forecasting approach
Weather-based crop yield forecasting models
Models using composite weather variables
Model using discriminant function analysis
Models using water balance technique
Crop growth simulation models
Pest and disease forewarning systems
Between-year models
Thumb rule
Multiple linear regression models
Fuzzy linear regression models
Principal component regression models
Growing degree day approach
Weather indices-based models
Discriminant function analysis
Complex polynomial approach
Machine learning techniques
ANN regression
Rough set-based decision tree
Deviation method
Ordinal logistic model
Within-year models
Loss of crop yield due to pest and disease outbreak
Conclusion
References
Machine learning-based remote monitoring and predictive analytics system for crop and livestock
Introduction
Background study
Framework for remote monitoring and predictive analysis using ML
Benefits of the work
Research challenges
Role of AI and machine learning for crop monitoring
Reported work
Wheat
Rice
Soybean
Other crops
Comparative analysis
Conclusion
References
Exploring performance and predictive analytics of agriculture data
Introduction
Literature survey
The need for data processing
Big data characteristics
Techniques and tools for big data processing
Advantages of data analysis in agriculture
Classical approach of farming process
Smart farm management
Advantages of smart farming
Analysis of usefulness of various smart farming techniques
Agricultural big data mining
Study of agricultural sector using mobile apps
Study of vegetable production using hydroponics
Comparative approach of implementation mechanisms
Literature survey on algorithms
Comparative approach of the various techniques
Methodology
Study of datasets
Crop production dataset overview
Technique used in data mining
Clustering
Simple K-means
Data isualization
Fertilizers datasets overview
Classification
Result and analysis
Concerns and conclusions
References
Further reading
Climate condition monitoring and automated systems
Introduction
Impacts of climate
Climate impacts on environment
Climate impacts on health
Climate impacts on agriculture
Climate monitoring systems and automation
Recent developments on applications of climate monitoring systems in environment, health, and agriculture
Conclusion and future work
References
Decision-making system for crop selection based on soil
Introduction
Machine learning role in agriculture: A review
Soil health and crop production
Experiment and analysis
Data development
Soil properties dataset
Soil properties
Physical properties
Chemical properties
Biological properties
Properties assumption
Numerical properties assumptions
Categorical properties assumptions
Soil dataset version assumption
Soil health recommendations data
Machine learning prediction algorithms
Performance metrics
Prediction algorithm implementation and performance outcomes
Prediction comparative analysis
Crop selection and soil health recommendation system
Conclusion and challenges
References
Cyberespionage: Socioeconomic implications on sustainable food security
Introduction
Conclusion
References
Internet of Things on sustainable aquaculture system
Introduction
Internet of Farming Things
Internet of Things on sustainable aquaculture system
Conclusions
References
IoT-based monitoring system for freshwater fish farming: Analysis and design
Introduction
Relevance of monitoring devices in freshwater fish farming (including IoT and smart monitoring systems)
IoT-based monitoring production: Feasibility, requirement planning, analysis, and design
Building IoT infrastructure for monitoring production: Feasibility, requirement planning, analysis, and design
Conclusions and recommendations
References
Transforming IoT in aquaculture: A cloud solution
Introduction
Cloud computing in IoT
Types of clouds
Benefits of cloud platform in IoT
Integration of cloud computing and IoT
Architecture
Cloud platforms
Cloud-based IoT monitoring aquaculture system
Security
Cloud-IoT architecture in shrimp aquaculture
Introduction of wireless sensor networks (WSN)
WSN architecture for aquaculture
Monitoring of water quality with the help of WSN
Challenges
Future trends and conclusion
References
Toward the design of an intelligent system for enhancing salt water shrimp production using fuzzy logic
Introduction
Specific examples of intelligent systems for enhancing salt water shrimp production using fuzzy logic
Conclusion
References
Index