AI, Edge and IoT-based Smart Agriculture
- Length: 574 pages
- Edition: 1
- Language: English
- Publisher: Academic Press
- Publication Date: 2021-11-26
- ISBN-10: 0128236949
- ISBN-13: 9780128236949
- Sales Rank: #5155580 (See Top 100 Books)
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.
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 Edge node 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
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