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E-raamat: AI, Edge and IoT-based Smart Agriculture

Edited by , Edited by (Vice-Chancellor, Sai University, Chennai, India), Edited by (Federal University of Piaui (UFPI), Teresina - PI, Brazil; Instituto de Telec), Edited by , Edited by (Department of Information Technology, School of Engineering and Technology, Nagaland University, India)
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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.

  • Integrates sustainable agriculture, Greenhouse IOT, precision agriculture, crops monitoring, crops controlling to prediction, livestock monitoring, and farm management
  • Presents data mining techniques for precision agriculture, including weather prediction, plant disease prediction, and decision support for crop and soil selection
  • Promotes the importance and uses in managing the agro ecosystem for food security
  • Emphasizes low energy usage options for low cost and environmental sustainability
Contributors xxi
SECTION 1 IoT and edge foundations and framework
Chapter 1 Internet of things (IoT) and data analytics in smart agriculture: Benefits and challenges
3(14)
Biswaranjan Acharya
Kyvalya Garikapati
Anuradha Yarlagadda
Sujata Dash
1 Introduction
3(2)
1.1 Understanding AI
4(1)
2 IoT ecosystem in agriculture
5(2)
2.1 Management techniques/systems (IoT and big data)
5(1)
2.2 Smart information systems (SIS) in agriculture
6(1)
3 Benefits of IoT in agriculture
7(3)
3.1 Remote sensing as a major tool in agriculture
7(1)
3.2 Weather forecasting as a prime IoT in agriculture
8(1)
3.3 Agriculture drones
8(1)
3.4 Crop monitoring
8(1)
3.5 Smart irrigation
9(1)
3.6 Greenhouse monitoring and automation system
9(1)
4 Open issues and key challenges in the adoption of IoT in agriculture
10(2)
4.1 Reliability
10(1)
4.2 Data privacy protection and issues of ownership
10(1)
4.3 Autonomy foreseeability and causation
11(1)
4.4 Control
11(1)
4.5 Opaque research and development
12(1)
5 Legal issues in regulating AI in agriculture
12(2)
5.1 Torts and contracts
12(1)
5.2 Crimes
13(1)
5.3 Law relating to accidents, health, and safety
13(1)
5.4 Accidents and negligence
13(1)
5.5 Environmental laws
13(1)
6 Conclusion
14(3)
References
14(3)
Chapter 2 Edge computing-Foundations and applications
17(14)
Jorge A. Ruiz-Vanoye
Ocotlan Diaz-Parra
Alejandro Fuentes-Penna
Alberto Ochoa-Zezzatti
Josue Roman Mireles
Jazmin Rodriguez-Flores
Israel Campero-Jurado
Miguel A. Ruiz-Jaimes
1 Introduction
17(1)
2 Edge computing
17(5)
3 Applications of edge computing
22(4)
3.1 Future trends of edge computing
26(1)
4 Conclusions
26(5)
References
28(3)
Chapter 3 IoT-based fuzzy logic-controlled novel and multilingual mobile application for hydroponic farming
31(12)
Sitanath Biswas
Bhupesh Deka
Sujata Dash
Kailash Rout
1 Introduction
31(1)
2 Literature review
32(1)
3 Methodology
33(1)
4 Proposed method
34(5)
5 Results and discussion
39(2)
6 Conclusion
41(2)
References
41(2)
Chapter 4 Functional framework for loT-based agricultural system
43(28)
Ram Sewak Singh
Demissie Jobir Gelmecha
Tadesse Hailu Ayane
Devendra Kumar Sinha
1 Introduction
43(13)
1.1 Overview of the cases
44(1)
1.2 Challenges, opportunities, and use of IoT applications in agriculture
45(2)
1.3 Opportunities allied with the solicitation of IoT in agriculture
47(1)
1.4 The architecture of a smart farm monitoring system
48(4)
1.5 Energy-saving technologies
52(1)
1.6 Security mechanisms
52(1)
1.7 Advantages of IoT in agriculture system
53(1)
1.8 Precision farming
53(1)
1.9 Smart greenhouse
54(1)
1.10 Data analytics
54(1)
1.11 Agricultural drones
55(1)
1.12 Limitations of the existing proposed model
56(1)
2 Methodology
56(3)
2.1 Block diagram of proposed model
56(1)
2.2 Flow diagram of controlling process of motor using sensors
57(1)
2.3 IoT with transmitter and receiver wireless sensor model
58(1)
3 Experimental results and discussion
59(3)
3.1 Experimental work
59(2)
3.2 Thingspeak cloud server
61(1)
4 Results
62(3)
4.1 Measurements at 14:30 when soil is dry
62(1)
4.2 Measurements on May 14, 2020; time varies when soil is wet
63(1)
4.3 Measurement in night, when soil is dry
64(1)
4.4 Measurement in night, when soil is wet
64(1)
5 Discussion
65(1)
6 Conclusion and future scope
66(5)
6.1 Future scope
66(1)
References
66(5)
Chapter 5 Functional framework for edge-based agricultural system
71(30)
S. Premkumar
A.N. Sigappi
1 Introduction
71(2)
2 Relevant technologies
73(1)
3 Edge computing in agricultural sectors
73(5)
3.1 Role of edge computing in multiple facets of agriculture
76(2)
4 Edge computing framework design in agriculture
78(7)
4.1 Communication
80(1)
4.2 Processing/computation
81(1)
4.3 Analytics
82(1)
4.4 Storage
83(1)
4.5 Actuation
84(1)
4.6 Sensing
84(1)
5 Edge computing implementation
85(9)
5.1 Hardware implementation
87(1)
5.2 Data communication technologies
88(1)
5.3 Data processing implementation
89(3)
5.4 Experimental set-up of edge-based agricultural system
92(2)
6 Conclusion
94(7)
References
96(5)
Chapter 6 Precision agriculture: Weather forecasting for future farming
101(24)
Kingsley Eghonghon Ukhurebor
Charles Oluwaseun Adetunji
Olaniyan T. Olugbemi
W. Nwankwo
Akinola Samson Olayinka
C. Umezuruike
Daniel Ingo Hefft
1 Introduction
101(5)
1.1 Terminologies employed in precision agriculture
103(2)
1.2 Connection between precision agriculture and traditional agriculture
105(1)
2 Weather and climate
106(3)
2.1 Weather
107(1)
2.2 Climate
108(1)
3 Agricultural implications of climate change
109(4)
3.1 Reducing the burden of agriculture on climate change
110(1)
3.2 Exploring the climate change influence as an influential element in agricultural productivity
111(2)
4 Modern tools and techniques for precision agriculture
113(3)
4.1 Internet of Things (IoT)
114(1)
4.2 Sensor technology
114(1)
4.3 Unmanned aerial vehicles (UAVs)
114(1)
4.4 Unmanned ground vehicles (UGVs)
115(1)
4.5 Robots
115(1)
4.6 Smartphone
115(1)
4.7 Autoguidance equipment (AGE)
115(1)
4.8 Variable rate technology
116(1)
4.9 Grid sampling
116(1)
5 Conclusion
116(9)
References
117(8)
SECTION 2 IoT use cases in smart farming and smart agriculture
Chapter 7 Crop management system using IoT
125(18)
Himadri Nath Saha
Reek Roy
Monojit Chakraborty
Chiranmay Sarkar
1 Introduction
125(2)
2 Background and related works
127(2)
3 Proposed model
129(3)
4 Methodology
132(5)
5 Performance analysis
137(1)
6 Future research direction
137(2)
7 Conclusion
139(4)
References
140(3)
Chapter 8 Smart irrigation and crop security in agriculture using IoT
143(14)
Sugamya Katta
Sangita Ramatenki
Harika Sammeta
1 Introduction
143(2)
1.1 Overview
144(1)
1.2 Applications
144(1)
1.3 Motivation
145(1)
1.4 Objectives
145(1)
2 Methodology
145(4)
2.1 Basic building blocks of an IoT device
146(3)
3 Algorithms
149(2)
3.1 Design flow
150(1)
4 Implementation
151(3)
4.1 System process
152(2)
5 Testing and results
154(1)
6 Conclusion and future scope
154(3)
References
155(2)
Chapter 9 The Internet of Things in agriculture for sustainable rural development
157(14)
Ashish Tripathi
Arush Jain
Arun Kumar Singh
Pushpa Choudhary
Krishna Kumar Mishra
Prem Chand Vashist
1 Introduction
157(3)
1.1 Literature survey
158(1)
1.2 Present scenario
158(2)
2 Background details
160(4)
2.1 Internet of Things
160(2)
2.2 IoT in agriculture for rural development
162(2)
3 IoT in agriculture: Use cases
164(1)
4 Case studies: IoT-based agriculture for sustainable rural development
165(1)
4.1 Slashing water consumption in avocado
165(1)
4.2 Smart dairy farming
166(1)
5 Impact of IoT on food sustainability and socioeconomic uplift
166(1)
5.1 Food sustainability
166(1)
5.2 Socioeconomic uplift
167(1)
6 Challenges and opportunities
167(1)
6.1 Unstable Internet connection in farms
167(1)
6.2 Disrupted connectivity to cloud servers
168(1)
6.3 Costly hardware
168(1)
7 Conclusion
168(3)
References
169(2)
Chapter 10 Internet of Things (IoT) in agriculture toward urban greening
171(12)
Sarita Samal
Biswaranjan Acharya
Prasanta Kumar Barik
1 Introduction
171(1)
2 IOT architectures
172(2)
2.1 Definitions of IoT
172(1)
2.2 G-IoT
173(1)
2.3 Architecture of IoT
173(1)
3 G-IOT application
174(1)
3.1 Green tags
174(1)
3.2 Green sensing networks
175(1)
3.3 Green Internet technologies
175(1)
4 IOT applications
175(4)
4.1 Smart industrial plants and machine-to-machine communications
177(1)
4.2 Smart plant monitoring
177(1)
4.3 Smart data collection
177(1)
4.4 Smart sensing
178(1)
4.5 Smart sports
178(1)
4.6 Smart social networks
178(1)
4.7 Smart agriculture
178(1)
4.8 Smart waste
178(1)
4.9 Smart environment
179(1)
4.10 Smart grid
179(1)
5 G-IOT challenges and opportunities
179(1)
5.1 Green infrastructure
179(1)
5.2 Green spectrum management
179(1)
5.3 Green communication
180(1)
5.4 Green security and management
180(1)
6 Conclusion
180(3)
References
181(2)
Chapter 11 Smart e-agriculture monitoring systems
183(22)
Sohail Saif
Priya Roy
Chandreyee Chowdhury
Suparna Biswas
Ujjwal Maulik
1 Introduction
183(1)
2 Need for smart e-monitoring system for agriculture
184(1)
3 System architecture
184(6)
3.1 WSN-based architecture
185(1)
3.2 IoT-Cloud based architecture
186(4)
4 IoT and data analytics in agriculture
190(3)
4.1 Devices deployed
191(1)
4.2 Data acquisition
191(1)
4.3 Data processing
191(1)
4.4 Data analytics
191(2)
5 Different types of solutions available
193(2)
5.1 Botanicalls
193(1)
5.2 Parrot flower power
193(1)
5.3 HarvestGeek
194(1)
5.4 Open garden
194(1)
5.5 Automated hydroponics: Bitponics
194(1)
5.6 Edyn
194(1)
5.7 Koubachi
195(1)
6 Research challenges
195(1)
7 Case study on IoT-based monitoring systems
196(3)
7.1 Case study 1: IoT-based greenhouse crop production
196(1)
7.2 Case study 2: IoT-based plant disease prediction
197(1)
7.3 Case study 3: IoT-based vineyard monitoring
198(1)
7.4 Case study 4: IoT-based irrigation management
198(1)
8 Open research issues
199(1)
9 Conclusion
199(6)
References
200(5)
Chapter 12 Smart agriculture using renewable energy and AI-powered IoT
205(22)
Moxa Doshi
Akson Varghese
1 Introduction
205(1)
2 Background and related work
206(3)
2.1 VAWT
206(1)
2.2 Data analytics platform
207(2)
2.3 IoT devices
209(1)
3 Concept
209(3)
3.1 Suburban set-up
210(1)
3.2 Rural set-up
211(1)
4 Architecture and system design
212(8)
4.1 Components of the system
212(5)
4.2 Concept model
217(3)
5 User operability
220(1)
5.1 Ideal scenario
220(1)
6 Application
221(1)
6.1 Fanning analytics
221(1)
7 Advantages
222(1)
7.1 Availability
222(1)
7.2 Economical
222(1)
7.3 Renewable source
222(1)
7.4 Efficiency of crop growth
223(1)
7.5 Holistic supply chain management
223(1)
8 Limitations
223(4)
8.1 Structural implementation
223(1)
8.2 Safety concerns
223(1)
8.3 Government support
223(1)
8.4 Connectivity
224(1)
8.5 Maintenance requirement
224(1)
References
224(3)
Chapter 13 Smart irrigation-based behavioral study of Moringa plant for growth monitoring in subtropical desert climatic condition
227(14)
Vinod Kumar Shukla
Reshmi S. Nair
Farjad Khan
1 Introduction
227(1)
2 Moringa oleifera as a miracle plant
228(3)
2.1 Properties of Moringa oleifera
229(1)
2.2 Medicinal value and health benefits
229(2)
3 Favorable climatic condition
231(1)
3.1 Growth pattern in arid and semiarid areas
231(1)
3.2 Challenges in subtropical climatic conditions
231(1)
4 Motivation and challenges
231(1)
4.1 Plant and smart technology
232(1)
5 Technology used
232(2)
5.1 Arduino UNO
232(1)
5.2 Relay
232(1)
5.3 Soil moisture sensor
233(1)
5.4 Water pump
233(1)
6 Methodology
234(2)
7 Flowchart
236(1)
8 Limitations and area of improvement
237(1)
9 Conclusion
237(4)
References
237(4)
Chapter 14 Surveying smart farming for smart cities
241(24)
Jorge A. Ruiz-Vanoye
Ricard A. Barrera-Camara
Ocotlan Diaz-Parra
Julio C. Ramos-Fernandez
Alejandro Fuentes-Penna
Alberto Ochoa-Zezzatti
Jose Alberto Hernandez-Aguilar
Israel Campero-Jurado
1 Introduction
241(2)
2 Smart farming history
243(3)
3 Smart farming and future trends
246(11)
4 Conclusions
257(8)
References
258(7)
SECTION 3 Edge computing use cases in smart farming and smart agriculture
Chapter 15 Farm automation
265(22)
K. Rupabarrta Singh
Sujata Dash
Sourav K. Giri
1 Introduction
265(2)
2 Current trends in smart farming automation systems
267(3)
3 Architecture of edge computing and IoT (E-IoT) platform
270(5)
3.1 FAR-edgeRA
271(1)
3.2 Edge computing reference architecture 2.0
271(1)
3.3 Industrial Internet Consortium reference architecture
272(1)
3.4 INTELSAP reference architecture
273(1)
3.5 Global edge computing reference architecture
274(1)
4 Applications of E-IoT in farm automation
275(7)
4.1 E-IoT in weed detection
275(2)
4.2 Smart irrigation system with E-IoT
277(1)
4.3 E-IoT in livestock management
278(1)
4.4 Farm security solution with E-IoT
279(2)
4.5 E-IoT in food safety
281(1)
5 Discussion
282(1)
6 Future challenges
282(1)
7 Conclusion
282(5)
References
283(4)
Chapter 16 A fog computing-based IoT framework for prediction of crop disease using big data analytics
287(14)
Chandrima Roy
Nivedita Das
Siddharth Swamp Rautaray
Manjusha Pandey
1 Introduction
287(4)
1.1 Fog computing
287(1)
1.2 IoT in agriculture
288(1)
1.3 Smart crop disease prediction
288(3)
1.4 The role of fog computing in IoT
291(1)
2 IoT-fog integration in crop disease prediction
291(6)
2.1 IOT in crop disease
292(2)
2.2 IoT agricultural framework
294(1)
2.3 A fog computing-based IOT framework for predicting crop disease
295(2)
3 Proposed model
297(1)
3.1 Information needed to predict disease accurately
297(1)
3.2 Map-reduce based prediction model
298(1)
4 Conclusion and future work
298(3)
References
300(1)
Chapter 17 Agribots: A gateway to the next revolution in agriculture
301(14)
Charles Oluwaseun Adetunji
Daniel Ingo Hefft
Olaniyan T. Olugbemi
1 Introduction
301(1)
2 Specific examples of how agribots could be integrated into a regional IoT-enabled single window for improving collective subsistence agricultural production in rural communities
302(6)
3 Conclusion
308(7)
References
309(6)
SECTION 4 Sensor network use cases in smart farming and smart agriculture
Chapter 18 SAW: A real-time surveillance system at an agricultural warehouse using IoT
315(14)
Samaleswari Prasad Nayak
Satyananda Champati Rai
Biswajit Sahoo
1 Introduction
315(1)
2 Issues and challenges with a traditional monitoring system in agriculture
316(1)
3 The possibilities of IoT as an alternate to conventional agriculture
316(1)
4 Components used with specifications and applications for IoT-enabled agriculture system
317(5)
4.1 Temperature sensor and humidity sensor
317(1)
4.2 Flame sensor and smoke sensor
318(1)
4.3 Main controller
318(1)
4.4 NRF24L01 transceiver module
319(1)
4.5 GSM communication module
319(2)
4.6 Earthquake sensor
321(1)
4.7 Buzzer
322(1)
5 IoT-enabled autonomous agriculture model (SAW)
322(4)
5.1 Performance analysis
323(3)
6 Conclusion
326(3)
References
326(3)
Chapter 19 The predictive model to maintain pH levels in hydroponic systems
329(16)
Jyotiprakash Panigrahi
Priyanka Pattnaik
Arup Kumar Mukherjee
Satya Ranjan Dash
1 Introduction
329(1)
2 Hydroponics system discussion
330(3)
2.1 Macronutrients
330(1)
2.2 Micronutrients
331(2)
3 NFT channels
333(1)
4 DWC hydroponics
334(1)
5 Drip system
334(1)
6 Aeroponics
334(1)
7 Ph management automation
335(3)
7.1 Data collection
336(1)
7.2 Correlation analysis
337(1)
8 Dataset
338(1)
9 Model generation
338(2)
9.1 Final set-up
339(1)
10 Hydroponics automation
340(2)
10.1 Automated factors
340(2)
10.2 Major advantages of hydroponics
342(1)
10.3 Major disadvantages of hydroponics
342(1)
11 Conclusion and future scope
342(3)
References
343(2)
Chapter 20 A crop-monitoring system using wireless sensor networking
345(16)
Himadri Nath Saha
Reek Roy
Monojit Chakraborty
Chiranmay Sarkar
1 Introduction
345(1)
2 Background and related works
346(3)
3 Proposed model
349(4)
3.1 NodeMCU (node microcontroller unit)
350(1)
3.2 Passive infrared sensor (PIR sensor)
351(1)
3.3 Temperature and humidity sensor
351(1)
3.4 Ph sensor
351(1)
3.5 UAV
352(1)
3.6 RGB-D sensor
352(1)
3.7 ThingSpeak
352(1)
4 Methodology
353(2)
5 Performance analysis
355(2)
6 Future research direction
357(1)
7 Conclusion
358(3)
References
359(2)
Chapter 21 Integration of RFID and sensors in agriculture using IOT
361(14)
Himadri Nath Saha
Sumanta Chakraborty
Reek Roy
1 Introduction
361(2)
2 Background and related works
363(2)
3 System design and architecture
365(3)
4 Methodology
368(2)
5 Future research direction
370(1)
6 Conclusion
370(5)
References
371(4)
SECTION 5 AI and data analytics in agriculture
Chapter 22 Prediction of crop yield and pest-disease infestation
375(20)
Pramit Pandit
K.N. Krishnamurthy
Bishvajit Bakshi
1 Introduction
375(1)
2 Crop yield forecasting models
376(9)
2.1 Time series models
376(6)
2.2 Bayesian forecasting approach
382(1)
2.3 Weather-based crop yield forecasting models
382(3)
3 Pest and disease forewarning systems
385(4)
3.1 Between-year models
385(3)
3.2 Within-year models
388(1)
3.3 Loss of crop yield due to pest and disease outbreak
388(1)
4 Conclusion
389(6)
References
389(6)
Chapter 23 Machine learning-based remote monitoring and predictive analytics system for crop and livestock
395(14)
Nikita Goel
Sumit Kaur
Yogesh Kumar
1 Introduction
395(1)
2 Background study
396(4)
2.1 Framework for remote monitoring and predictive analysis using ML
397(1)
2.2 Benefits of the work
397(2)
2.3 Research challenges
399(1)
2.4 Role of AI and machine learning for crop monitoring
400(1)
3 Reported work
400(3)
3.1 Wheat
400(1)
3.2 Rice
401(1)
3.3 Soybean
402(1)
3.4 Other crops
402(1)
4 Comparative analysis
403(2)
5 Conclusion
405(4)
References
405(4)
Chapter 24 Exploring performance and predictive analytics of agriculture data
409(28)
Madhavi Vaidya
Shweta Katkar
1 Introduction
409(1)
2 Literature survey
410(1)
3 The need for data processing
410(2)
4 Big data characteristics
412(2)
5 Techniques and tools for big data processing
414(2)
6 Advantages of data analysis in agriculture
416(1)
7 Classical approach of farming process
417(1)
8 Smart farm management
417(1)
9 Advantages of smart farming
418(1)
10 Analysis of usefulness of various smart farming techniques
418(1)
11 Agricultural big data mining
419(1)
12 Study of agricultural sector using mobile apps
419(1)
13 Study of vegetable production using hydroponics
420(1)
14 Comparative approach of implementation mechanisms
420(1)
15 Literature survey on algorithms
421(1)
16 Comparative approach of the various techniques
421(2)
16.1 Methodology
422(1)
17 Study of datasets
423(1)
17.1 Crop production dataset overview
423(1)
17.2 Technique used in data mining
423(1)
18 Data isualization
423(4)
18.1 Fertilizers datasets overview
425(2)
19 Result and analysis
427(4)
20 Concerns and conclusions
431(6)
References
434(2)
Further Reading
436(1)
Chapter 25 Climate condition monitoring and automated systems
437(12)
Kingsley Eghonghon Ukhurebor
Charles Oluwaseun Adetunji
Olaniyan T. Olugbemi
Daniel Ingo Hefft
1 Introduction
437(1)
2 Impacts of climate
438(5)
2.1 Climate impacts on environment
439(1)
2.2 Climate impacts on health
440(1)
2.3 Climate impacts on agriculture
440(3)
3 Climate monitoring systems and automation
443(1)
4 Recent developments on applications of climate monitoring systems in environment, health, and agriculture
443(2)
5 Conclusion and future work
445(4)
References
445(4)
Chapter 26 Decision-making system for crop selection based on soil
449(28)
Jitendra Singh
Preeti Pandey
P.K. Pandey
1 Introduction
449(2)
2 Machine learning role in agriculture: A review
451(1)
3 Soil health and crop production
452(7)
4 Experiment and analysis
459(9)
4.1 Data development
459(9)
5 Performance metrics
468(3)
5.1 Prediction algorithm implementation and performance outcomes
469(1)
5.2 Prediction comparative analysis
469(2)
6 Crop selection and soil health recommendation system
471(1)
7 Conclusion and challenges
472(5)
References
473(4)
Chapter 27 Cyberespionage: Socioeconomic implications on sustainable food security
477(10)
Charles Oluwaseun Adetunji
Olaniyan T. Olugbemi
Osikemekha Anthony Anani
Daniel Ingo Hefft
Nwankwo Wilson
Akinola Samson Olayinka
Kingsley Eghonghon Ukhurebor
1 Introduction
477(7)
2 Conclusion
484(3)
References
485(2)
Chapter 28 Internet of Things on sustainable aquaculture system
487(18)
Jorge A. Ruiz-Vanoye
Ricardo A. Barrera-Camara
Alejandro Fuentes-Penna
Ocotlan Diaz-Parra
Francisco R. Trejo-Macotela
Israel Campero-Jurado
Miguel A. Ruiz-Jaimes
Yadira Toledo-Navarro
1 Introduction
487(3)
2 Internet of Farming Things
490(3)
3 Internet of Things on sustainable aquaculture system
493(8)
4 Conclusions
501(4)
References
501(4)
Chapter 29 IoT-based monitoring system for freshwater fish farming: Analysis and design
505(12)
Osikemekha Anthony Anani
Charles Oluwaseun Adetunji
Olaniyan T. Olugbemi
Daniel Ingo Hefft
Nwankwo Wilson
Akinola Samson Olayinka
1 Introduction
505(1)
2 Relevance of monitoring devices in freshwater fish farming (including IoT and smart monitoring systems)
506(3)
3 IoT-based monitoring production: Feasibility, requirement planning, analysis, and design
509(2)
4 Building IoT infrastructure for monitoring production: Feasibility, requirement planning, analysis, and design
511(1)
5 Conclusions and recommendations
512(5)
References
513(4)
Chapter 30 Transforming IoT in aquaculture: A cloud solution
517(16)
Shavika Gupta
Abhishek Gupta
Yasha Hasija
1 Introduction
517(2)
2 Cloud computing in IoT
519(1)
3 Types of clouds
519(1)
4 Benefits of cloud platform in IoT
520(1)
5 Integration of cloud computing and IoT
520(2)
5.1 Architecture
521(1)
6 Cloud platforms
522(1)
7 Cloud-based IoT monitoring aquaculture system
523(1)
8 Security
524(1)
9 Cloud-IoT architecture in shrimp aquaculture
524(2)
10 Introduction of wireless sensor networks (WSN)
526(3)
10.1 WSN architecture for aquaculture
526(1)
10.2 Monitoring of water quality with the help of WSN
527(1)
10.3 Challenges
528(1)
11 Future trends and conclusion
529(4)
References
530(3)
Chapter 31 Toward the design of an intelligent system for enhancing salt water shrimp production using fuzzy logic
533(10)
Charles Oluwaseun Adetunji
Osikemekha Anthony Anani
Olaniyan T. Olugbemi
Daniel Ingo Hefft
Nwankwo Wilson
Akinola Samson Olayinka
1 Introduction
533(1)
2 Specific examples of intelligent systems for enhancing salt water shrimp production using fuzzy logic
534(5)
3 Conclusion
539(4)
References
540(3)
Index 543
Dr. Ajith Abraham is the Vice Chancellor at Sai University, Chennai. Before joining Sai University, he held the position of vice chancellor at prominent institutions and was also the founding director of Machine Intelligence Research Labs (MIR Labs), a non-profit scientific network for innovation and research excellence with headquarters in Seattle, USA. Dr. Abraham has completed research projects valued at over $110 million as an investigator or co-investigator from the United States, the European Union, Italy, the Czech Republic, France, Malaysia, China, and Australia. He has worked in a multidisciplinary setting for more than 35 years and has authored or co-authored more than 1,500+ research publications in artificial intelligence and related applications in the industry. A handful of his publications have been translated into Chinese and Russian, and one of his books has been translated into Japanese. The Scopus database has approximately 1,400 papers indexed, whereas the Thomson Web of Science has over 1,000 publications indexed.

In addition to other esteemed universities, Dr. Abraham has worked with researchers from MIT (USA), the University of Cambridge (UK), Harvard University (USA), and Oxford University (UK). According to Google Scholar, Dr. Abraham possesses over 63,000 scholarly citations with an H-index of over 118. He has delivered over 250 conference plenary talks and tutorials in more than 20 countries. From 2008 to 2021, Dr. Abraham chaired the IEEE Systems, Man, and Cybernetics Society Technical Committee on Soft Computing, which had more than 200 members. From 2011 to 2013, he represented Europe as a Distinguished Lecturer for the IEEE Computer Society (USA). Dr. Abraham is continuously listed in the Stanford/Elsevier list, highlighting the top 2% of the most cited scientists across the globe. Based on 2024 data, ScholarGPS listed Dr. Abraham as one of the worlds top 0.01% cited scientists in the engineering and computer science fields.

From 2016 to 2021, Dr. Abraham worked as the chief editor of Engineering Applications of Artificial Intelligence (EAAI) at Elsevier, New York. EAAI is one of the oldest journals (founded in 1988) in the artificial intelligencedomain. Additionally, he sat on the editorial boards of more than 15 international journals indexed by Thomson ISI. Dr. Abraham received his Ph.D. degree in artificial intelligence from Monash University, Melbourne, Australia (2001), a Master of Science degree from Nanyang Technological University, Singapore (1998), and a B.Tech (Hons) degree from the University of Calicut in 1990.

Sujata Dash holds the position of Professor at the Information Technology School of Engineering and Technology, Nagaland University, Dimapur Campus, Nagaland, India, bringing more than three decades of dedicated service in teaching and mentoring students. She has been honoured with the prestigious Titular Fellowship from the Association of Commonwealth Universities, United Kingdom. As a testament to her global contributions, she served as a visiting professor in the Computer Science Department at the University of Manitoba, Canada. With a prolific academic record, she has authored over 200 technical papers published in esteemed international journals, and conference proceedings, and edited book chapters by reputed publishers Serving as a reviewer and Associate Editor for approximately 15 international journals.

Joel J. P. C. Rodrigues is a professor at the Federal University of Piauí, Brazil, and senior researcher at the Instituto de Telecomunicações, Portugal. He is the leader of the Next Generation Networks and Applications (NetGNA) research group (CNPq), an IEEE Distinguished Lecturer, Member Representative of the IEEE Communications Society on the IEEE Biometrics Council, and the President of the scientific council at ParkUrbis Covilhã Science and Technology Park. He has been general chair and TPC Chair of many international conferences, including IEEE ICC, IEEE GLOBECOM, IEEE HEALTHCOM, and IEEE LatinCom. He has authored or coauthored over 800 papers in refereed international journals and conferences, 3 books, 2 patents, and 1 ITU-T Recommendation. He had been awarded several Outstanding Leadership and Outstanding Service Awards by IEEE Communications Society and several best papers awards. Prof. Rodrigues is a member of the Internet Society, a senior member ACM, and Fellow of IEEE. Biswa Ranjan Acharya is an academic currently associated with Kalinga Institute of Industrial Technology Deemed to be University along with pursuing PhD in computer application from Veer Surendra Sai University of Technology (VSSUT), Burla, Odisha, India. He received MCA in 2009 from IGNOU, New Delhi, India and M.Tech in Computer Science and Engineering in the year of 2012 from Biju Pattanaik University of Technology (BPUT), Odisha, India. He is also associated with various educational and research societies like IEEE, IACSIT, CSI, IAENG, and ISC. He has industry experience as a software engineer. He currently is working on research in multiprocessor scheduling along with fields such as Data Analytics, Computer Vision, Machine Learning and IOT. Dr. Subhendu Kumar Pani received his Ph.D. from Utkal University, Odisha, India in the year 2013. He is working as a professor at Krupajal Engineering College under BPUT, Odisha, India. He has more than 20 years of teaching and research experience His research interests include Data mining, Big Data Analysis, web data analytics, Fuzzy Decision Making and Computational Intelligence. He is the recipient of 5 researcher awards. In addition to research, he has guided two PhD students and 31 M. Tech students. He has published 150 International Journal papers (100 Scopus index). His professional activities include roles as Book Series Editor (CRC Press, Apple Academic Press, Wiley-Scrivener), Associate Editor, Editorial board member and/or reviewer of various International Journals. He is an Associate with no. of the conference societies. He has more than 250 international publications, 5 authored books, 25 edited and upcoming books; 40 book chapters into his account. He is a fellow in SSARSC and a life member in IE, ISTE, ISCA, and OBA.OMS, SMIACSIT, SMUACEE, CSI.