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E-raamat: Digital Agricultural Revolution: Innovations and Challenges in Agriculture through TechnologyDi sruptions [Wiley Online]

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  • Formaat: 496 pages
  • Ilmumisaeg: 08-Aug-2022
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119823463
  • ISBN-13: 9781119823469
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  • Wiley Online
  • Hind: 237,89 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Digital Agricultural Revolution: Innovations and Challenges in Agriculture through TechnologyDi sruptionsSuurem pilt
  • Formaat: 496 pages
  • Ilmumisaeg: 08-Aug-2022
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119823463
  • ISBN-13: 9781119823469
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119823469
THE DIGITAL AGRICULTURAL REVOLUTION The book integrates computational intelligence, applied artificial intelligence, and modern agricultural practices and will appeal to scientists, agriculturists, and those in plant and crop science management. There is a need for synergy between the application of modern scientific innovation in the area of artificial intelligence and agriculture, considering the major challenges from climate change consequences viz. rising temperatures, erratic rainfall patterns, the emergence of new crop pests, drought, flood, etc. This volume reports on high-quality research (theory and practice including prototype & conceptualization of ideas, frameworks, real-world applications, policy, standards, psychological concerns, case studies, and critical surveys) on recent advances toward the realization of the digital agriculture revolution as a result of the convergence of different disruptive technologies. The book touches upon the following topics which have contributed to revolutionizing agricultural practices. Applications of Artificial Intelligence in Agriculture (AI models and architectures, system design, real-world applications of AI, machine learning and deep learning in the agriculture domain, integration & coordination of systems and issues & challenges). IoT and Big Data Analytics Applications in Agriculture (theory & architecture and the use of various types of sensors in optimizing agriculture resources and final product, benefits in real-time for crop acreage estimation, monitoring & control of agricultural produce). Robotics & Automation in Agriculture Systems (Automation challenges, need and recent developments and real case studies). Intelligent and Innovative Smart Agriculture Applications (use of hybrid intelligence in better crop health and management). Privacy, Security, and Trust in Digital Agriculture (government framework & policy papers). Open Problems, Challenges, and Future Trends. Audience Researchers in computer science, artificial intelligence, electronics engineering, agriculture automation, crop management, and science.
Preface xv
1 Scope and Recent Trends of Artificial Intelligence in Indian Agriculture
1(24)
X. Anitha Mary
Vladimir Popov
Kumudha Raimond
I. Johnson
S. J. Vijay
1.1 Introduction
2(1)
1.2 Different Forms of AI
2(1)
1.3 Different Technologies in AI
3(8)
1.3.1 Machine Learning
4(1)
1.3.1.1 Data Pre-processing
5(1)
1.3.1.2 Feature Extraction
5(1)
1.3.1.3 Working With Data Sets
6(1)
1.3.1.4 Model Development
6(2)
1.3.1.5 Improving the Model With New Data
8(1)
1.3.2 Artificial Neural Network
8(1)
1.3.2.1 ANN in Agriculture
9(1)
1.3.3 Deep Learning for Smart Agriculture
9(1)
1.3.3.1 Data Pre-processing
10(1)
1.3.3.2 Data Augmentation
10(1)
1.3.3.3 Different DL Models
10(1)
1.4 AI With Big Data and Internet of Things
11(1)
1.5 AI in the Lifecycle of the Agricultural Process
12(3)
1.5.1 Improving Crop Sowing and Productivity
12(1)
1.5.2 Soil Health Monitoring
13(1)
1.5.3 Weed and Pest Control
14(1)
1.5.4 Water Management
14(1)
1.5.5 Crop Harvesting
15(1)
1.6 Indian Agriculture and Smart Farming
15(2)
1.6.1 Sensors for Smart Farming
16(1)
1.7 Advantages of Using AI in Agriculture
17(1)
1.8 Role of AI in Indian Agriculture
18(1)
1.9 Case Study in Plant Disease Identification Using AI Technology---Tomato and Potato Crops
19(1)
1.10 Challenges in AI
20(1)
1.11 Conclusion
21(4)
References
21(4)
2 Comparative Evaluation of Neural Networks in Crop Yield Prediction of Paddy and Sugarcane Crop
25(32)
K. Krupavathi
M. Raghu Babu
A. Mani
2.1 Introduction
26(1)
2.2 Introduction to Artificial Neural Networks
27(3)
2.2.1 Overview of Artificial Neural Networks
27(1)
2.2.2 Components of Neural Networks
28(1)
2.2.3 Types and Suitability of Neural Networks
29(1)
2.3 Application of Neural Networks in Agriculture
30(2)
2.3.1 Potential Applications of Neural Networks in Agriculture
30(2)
2.3.2 Significance of Neural Networks in Crop Yield Prediction
32(1)
2.4 Importance of Remote Sensing in Crop Yield Estimation
32(1)
2.5 Derivation of Crop-Sensitive Parameters From Remote Sensing for Paddy and Sugarcane Crops
33(7)
2.5.1 Study Area
33(2)
2.5.2 Materials and Methods
35(1)
2.5.2.1 Data Acquisition and Crop Parameters Retrieval From Remote Sensing Images
35(2)
2.5.3 Results and Conclusions
37(3)
2.6 Neural Network Model Development, Calibration and Validation
40(10)
2.6.1 Materials and Methods
40(1)
2.6.1.1 ANN Model Design
40(2)
2.6.1.2 Model Training
42(1)
2.6.1.3 Model Validation
43(1)
2.6.2 Results and Conclusions
43(7)
2.7 Conclusion
50(7)
References
50(7)
3 Smart Irrigation Systems Using Machine Learning and Control Theory
57(30)
Meric Cetin
Selami Beyhan
3.1 Machine Learning for Irrigation Systems
58(4)
3.2 Control Theory for Irrigation Systems
62(13)
3.2.1 Application Literature
65(7)
3.2.2 An Evaluation of Machine Learning-Based Irrigation Control Applications
72(1)
3.2.3 Remote Control Extensions
72(3)
3.3 Conclusion and Future Directions
75(12)
References
79(8)
4 Enabling Technologies for Future Robotic Agriculture Systems: A Case Study in Indian Scenario
87(22)
X. Anitha Mary
Kannan Mani
Kumudha Raimond
Johnson I.
Dinesh Kumar P.
4.1 Need for Robotics in Agriculture
88(1)
4.2 Different Types of Agricultural Bots
89(2)
4.2.1 Field Robots
89(1)
4.2.2 Drones
90(1)
4.2.3 Livestock Drones
91(1)
4.2.4 Multirobot System
91(1)
4.3 Existing Agricultural Robots
91(2)
4.4 Precision Agriculture and Robotics
93(1)
4.5 Technologies for Smart Farming
94(1)
4.5.1 Concepts of Internet of Things
94(1)
4.5.2 Big Data
94(1)
4.5.3 Cyber Physical System
95(1)
4.5.4 Cloud Computing
95(1)
4.6 Impact of AI and Robotics in Agriculture
95(3)
4.7 Unmanned Aerial Vehicles (UAV) in Agriculture
98(1)
4.8 Agricultural Manipulators
99(1)
4.9 Ethical Impact of Robotics and AI
99(1)
4.10 Scope of Agribots in India
100(1)
4.11 Challenges in the Deployment of Robots
101(1)
4.12 Future Scope of Robotics in Agriculture
102(1)
4.13 Conclusion
103(6)
References
103(6)
5 The Applications of Industry 4.0 (14.0) Technologies in the Palm Oil Industry in Colombia (Latin America)
109(34)
James Perez-Moron
Ana Susana Cantillo-Orozco
5.1 Introduction
110(3)
5.2 Methodology
113(5)
5.2.1 Sample Selection
113(5)
5.3 Results Analysis
118(8)
5.3.1 Data Visualization
122(1)
5.3.2 Cooccurrence
123(1)
5.3.3 Coauthorship
123(1)
5.3.4 Citation
124(1)
5.3.5 Cocitation
125(1)
5.4 Colombia PO Industry
126(4)
5.5 The PO Industry and the Circular Economy
130(1)
5.6 Conclusion
131(1)
5.7 Further Recommendations for the Colombian PO Industry
132(11)
Acknowledgments
133(1)
References
133(10)
6 Intelligent Multiagent System for Agricultural Management Processes (Case Study: Greenhouse)
143(28)
Djamel Saba
Youcef Sahli
Abdelkader Hadidi
Abbreviations 144(27)
6.1 Introduction
144(2)
6.2 Modern Agricultural Methods
146(2)
6.3 Internet of Things Applications in Smart Agriculture
148(1)
6.4 Artificial Intelligence
149(6)
6.4.1 Overview of AI
149(2)
6.4.2 Branches of DAI
151(2)
6.4.3 The Differences Between MAS and Computing Paradigms
153(2)
6.5 MAS
155(4)
6.5.1 Overview of MAS
155(2)
6.5.2 MAS Simulation
157(2)
6.6 Design and Implementation
159(5)
6.6.1 Conception of the Solution
159(1)
6.6.1.1 The Existing Study
159(1)
6.6.1.2 Agents List
160(1)
6.6.2 Introduction to the System Implementation
161(1)
6.6.2.1 Environment
161(1)
6.6.2.2 Group Communication (Multicast)
162(1)
6.6.2.3 Message Transport
162(1)
6.6.2.4 Data Exchange Format
162(1)
6.6.2.5 Cooperation
163(1)
6.6.2.6 Coordination
164(1)
6.6.2.7 Negotiation
164(1)
6.7 Analysis and Discussion
164(3)
6.8 Conclusion
167(4)
References
168(3)
7 Smart Irrigation System for Smart Agricultural Using IoT: Concepts, Architecture, and Applications
171(28)
Abdelkader Hadidi
Djamel Saba
Youcef Sahli
7.1 Introduction
172(1)
7.2 Irrigation Systems
173(7)
7.2.1 Agricultural Irrigation Techniques
174(1)
7.2.2 Surface Irrigation Systems
174(3)
7.2.3 Sprinkler Irrigation
177(1)
7.2.4 Micro-Irrigation Systems
178(1)
7.2.5 Comparison of Irrigation Methods
178(1)
7.2.6 Efficiency of Irrigation Systems
179(1)
7.3 IoT
180(4)
7.3.1 IoT History
180(1)
7.3.2 IoT Architecture
181(1)
7.3.3 Examples of Uses for the IoT
182(1)
7.3.4 IoT Importance in Different Sectors
183(1)
7.4 IoT Applications in Agriculture
184(1)
7.4.1 Precision Cultivation
184(1)
7.4.2 Agricultural Unmanned Aircraft
184(1)
7.4.3 Livestock Control
185(1)
7.4.4 Smart Greenhouses
185(1)
7.5 IoT and Water Management
185(1)
7.6 Introduction to the Implementation
186(6)
7.7 Analysis and Discussion
192(1)
7.8 Conclusion
193(6)
References
194(5)
8 The Internet of Things (IoT) for Sustainable Agriculture
199(26)
M.S. Sadiq
L.P. Singh
M.M. Ahmad
N. Karunakaran
8.1 Introduction
200(2)
8.2 ICT in Agriculture
202(1)
8.3 Internet of Things in Agriculture and Allied Sector
203(8)
8.3.1 Precision Farming
205(3)
8.3.2 Agriculture Drones
208(1)
8.3.3 Livestock Monitoring
209(1)
8.3.4 Smart Greenhouses
210(1)
8.4 Geospatial Technology
211(11)
8.4.1 Remote Sensing
211(4)
8.4.2 Geographic Information System
215(2)
8.4.3 GPS for Agriculture Resources Mapping
217(5)
8.5 Summary and Conclusion
222(3)
References
223(2)
9 Advances in Bionic Approaches for Agriculture and Forestry Development
225(30)
Vipin Parkash
Anuj Chauhan
Akshita Gaur
Nishant Rai
9.1 Introduction
226(1)
9.2 Precision Farming
227(4)
9.2.1 Nanosensors and Its Role in Agriculture
229(1)
9.2.1.1 Nanobiosensor Use for Heavy Metal Detection
230(1)
9.2.1.2 Nanobiosensors Use for Urea Detection
230(1)
9.2.1.3 Nanosensors for Soil Analysis
231(1)
9.2.1.4 Nanosensors for Disease Assessment
231(1)
9.3 Powerful Role of Drones in Agriculture
231(9)
9.3.1 Unmanned Aerial Vehicle Providing Crop Data
232(1)
9.3.2 Using Raw Data to Produce Useful Information
233(6)
9.3.3 Crop Health Surveillance and Monitoring
239(1)
9.4 Nanobionics in Plants
240(1)
9.5 Role of Nanotechnology in Forestry
241(5)
9.5.1 Chemotaxonomy
243(1)
9.5.2 Wood and Paper Processing
244(2)
9.6 Conclusion
246(9)
References
246(9)
10 Simulation of Water Management Processes of Distributed Irrigation Systems
255(14)
Aysulu Aydarova
10.1 Introduction
255(1)
10.2 Modeling of Water Facilities
256(8)
10.3 Processing and Conducting Experiments
264(2)
10.4 Conclusion
266(3)
References
266(3)
11 Conceptual Principles of Reengineering of Agricultural Resources: Open Problems, Challenges and Future Trends
269(20)
Zamlynskyi Viktor
Livinskyi Anatolii
Zamlynska Olha
Minakova Svetlana
11.1 Introduction
270(2)
11.2 Modern Agronomy and Approaches for Environment Sustenance
272(6)
11.2.1 Sustainable Agriculture
273(5)
11.3 International Federation of Organic Agriculture Movements (IFOAM) and Significance
278(2)
11.4 Low Cost versus Sustainable Agricultural Production
280(4)
11.5 Change of Trends in Agriculture
284(5)
References
287(2)
12 Role of Agritech Start-Ups in Supply Chain---An Organizational Approach of Ninjacart
289(12)
D. Rafi
Md. Mubeena
12.1 Introduction
290(1)
12.2 How Does the Chain Work?
291(6)
12.3 Undisrupted Chain of Ninjacart During Pandemic-19
297(1)
12.4 Conclusion
298(3)
References
298(3)
13 Institutional Model of Integrating Agricultural Production Technologies with Accounting and Information Systems
301(10)
Nataliya Kantsedal
Oksana Ponomarenko
13.1 Introduction
302(1)
13.2 Research Methodology
302(1)
13.3 The General Model of a New Informational Paradigm of Agricultural Activities' Organization
303(2)
13.4 The Model of Institutional Interaction of Information Agents in Agricultural Production
305(3)
13.5 Conclusions
308(3)
References
309(2)
14 Relevance of Artificial Intelligence in Wastewater Management
311(22)
Poornima Ramesh
Kathirvel Suganya
T. Uma Maheswari
S. Paul Sebastian
K. Sara Parwin Banu
14.1 Introduction
312(1)
14.2 Digital Technologies and Industrial Sustainability
313(2)
14.3 Artificial Neural Networks and Its Categories
315(1)
14.4 AI in Technical Performance
316(6)
14.5 AI in Economic Performance
322(1)
14.6 AI in Management Performance
323(1)
14.7 AI in Wastewater Reuse
324(1)
14.8 Conclusion
325(8)
References
326(7)
15 Risks of Agrobusiness Digital Transformation
333(26)
Inna Riepina
Anastasiia Koval
Olexandr Starikov
Volodymyr Tokar
15.1 Modern Global Trends in Agriculture
334(3)
15.2 The Global Innovative Differentiation
337(5)
15.3 National Indicative Planning of Innovative Transformations
342(7)
15.4 Key Myths and Risks of Digitalization of Agrobusiness
349(1)
15.5 Examples of Use of Digital Technologies in Agriculture
350(1)
15.6 Imperatives of Transforming the Region into a Cost-Effective Ecosystem of Digital Highly Productive and Risk-Free Agriculture
351(3)
15.7 Conclusion
354(5)
References
356(3)
16 Water Resource Management in Distributed Irrigation Systems
359(20)
Varlantova Lyudmila P.
Yakubov Maqsadhon S.
Elmurodova Barno E.
16.1 Introduction
360(1)
16.2 Types of Mathematical Models for Modeling the Process of Managing Irrigation Channels
360(2)
16.3 Building a River Model
362(7)
16.3.1 Classification of Models by Solution Methods
364(1)
16.3.2 Method of Characteristics
364(1)
16.3.3 Hydrological Analogy Method
365(2)
16.3.4 Analysis of Works on the Formulation of Boundary Value Problems
367(2)
16.4 Spatial Hierarchy of River Terrain
369(5)
16.4.1 Small Drainage Basin Study Scheme
371(1)
16.4.2 Modeling Water Management in Uzbekistan
371(1)
16.4.3 Stages of Developing a Water Resources Management Model
371(3)
16.5 Organizations in the Structure of Water Resources Management
374(1)
16.6 Conclusion
375(4)
References
375(4)
17 Digital Transformation via Blockchain in the Agricultural Commodity Value Chain
379(20)
Necla I. Kucukcolak
Ali Sabri Taylan
17.1 Introduction
380(1)
17.2 Precision Agriculture for Food Supply Security
380(6)
17.2.1 Smart Agriculture Business
381(3)
17.2.2 Trading Venues for Contract Farming, Crowdfunding and E-Trades
384(2)
17.3 Blockchain Technology Practices and Literature Reviews on Food Supply Chain
386(5)
17.3.1 Food Supply Chain
388(1)
17.3.2 Smart Contracts
389(2)
17.4 Agricultural Sector Value Chain Digitalization
391(4)
17.4.1 Digital Solution for Contract Farming
391(1)
17.4.2 Commodity Funding
392(1)
17.4.2.1 Smart Contracts
392(1)
17.4.2.2 Crowdfunding Token Trading
393(1)
17.4.3 Digital Transfer System
393(2)
17.5 Conclusion
395(4)
References
395(4)
18 Role of Start-Ups in Altering Agrimarket Channel (Input-Output)
399(12)
D. Rafi
Md. Mubeena
18.1 Introduction
400(1)
18.2 Agriculture Supply Chain Management
400(2)
18.3 How Start-Ups Fill the Concerns and Gaps in Agri Input Supply Chain?
402(2)
18.4 Output Supply Chain
404(3)
18.5 How Start-Ups are Filling the Concerns and Gaps in Agri Output Supply Chain?
407(1)
18.6 Conclusion
408(3)
References
409(2)
19 Development of Blockchain Agriculture Supply Chain Framework Using Social Network Theory: An Empirical Evidence Based on Malaysian Agriculture Firms
411(36)
Muhammad Shabir Shaharudin
Yudi Fernando
Yuvaraj Ganesan
Faizah Shahudin
19.1 Introduction
412(1)
19.2 Literature Review
413(8)
19.2.1 Agriculture Malaysia
413(2)
19.2.2 Agriculture Supply Chain
415(1)
19.2.3 Blockchain Technology
416(2)
19.2.4 Blockchain Agriculture Supply Chain Management
418(1)
19.2.5 Social Network Theory
419(1)
19.2.6 Social Network Analysis
420(1)
19.3 Methodology
421(3)
19.3.1 Blockchain Agriculture Supply Chain Management Framework
421(2)
19.3.2 Research Design
423(1)
19.4 Results and Discussion
424(16)
19.4.1 Demographic Profiles
424(1)
19.4.2 Social Network Analysis Results
424(16)
19.5 Conclusion
440(1)
19.6 Acknowledgment
441(6)
References
441(6)
20 Potential Options and Applications of Machine Learning in Soil Science
447(14)
Anandkumar Naorem
Shiva Kumar Udayana
Somasundaram Jayaraman
20.1 Introduction: A Deep Insight on Machine Learning, Deep Learning and Artificial Intelligence
448(1)
20.2 Application of ML in Soil Science
449(3)
20.3 Classification of ML Techniques
452(2)
20.3.1 Supervised ML
453(1)
20.3.2 Unsupervised ML
453(1)
20.3.3 Reinforcement ML
453(1)
20.4 Artificial Neural Network
454(1)
20.5 Support Vector Machine
455(2)
20.6 Conclusion
457(4)
References
457(4)
Index 461
Roheet Bhatnagar, PhD, is a professor in the Department of Computer Science & Engineering, Manipal University Jaipur, India. He has published over 60 research papers in reputed conferences and journals, and edited five books.

Nitin Kumar Tripathi, PhD, is a professor in Remote Sensing (RS) and Geographical Information Systems (GIS) at the Asian Institute of Technology (AIT), Thailand. He has supervised 42 Doctoral and 142 Masters theses where a majority of the research topics focused on the applications of GIS and RS in Climate Change impacts on water resources, agriculture, and health. Dr. Tripathi has a total of 182 publications to his credit (two books, 11 chapters in books, 109 research papers in peer-reviewed Journals, and 60 conference papers).

Chandan Kumar Panda, PhD, is an assistant professor and research scientist in the Department of Extension Education at Bihar Agricultural University, Sabour, India.

Nitu Bhatnagar, PhD, is an associate professor in the Department of Chemistry of the Faculty of Science at Manipal University Jaipur.
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