카트

이미지 검색을 사용해 보세요

검색창 이전화면 이전화면

최근 검색어: 전체삭제

인기 검색어

닫기

Geospatial Intelligence in Precision Agriculture

공유하기

소득공제 EPUB

eBook Geospatial Intelligence in Precision Agriculture

EPUB

저

Wiley-Scrivener 2026.09.10.

회원리뷰 첫번째 리뷰어가 되어주세요

가격: 244,800원

244,800

YES포인트?: 12,240원 (5%)

5만원 이상 구매 시 2천원 추가 적립

앱 다운 시 1천원 상품권

결제혜택: 카드/간편결제 혜택을 확인하세요

eBook 안내: 이 상품은 구매 후 지원 기기에서 예스24 eBook앱 설치 후 바로 이용 가능한 상품이며, 배송되지 않습니다.

시리즈 태그 업체 공지사항 구성 소개 특별 구성 MD 한마디 카드뉴스 상세 이미지 소개 관련 동영상 목차 미리듣기 저자 소개 관련분류 품목정보 만든이 코멘트 예스24 리뷰 책 속으로 줄거리 캐스팅 관련 자료 부가 영상 출판사 리뷰 전문가 리뷰 추천평

펼치기/닫기

소개

To secure the future of global food systems in an era of climate disruption, this comprehensive, hands-on reference equips you with the cutting-edge geospatial intelligence and AI tools needed to build highly profitable, sustainable, and climate-resilient farms. With a growing population, rising demand for food, and accelerating climate change, geospatial intelligence is reshaping how the world farms. This book delivers a comprehensive, hands-on guide to modern precision agriculture, bringing together the core enabling technologies of remote sensing, GIS, IoT, artificial intelligence, and cloud-based earth-observation platforms into one practical, decision-focused reference. From planting to harvest, readers see how location-aware data and smart sensors are used to monitor crops, manage irrigation, assess soil health, classify land use, and quantify environmental impact with a resolution and speed that traditional methods cannot match. Across eighteen carefully sequenced chapters, the book moves from foundations to frontier applications. Each chapter is grounded in real-world case studies that show how these tools are already being deployed across emerging agricultural economies. The book ends with a forward look at AI-driven analytics, multisensor fusion, and cloud technologies that will define the next decade. The result is a single, authoritative volume for students, researchers, agri-tech innovators, and policy professionals who want to understand and apply geospatial intelligence to build farming systems that are productive, profitable, and sustainable.

소개 더보기펼치기

2.1 Introduction to Geospatial Intelligence 20
2.2 Current Challenges in Agriculture 20
2.3 Application of Geospatial Intelligence in Agriculture 22
2.4 Technological Progress in Geospatial Intelligence Applications in Agriculture 25
2.5 Case Studies and Practical Applications 30
2.6 Advantages of Geospatial Intelligence in Agriculture 31
2.7 Challenges and Limitations of Geospatial Intelligence in Agriculture 34
2.8 Future Trends and Opportunities 36

3 Data Acquisition and Sensing Technologies in Geospatial Intelligence 43
Vishvajeet D. Jadhav, Santosh Korav, Sandeep Menon and Prakash Kumar Jha

3.1 Introduction 44
3.2 Data Acquisition Techniques 46
3.3 GEOINT and Sensing Technologies 49
3.4 Aerial Photography—A Vital Part of GEOINT 52
3.5 Remote Sensing Resolution 54
3.6 Applications of Big Data in Agriculture 55
3.7 Cloud Computing in Agriculture 56
3.8 Challenges and Future Directions 56

4 Integrating IoT and Geospatial Technologies for Real-Time Crop and Soil Monitoring in Precision Agriculture 63
Aritra Guin, Santosh Korav and Sujatha H.T.

4.1 Introduction 64
4.2 Overview of IoT Sensors in Agriculture 65
4.3 Geospatial Technologies in Agriculture 67
4.4 Integration of IoT and Geospatial Technologies 70
4.5 Benefits of Real-Time Crop Management 73
4.6 Challenges and Limitations 74
4.7 Future Trends 76
4.8 Conclusion 76

5 Real-Time Crop Management with IoT Sensors and Geospatial Technologies 83
R. Kanthavel, R. Adline Freeda and R. Dhaya

5.1 Introduction 84
5.2 Real-Time Crop Management with IoT Sensors and Geospatial Technologies 85
5.3 Sensor Deployment in The Field 89
5.4 Data Collection and Aggregation 92
5.5 Crop Monitoring and Analytics 97
5.6 The Future of Smart Farming 103Contents ix
5.7 Conclusion 105

6 Enhancing Irrigation Efficiency with GIS and Precision Mapping Techniques 109
Himanshu and Deepika Saxena

6.1 Introduction 110
6.2 Fundamentals of GIS and Precision Agriculture 111
6.3 Precision Mapping Techniques for Irrigation 113
6.4 Challenges in Traditional Irrigation Systems 116
6.5 Optimizing Irrigation Efficiency with GIS 117
6.6 Remote Sensing in Precision Irrigation 119
6.7 UAVs for High-Resolution Water-Use Mapping 123
6.8 Challenges and Barriers to Adoption 127
6.9 Future Trends and Innovation in Irrigation Efficiency 129
6.10 Conclusion 132

7 Integrating SAR and Multispectral Data with HSV-Based Fusion for Accurate Land Cover Classification 141
Narayan Vyas

7.1 Introduction 142
7.2 Study Area and Satellite Dataset 145
7.3 Methodology 147
7.4 Results 151
7.5 Discussion 153
7.6 Conclusion 154

8 Precision Agriculture through IoT and Geospatial Technologies for Sustainable Crop Management 159
Rupinder Singh, Jaswinder Singh and Amanpreet Singh

8.1 Introduction 160
8.2 Literature Review 163
8.3 The Role of IoT Sensors in Agriculture 163
8.4 Application of Geographical Information Systems (GIS) in Crop Production 169
8.5 Integration of IoT and Geospatial Technologies 171
8.6 Challenges 172
8.7 Conclusion 173

9 Systematic Review on Agricultural Land Use Classification Based on Machine Learning and Deep Learning Approaches Using Satellite Dataset 177
Nisha Sharma, Kawaljit Kaur and Sartajvir Singh

9.1 Introduction 178
9.2 Satellite Datasets for Agricultural LU Classification 181
9.3 Methodology 181
9.4 Related Work 183
9.5 Analysis and Discussion 189
9.6 Conclusion 190

10 Remote Sensing and GIS-Based Approaches to LULC Analysis for Sustainable Agricultural Planning 197
Garima, Ankita Kumari and Soubhagya Ranjan Dhall

10.1 Introduction 198
10.2 Technological Advancements in LULC Mapping 200
10.3 Applications in Sustainable Agriculture 203
10.4 Case Studies on LULC Mapping for Sustainable Agriculture 204
10.5 Future Directions 208

11 Image Classification Techniques for Precision Agriculture and Integrated Pest Management 213
Neetu Agrawal, Mehul Mahrishi and Mukesh Kumar Gupta

11.1 Introduction 214
11.2 Pest Image Acquisition 217
11.3 Image Pre-Processing 218
11.4 AI-Driven Pest Identification 220

12 Spatio-Temporal Analysis of Tropospheric NO2 Over Haryana Using Sentinel-5P and Google Earth Engine 233
Narayan Vyas, Neha Bhati, Rajesh Yadav and Abdullah Alzahrani

12.1 Introduction 234
12.2 Study Area 237
12.3 Dataset and Tools 238
12.4 Methodology 239
12.5 Results 242
12.6 Discussion 245

13 Agriculture Crop Pest Monitoring and Management Using LiDAR Technology 251
Satyabrata Sarangi, Tarini Prasad Das, Ekamra Kanan Padhan, Jyoti Prakash Das and Harmohan Pradhan

13.1 Introduction 252
13.2 Principles of Lidar Technology in Pest Monitoring and Management 254
13.3 Integration of Lidar with Other Digital Technologies for Precision Pest Monitoring and Control 257
13.4 Successful Case Studies of Pest Identification and Forecasting Using Lidar System 258
13.5 Challenges and Future Studies 265
13.6 Conclusion 267

14 Role of Google Earth Engine (GEE) in Natural Hazard Monitoring and Management 275
Vijaya Ilango and Bhavadev Gajendiran

14.1 Introduction 276
14.2 Tools and Techniques for Natural Hazard Assessment 278
14.3 Role of GEE in Disaster Monitoring and Prediction 282
14.4 Conclusion 286

15 Innovative Business Models for Geospatial-Enabled Agricultural Solutions 295
Amit Sharma, Shubhi Sharma, Ruchi Sharma and Harshita Doad

15.1 Introduction 296
15.2 Understanding the Geospatial-Agriculture Nexus 297
15.3 The Shift in Business Thinking: From Inputs to Intelligence 298
15.4 Business Model Typologies in Geospatial AgTech 299
15.5 Innovation through Collaboration and Open Models 301
15.6 Real-World Case Studies 302
15.7 Challenges in Scaling Geospatial Business Models 303
15.8 Designing a Scalable, Inclusive Future 304
15.9 Conclusion 306

16 Economic Impacts of Geospatial Intelligence on Sustainable Farming 313
Gurwinder Singh, Aaskaran Bishnoi and Ranjit Singh

16.1 Introduction 314
16.2 Background and Context 316
16.3 Role of GI in Addressing Economic Challenges 317
16.4 Economic Benefits of GI 319
16.5 Risk Mitigation 321
16.6 Market Dynamics and Economic Opportunities 324
16.7 Challenges and Economic Barriers 328
16.8 Case Studies or Practical Applications 331
16.9 Future Economic Implications 334

17 Future Directions and Challenges for Geospatial Technologies: Shaping India's Policy Landscape for Sustainable Renewable Energy 339
Deepali Singh, Harshita Sharma, R. B. Singh, Seema Ahmad and Ronak Duggar

17.1 Introduction 340
17.2 The Policy Framework for Renewable Energy in India 342
17.3 Obstacles in the Implementation of Policies 342
17.4 Impact of Policy and Future Directions 343
17.5 Solar Energy Vision 344
17.6 Hydropower Position 344
17.7 National Renewable Energy Policy Measures 345
17.8 The Influence of Partnership to Advance Clean Energy 345
17.9 Policy Interconnections and Energy Security 345
17.10 India's Renewable Energy 347
17.11 Conclusion 348

18 Future Directions and Challenges for Geospatial Intelligence-Based Precision Agriculture 353
Amit Sharma, Shubhi Sharma, Ruchi Sharma and Balasani Raghupathi

18.1 Introduction 354
18.2 Current State of GEOINT in Precision Agriculture 355
18.3 Emerging Technologies and Trends 359
18.4 Challenges in GEOINT-Driven Precision Agriculture 361
18.5 Comparative Review of Global Research and Gaps 365
18.6 Future Research Directions 369
18.7 Conclusion 371

References 371
Index 377

목차 더보기펼치기

저자 소개

Gurwinder Singh, PhD works with the School of Advanced Computing and the Advanced Centre of Research and Innovation at Chandigarh Group of Colleges University, Mohali, Punjab, India. He is a recipient of the Young Scientist Award and a member of the International Society for Photogrammetry and Remote Sensing. His research focuses on remote sensing, agricultural land-use classification, machine learning, and deep learning.

Vishakha Sood, PhD is a Project Scientist in the Centre of Excellence SEnSRS at the Indian Institute of Technology Ropar. She holds a PhD from Chitkara University and is the founder of Aiotronics Automation Pvt. Ltd. Her research focuses on satellite sensors, remote sensing, and digital image analysis.

Narayan Vyas is with the Department of Computer Science and Engineering at Vivekananda Global University. He has authored more than 60 research publications and edited more than ten books. His research focuses on remote sensing, IoT, machine learning, deep learning, and computer vision.

Surendra Yadav, PhD is with the Department of Computer Science and Engineering at Vivekananda Global University, Jaipur, Rajasthan, India with more than 20 years of experience. He has led GIS-based projects for the Karnataka and Delhi Agricultural Marketing Boards and the National Institute of Agriculture Marketing. He has more than 50 publications spanning remote sensing, GIS, cloud computing, and data mining.

Dankan Gowda V., PhD is an Assistant Professor in the Department of Electronics and Communication Engineering at the BMS Institute of Technology and Management, Bangalore, India. He brings 15 years of teaching and industry experience, including roles at ADA-DRDO and Robert Bosch. His research focuses on IoT and signal processing, with more than 100 international publications and six granted patents.