Big Data Analytics in Agriculture

Algorithms and Applications
Erscheinungsjahr 2025
ISBN: 978-0-323-99932-8
Verlag: Elsevier Science

Buch, Englisch, 350 Seiten, Format (B × H): 6 mm x 10 mm

Big Data Analytics in Agriculture: Algorithms and Applications focuses on quantitative and qualitative assessment using state-of-the-art technology to provide practical improvements to agricultural production. The book provides a complete mapping-from data generation to storage to curation, processing and implementation/application-to produce high-quality reliable information for decision-making. It follows a logical pathway to demonstrate how data contributes to a converging flow of information towards a decision support system and how it can be transformed into actionable steps. The book develops ideas surrounding a strong integration of ICT and IoT to manage rural assets to deliver improved economic and environmental performance in a spatially and temporarily variable environment.

Zielgruppe

Postgraduate students, PhD Research Scholars, Scientists, Academicians, Geospatial Experts, Modellers, Agricultural Scientists, Remote Sensing and Computer Science Professionals, IT Professionals, Management Firms, Computing Experts and any other field related to this.

Autoren/Hrsg.

Srivastava, Prashant K

Kumar Mall, Rajesh

Pradhan, Biswajeet

Pandey, Manish K.

Fachgebiete

• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Primärer Sektor
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Naturwissenschaften Agrarwissenschaften Ackerbaukunde, Pflanzenbau
• Wirtschaftswissenschaften Volkswirtschaftslehre Internationale Wirtschaft Entwicklungsökonomie & Emerging Markets
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften

Inhaltsverzeichnis

Section 1: Introduction to Big Data Analytics in Agriculture
1. Introduction to Traditional Data Analytics
2. Introduction to Big Data and Big Data Analytics

Section II: Big Data Management and Processing
3. The efficient management of Big Data from Scalability and Cost Evaluation Perspective
4. The Approaches for the Big Data Processing: Applications and Challenges

Section III: Big Data Analytics Algorithms
5. Big Data Mining in real-time scenarios with limited resources and computational power
6. Big Data Analytics techniques comprising descriptive, predictive, prescriptive and preventive analytics with an emphasis on feature engineering and model fitting

Section IV: Big Data Applications
7. IoT foundations in Precision Agriculture and its Application.
8. Practical applications of Big Data-driven Smart farming
9. Practical applications of Smart & Precise irrigation
10. Weed or Disease Detection using AI/ML/Deep Learning techniques
11. Nutrient Stress Detection using AI/ML/Deep Learning techniques
12. Leaf Disease Detection using AI/ML/Deep Learning techniques
13. Efficient soil water management using AI/ML
14. Microclimatic Forecasting using AI/ML/Deep Learning techniques
15. AI/ML/Deep Learning techniques in precipitation forecast
16. Yield Prediction using AI/ML/Deep Learning techniques
17. Practical applications of Supply Chain Analytics in Agriculture
18. Efficient Farm Analytics using AI/ML/Deep Learning techniques

Section V: Challenges and prospects
19. Challenges and future pathway for big data analytics algorithms and applications in Agriculture

Über Autor(innen)

Srivastava, Prashant K
Prashant K. Srivastava is working at IESD, Banaras Hindu University, as a faculty and was affiliated with Hydrological Sciences, NASA Goddard Space Flight Center, as research scientist on SMAP satellite soil mois ture retrieval algorithm development, instrumentation, and simulation for various applications. He received his PhD degree from the Department of Civil Engineering, University of Bristol, Bristol, United Kingdom. Prashant was the recipient of several awards such as NASA Fellowship, USA; University of Maryland Fellowship, USA; Commonwealth Fellowship, UK; Early Career Research Award (ECRA, DST, India), CSIR, as well as UGC JRF-NET (2005, 2006). He is leading a number of projects funded from reputed agencies in India as well as world. He was also a collaborator with NASA JPL on SMAP soil mois ture calibration and validation as well as Scatsat-1, NISAR, AVIRIS-NG missions of India. Prashant made more than 200+ publications in peer-reviewed journals and published 14 books with reputed publishing house such as Springer, Taylor and Francis, AGU-Wiley, and Elsevier, and several book chapters with good cita tion index. He presented his work in several conferences and workshops and is acting as a convener for the last few years in EGU, Hydroinformatics (HIC), and other conferences. He is also acting as Regional Editor Asia-Geocarto International (T & F), Associate Editor-Journal of Hydrology (Elsevier), GIScience and Remote Sensing (T & F), Remote Sensing Applications: Society and Environment (Elsevier), Sustainable Environment (T & F), Water Resources Management (Springer), Frontiers Remote Sensing, Associate Editor- Remote Sensing-MDPI, Associate Editor- Environment, Development and Sustainability (Springer), Environmental Processes (Springer), Bull of Env and Sci Res.

Pandey, Manish K.
Manish K Pandey works in the Remote Sensing Laboratory at Banaras Hindu University in Varanasi, India.

Pradhan, Biswajeet
Biswajeet Pradhan is a distinguished professor at UTS School of Civil and Environmental Engineering. He is an international expert in data-driven modelling and a pioneer in combining spatial modelling with statistical and machine learning models for natural hazard predictions including landslides. He has a track record of outstanding research outputs, with over 600 journal articles. He is a highly interdisciplinary researcher with publications across 12 areas, listed as having 'Excellent' international collaboration status. He has been a Highly Cited Researcher for five consecutive years (2016-2020) and ranks fifth in the field of Geological & Geoenvironmental Engineering.