Data Science for Agricultural Innovation and Productivity

• Autore: Gowrishankar S
• Pubblicazione: Sharjah : , : Bentham Science Publishers, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (229 pages)
• Soggetto topico: Agriculture - Data processing; Precision farming
• Altri autori: IbrahimHamidah VeenaA
• Nota di contenuto: Cover -- Title -- Copyright -- End User License Agreement -- Contents -- Foreword -- Preface -- List of Contributors -- Digital Twin for Smart Farming -- Galiveeti Poornima1,*, Deepak S. Sakkari1 and Sukruth Gowda M.A.1 -- INTRODUCTION -- TECHNOLOGIES USED IN SMART FARMING -- DEFINITION OF DIGITAL TWIN -- DIGITAL TWIN TYPOLOGY -- DIGITAL TWINS IN FARM MANAGEMENT -- APPLICATION OF DIGITAL TWINS IN SMART FARMING -- USE CASES -- CONCLUSION -- FUTURE SCOPE -- REFERENCES -- Deep Learning Models for Prediction of Disease in Lycopersicum -- Nakatha Arun Kumar1,* and Sathish S. Kumar2 -- INTRODUCTION -- RELATED WORK -- Pretrained Models -- VGGNet -- GoogleNet -- ResNet -- Inception -- Methodology -- RESULTS -- CONCLUDING REMARKS -- REFERENCES -- A Smart Hydroponics System for Sustainable Agriculture -- Supriya Jaiswal1,*, Gopal Rawat1, Chetan Khadse1 and Sohit Sharma1 -- INTRODUCTION -- TYPES OF FARMING -- Hydroponic Farming -- Scope and Challenges -- ADVANTAGES AND LIMITATIONS OF HYDROPONICS -- GOVERNMENT INITIATIVES AND RECENT RESEARCH TECHNOLOGIES TO SUPPORT FARMING -- COMPONENTS AND STRUCTURE OF SMART HYDROPONIC FARMING -- Supply System for Automated Water Pumping -- Sensor Network for Smart Farming -- Internet of Hydroponics (IoH): Architecture and Working -- MACHINE LEARNING AND DATA MINING IN HYDROPONICS -- Applications of Machine Learning -- Challenges Faced in Machine Learning -- Use Cases of ML in Hydroponics -- Future Area of Research in Hydroponics -- CONCLUDING REMARKS -- REFERENCES -- Agriculture Robotics -- Bogala Mallikharjuna Reddy1,* -- INTRODUCTION -- AUTOMATION OF FARMING -- PRECISION AGRICULTURE ROBOTICS -- IOT-BASED SMART AGRICULTURE -- ROBOTICS IN AGRICULTURE -- Classification of Robots -- Agriculture Robotics Evolution -- Cooperative Agricultural Robotics -- APPLICATIONS OF AGRICULTURE ROBOTICS -- Farmland Preparation. ; Sowing and Planting -- Inspection -- Spraying and Plant Treatment -- Yield and Phenotype Estimating -- Harvesting -- Commercial Agricultural Robots -- CHALLENGES IN AGRICULTURE ROBOTICS -- CONCLUSION AND FUTURE OUTLOOK -- REFERENCES -- Internet of Green Things (IoGT) for Carbon-Free Economy -- Sadiq Mohammed Sanusi1,*, Singh Invinder Paul2 and Ahmad Muhammad Makarfi3 -- INTRODUCTION -- MAJOR IMPACTS OF CLIMATE CHANGE ON HEALTH -- THE LOW CARBON ECONOMY AND ICT -- GLOBAL RISKS 2019 REPORT: FAILURE OF CLIMATE CHANGE MITIGATION AND ADAPTATION -- INTERNET OF HEALTH THINGS (IOHT) -- IOT APPLICATION IN COMBATING CLIMATE CHANGE -- Agriculture -- Stopping Illegal Logging and Deforestation -- Smart Cities -- Utilities -- Waste Control -- Transportation and Traffic -- Data About the Climate and the Environment -- Automated Buildings and Energy Storage -- IoT-Based Environment Solutions Examples -- IOT ENVIRONMENTAL TECHNOLOGY PROJECT CASE STUDY BY ERICSSON -- INDUSTRIAL INTERNET OF THINGS (IIOTS) IS THE FUTURE -- Challenges of Industrial Internet of Things (IIoT) -- Energy Savings -- Performance in Real-Time -- Interoperability and Coexistence -- Privacy and Security -- IOT FUTURE PERSPECTIVES FOR CARBON-FREE ECOLOGY -- CONCLUSION -- REFERENCES -- Revolutionizing Precision Agriculture Using Artificial Intelligence and Machine Learning -- Jayalakshmi Murugan1,*, Maharajan Kaliyanandi1 and Carmel Sobia M.2 -- Untitled -- INTRODUCTION -- BACKGROUND -- LITERATURE SURVEY -- DATA SETS -- FEATURE EXTRACTION FOR DISEASE IDENTIFICATION -- PERFORMANCE COMPARISON -- CONCLUSION AND FUTURE WORK -- REFERENCES -- Internet of Fisheries Things (IOFT) for Blue Economy & -- Ecosystem -- Sadiq Mohammed Sanusi1,*, Singh Invinder Paul2 and Ahmad Muhammad Makarfi3 -- INTRODUCTION -- DIGITALIZATION OF AQUACULTURE -- Definition of Digitization in the Fisheries Sector. ; Digitalization Use in the Aquaculture Sector -- Location Determination Using GIS -- Making Use of Technology for Automatic Feeders -- Automatic Evaluation of Water Quality -- Marketing Aquaculture Products Online -- IOT FOR MONITORING SHRIMP/FISH POND -- APPS FOR FISHERIES AND AQUACULTURE USE OF IOT IN MOBILE -- Aquaculture-Related Mobile Applications -- Marine Fisheries-Related Mobile Apps -- Mobile Apps for Marketing -- FOOD SUPPLY CHAIN MANAGEMENT IN THE AGE OF DIGITALIZATION -- Technologies that can be Employed in the Context of the IoT for the Supply Chain -- CONCLUSION -- REFERENCES -- Tea Rhizospheres and Their Functional Role in Tea Gardens -- Rwitabrata Mallick1,* -- INTRODUCTION -- METHODOLOGY -- Withering -- Rolling -- Fermentation -- Drying -- Rhizosphere -- DISCUSSION -- Study Area Under Kurseong subdivision -- CONCLUDING REMARKS -- REFERENCES -- Applications of Smart Farming Sensors: A Way Forward -- Prasenjit Pal1,* and Sandeep Poddar2 -- INTRODUCTION -- SMART FARMING: AN EMERGING CONCEPT -- DIFFERENT SENSORS USED IN AGRICULTURE -- Optical Sensors: -- Electrochemical Sensors: -- Dielectric Sensors: -- Location Sensors in Agriculture: -- Electronic Sensors -- Airflow Sensors -- Sensors used in Agriculture -- WHAT ARE THE BENEFITS OF SENSORS IN AGRICULTURE? -- Excelled Efficiency -- Expansion -- Reduced Sources -- Cleaning Procedure -- Agility -- Improved Production and Quality -- Monitoring Weather Situations -- Greenhouse Automation -- Crop Tracking -- Drones -- APPLICATIONS OF SENSORS IN FARMING -- Applications in Animal and dairy science: -- CHALLENGES TO ADOPT SENSOR BASED APPLICATIONS -- CONCLUSION -- REFERENCES -- An Overview of Building a Global Data Area on the Web for Farming -- R. Sapna1,*, Ravva Akash Guptha2, Paritala Venkateswara Rao2 and Raavi Sai Pranay2 -- INTRODUCTION -- ABOUT THE WEB AND ITS HISTORY. ; First Era (Web 1.0) -- Second Era (Web 2.0) -- Third Era (WEB 3.0) -- SEMANTIC WEB STACK -- Semantic Web Technologies -- Hypertext Web Technologies -- Standardized Semantic Web Technologies -- Unrealized Semantic Web Technologies -- Machine Learning on Semantic Web -- Semantic Web and Agriculture -- The Semantic Web Technology for Agriculture -- The Semantic Resources for Agriculture -- CONCLUSION -- REFERENCES -- Subject Index -- Back Cover.
• Sommario/riassunto: Data Science for Agricultural Innovation and Productivity explores the transformation of agriculture through data-driven practices. This comprehensive book delves into the intersection of data science and farming, offering insights into the potential of big data analytics, machine learning, and IoT integration. Readers will find a wide range of topics covered in 10 chapters, including smart farming, AI applications, hydroponics, and robotics. Expert contributors, including researchers, practitioners, and academics in the fields of data science and agriculture, share their knowledge to provide readers with up-to-date insights and practical applications. The interdisciplinary emphasis of the book gives a well-rounded view of the subject. With real-world examples and case studies, this book demonstrates how data science is being successfully applied in agriculture, inspiring readers to explore new possibilities and contribute to the ongoing transformation of the agricultural sector. Sustainability and future outlook are the key themes, as the book explores how data science can promote environmentally conscious agricultural practices while addressing global food security concerns. Key Features: Focus on data-driven agricultural practices Comprehensive coverage of modern farming topics with an interdisciplinary perspective ‍ Expert insights Sustainability and future outlook Highlights practical applications Data Science for Agricultural Innovation and Productivity is an essential resource for researchers, data scientists, farmers, agricultural technologists, students, educators, and anyone with an interest in the future of farming through data-driven agriculture. Readership Researchers, data scientists, farmers, agricultural technologists, students, educators, and general readers.
• Titolo autorizzato: Data Science for Agricultural Innovation and Productivity
• ISBN: 9789815196177; 9815196170
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910985653003321
• Lo trovi qui: Univ. Federico II