This book explores the cutting-edge intersection of artificial intelligence, fuzzy logic, and sustainable agriculture. Written in accessible language, this comprehensive volume brings together insights from leading experts to address the pressing challenges facing global food systems in the era of climate change. From multi-factorial yield forecasting to adaptive fertilizer prescription, the book covers a wide range of innovative applications. It examines how emerging technologies like satellite imagery analysis, precision farming, and even the agricultural metaverse are revolutionizing…mehr
This book explores the cutting-edge intersection of artificial intelligence, fuzzy logic, and sustainable agriculture. Written in accessible language, this comprehensive volume brings together insights from leading experts to address the pressing challenges facing global food systems in the era of climate change. From multi-factorial yield forecasting to adaptive fertilizer prescription, the book covers a wide range of innovative applications. It examines how emerging technologies like satellite imagery analysis, precision farming, and even the agricultural metaverse are revolutionizing farming practices. Readers will discover how these advanced tools are enhancing crop resilience, optimizing resource use, and improving overall agricultural sustainability. More than just a technical guide, "Smart Farming" also delves into the economic dynamics of technology adoption in agriculture and discusses the policy interventions needed to accelerate this transformation. Whether you're a farmer, researcher, policymaker, or student, this book offers valuable insights into creating a more resilient and efficient global food system. It presents a vision of the future where technology and nature work in harmony to feed a growing world population while preserving our planet's resources. Agriculture has come a long way from its ancient roots of manual labor and basic tools. Over centuries, it progressed through mechanization with tractors and machinery, and in recent decades, has entered the digital age. This latest evolution, known as "smart farming," combines sensors, data analysis, and automation to make farming more precise and efficient. At the forefront of this revolution are Artificial Intelligence (AI) and fuzzy logic. AI systems can perform complex tasks like visual analysis and decision-making, while fuzzy logic deals with approximate reasoning, making both particularly suited to the complex, variable world of agriculture.
Dr. Pushan Kumar Dutta is an Associate Professor Grade at Amity University Kolkata in the Electronics and Communication Engineering department. He holds a Ph.D. from Jadavpur University and completed a post-doctorate as an Erasmus Mundus Scholar under the European Union Leaders Program (2015–2016) at the University of Oradea. His research interests include data mining, AI, edge computing, and predictive analytics, with applications in smart cities, healthcare, and sustainability. Dr. Dutta has published over 114 Scopus-indexed articles and numerous works in IEEE Xplore and Springer Lecture Notes. A recipient of the ‘Mentor of Change’ by NITI Aayog and other awards, he is known for his innovative teaching methods, two Indian patents, and international contributions, including winning an international white paper contest. Dr. Rashmi Singh, a professor in the Department of Mathematics at Amity Institute of Applied Sciences, Amity University Uttar Pradesh, Noida, has dedicated over two decades to academic excellence and mathematical research. Her impactful career includes publishing more than 45 research articles, supervising 4 Ph.D. students, and mentoring over 30 Masters and undergraduate students. She also worked as a research assistant at Allahabad Mathematical Society for three years, from 2002 to 2005. She is recognized as an editor and reviewer for various referred and peer-reviewed national and international journals. Dr. Singh serves as the Brand Ambassador for Bentham Sciences journals, guest editor for IEEE Transactions on Consumer Electronics and Measurement Sensors, and holds editorial positions in various prestigious journals, including Scientific Reports. A. K. Haghi, Ph.D., is a retired professor and has written, co-written, edited or co-edited more than 1000 publications, including books, book chapters, and papers in refereed journals with over 4500 citations. He is an Honorary Research Associate (HRA) at the Chemistry Centre, University of Coimbra, Portugal. He is the Founder and former Editor-in-Chief of the International Journal of Chemoinformatics and Chemical Engineering and Polymers Research Journal. He has acted as an editorial board member of many international journals. Dr. Ahmed Alhussaini Hamad is an Egyptian academic and researcher specializing in Food Hygiene and Quality Control. He has over a decade of experience in veterinary public health and food safety, with research focusing on food hygiene, biofilm control, and innovative preservation technologies. He has authored and co-authored several scientific papers, reviews, and book chapters in reputable international journals. Dr. Ahmed serves as a peer reviewer and editorial board member for leading publishers. His contributions extend to national research projects funded by Egypt’s STDF and Ministry of Higher Education, promoting advancements in food safety and quality assurance.
Inhaltsangabe
Advancement in Smart Farming with Respect to Industry 4.0: Harnessing AI to Reshape the Future of Agriculture. Block chain Technology in Agriculture. Factoring Satellite and Sensor Integration for Sustainable Crop Monitoring. Revolutionizing Beekeeping: IoT Driven Remote Hive Monitoring for Sustainable and Proactive Apiculture Management. Shaping the Future of Farming with Generative AI. Advancements in Smart Farming: Using Internet of Things, Artificial Intelligence, Machine Learning, and Deep Learning. Leveraging Machine Learning and Climate Data for Predictive Crop Modeling and Smart Farming Advisory Systems. Wavelength Selective Partial Least Squares Regression of Near Infrared Spectra for Biopolymer Prediction in Leaves. A Comprehensive Analysis of GIS Based Fuzzy Optimization Techniques and Their Impact on Precision Agriculture and Resource Management. Explainable AI models for Transparent AND Trustworthy Decision making in Precision agriculture. LiDAR and IoT Sensor Networks for Climate Aware Yield Forecasting in Agriculture. Real time temperature monitoring and nutrient delivery for optimal yield using aeroponic systems. AI enabled Early Anomaly Detection of Crop Diseases at Real Field Environments. Smart Crop Advisory Systems for Pest and Disease Management. Classification of Healthy and Unhealthy Bell Pepper Crops and Leaves for Precision Agriculture Using Mobile Net V2. DenseNet 169 and Inception V3 Neural Network for Peanut Leaf Disease Detection. Effective Harvest Suggestion System based on Expert System. AI Driven Solutions for Precision Farming: Crop Selection, Fertilizer Optimization, and Disease Detection using Machine Learning & Deep Learning Models. Precision Agriculture and Resource Optimization. Integrating Artificial Intelligence in Climate Aware Yield Forecasting for Sustainable Precision Agriculture. AI Powered Plant Disease Detection and Management in Agriculture. Cucurbita Pepo Disease Classification Through Attention Enhanced Deep Learning Models. Influence of FinTech Driven Smart Farming and Crop Management on Cost Effective Sustainable Growth. Policy, Regulation and International Collaboration to Foster Sustainable Agriculture. Driving Zero Carbon Agri Tourism Innovations for Sustainable Growth. Food Processing and Preservation: Techniques and Innovations for Reducing Food Waste. Designing and Implementing Green Energy Systems for Agriculture. Integrating AI Powered Soil Health Monitoring with Variable Rate Technology for Sustainable Resource Optimization in Agriculture. Vision for the Future of Agriculture. Smart farming in developing economies: Its economic Impacts and policy challenges for farmers. Convolutional Neural Network for Agricultural Terrain Mapping. Sugarcane Leaf Disease Detection Using Deep Learning Approaches. Deep Learning Framework for Early Detection and Management of Plant Diseases. AQUACROP: IOT enhanced Smart irrigation system. Drip by Click: Sustainable Irrigation through AI Enhanced Digital Marketing in Agriculture 5.0. Leaf Guard: Harnessing Deep Learning for Early Detection of Tea Leaf Diseases. Agro Scan – A Real Time Plant Disease Detection. AGRINEXUS: The Hub for Agriculture and Financial Services.
Advancement in Smart Farming with Respect to Industry 4.0: Harnessing AI to Reshape the Future of Agriculture. Block chain Technology in Agriculture. Factoring Satellite and Sensor Integration for Sustainable Crop Monitoring. Revolutionizing Beekeeping: IoT Driven Remote Hive Monitoring for Sustainable and Proactive Apiculture Management. Shaping the Future of Farming with Generative AI. Advancements in Smart Farming: Using Internet of Things, Artificial Intelligence, Machine Learning, and Deep Learning. Leveraging Machine Learning and Climate Data for Predictive Crop Modeling and Smart Farming Advisory Systems. Wavelength Selective Partial Least Squares Regression of Near Infrared Spectra for Biopolymer Prediction in Leaves. A Comprehensive Analysis of GIS Based Fuzzy Optimization Techniques and Their Impact on Precision Agriculture and Resource Management. Explainable AI models for Transparent AND Trustworthy Decision making in Precision agriculture. LiDAR and IoT Sensor Networks for Climate Aware Yield Forecasting in Agriculture. Real time temperature monitoring and nutrient delivery for optimal yield using aeroponic systems. AI enabled Early Anomaly Detection of Crop Diseases at Real Field Environments. Smart Crop Advisory Systems for Pest and Disease Management. Classification of Healthy and Unhealthy Bell Pepper Crops and Leaves for Precision Agriculture Using Mobile Net V2. DenseNet 169 and Inception V3 Neural Network for Peanut Leaf Disease Detection. Effective Harvest Suggestion System based on Expert System. AI Driven Solutions for Precision Farming: Crop Selection, Fertilizer Optimization, and Disease Detection using Machine Learning & Deep Learning Models. Precision Agriculture and Resource Optimization. Integrating Artificial Intelligence in Climate Aware Yield Forecasting for Sustainable Precision Agriculture. AI Powered Plant Disease Detection and Management in Agriculture. Cucurbita Pepo Disease Classification Through Attention Enhanced Deep Learning Models. Influence of FinTech Driven Smart Farming and Crop Management on Cost Effective Sustainable Growth. Policy, Regulation and International Collaboration to Foster Sustainable Agriculture. Driving Zero Carbon Agri Tourism Innovations for Sustainable Growth. Food Processing and Preservation: Techniques and Innovations for Reducing Food Waste. Designing and Implementing Green Energy Systems for Agriculture. Integrating AI Powered Soil Health Monitoring with Variable Rate Technology for Sustainable Resource Optimization in Agriculture. Vision for the Future of Agriculture. Smart farming in developing economies: Its economic Impacts and policy challenges for farmers. Convolutional Neural Network for Agricultural Terrain Mapping. Sugarcane Leaf Disease Detection Using Deep Learning Approaches. Deep Learning Framework for Early Detection and Management of Plant Diseases. AQUACROP: IOT enhanced Smart irrigation system. Drip by Click: Sustainable Irrigation through AI Enhanced Digital Marketing in Agriculture 5.0. Leaf Guard: Harnessing Deep Learning for Early Detection of Tea Leaf Diseases. Agro Scan – A Real Time Plant Disease Detection. AGRINEXUS: The Hub for Agriculture and Financial Services.
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