Artificial Intelligence And Advanced Analytics For Food Security
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| Author | : Chandrasekar Vuppalapati |
| Publisher | : CRC Press |
| Total Pages | : 548 |
| Release | : 2023-07-17 |
| Genre | : Technology & Engineering |
| ISBN | : 1000919153 |
Climate change, increasing population, food-versus-fuel economics, pandemics, etc. pose a threat to food security to unprecedented levels. It has fallen upon the practitioners of agriculture and technologists of the world to innovate and become more productive to address the multi-pronged food security challenges. Agricultural innovation is key to managing food security concerns. The infusion of data science, artificial intelligence (AI), advanced analytics, satellites data, geospatial data, climatology, sensor technologies, and climate modeling with traditional agricultural practices such as soil engineering, fertilizers use, and agronomy are some of the best ways to achieve this. Data science helps farmers to unravel patterns in fertilizer pricing, equipment usage, transportation and storage costs, yield per hectare, and weather trends to better plan and spend resources. AI enables farmers to learn from fellow farmers to apply best techniques that are transferred learning from AI to improve agricultural productivity and to achieve financial sustainability. Sensor technologies play an important role in getting real-time farm field data and provide feedback loops to improve overall agricultural practices and can yield huge productivity gains. Advanced Analytics modeling is essential software technique that codifies farmers’ tacit knowledge such as better seed per soil, better feed for dairy cattle breed, or production practices to match weather pattern that was acquired over years of their hard work to share with worldwide farmers to improve overall production efficiencies, the best antidote to food security issue. In addition to the paradigm shift, economic sustainability of small farms is a major enabler of food security. The book reviews all these technological advances and proposes macroeconomic pricing models that data mines macroeconomic signals and the influence of global economic trends on small farm sustainability to provide actionable insights to farmers to avert any financial disasters due to recurrent economic crises.
| Author | : Chandrasekar Vuppalapati |
| Publisher | : Springer Nature |
| Total Pages | : 910 |
| Release | : 2022-09-16 |
| Genre | : Business & Economics |
| ISBN | : 3031087437 |
This book introduces readers to advanced data science techniques for signal mining in connection with agriculture. It shows how to apply heuristic modeling to improve farm-level efficiency, and how to use sensors and data intelligence to provide closed-loop feedback, while also providing recommendation techniques that yield actionable insights. The book also proposes certain macroeconomic pricing models, which data-mine macroeconomic signals and the influence of global economic trends on small-farm sustainability to provide actionable insights to farmers, helping them avoid financial disasters due to recurrent economic crises. The book is intended to equip current and future software engineering teams and operations research experts with the skills and tools they need in order to fully utilize advanced data science, artificial intelligence, heuristics, and economic models to develop software capabilities that help to achieve sustained food security for future generations.
| Author | : Chandrasekar Vuppalapati |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2024-10-15 |
| Genre | : Business & Economics |
| ISBN | : 9783111438412 |
This book presents some of the most important applications of Artificial Intelligence, Data Science and Machine Learning for questions arising in agriculture. The book introduces data sources and methods used to estimate crop yields and prices under different climate scenarios. The methods and models introduced in the book can be applied across a large set of concrete questions across technology, industry, economics and sustainablility.
| Author | : Khan, Mohammad Ayoub |
| Publisher | : IGI Global |
| Total Pages | : 352 |
| Release | : 2022-05-27 |
| Genre | : Technology & Engineering |
| ISBN | : 1668451433 |
Food is a necessary aspect of human life, and agriculture is crucial to any country’s global economy. Because the food business is essential to both a country’s economy and global economy, artificial intelligence (AI)-based smart solutions are needed to assure product quality and food safety. The agricultural sector is constantly under pressure to boost crop output as a result of population growth. This necessitates the use of AI applications. Artificial Intelligence Applications in Agriculture and Food Quality Improvement discusses the application of AI, machine learning, and data analytics for the acceleration of the agricultural and food sectors. It presents a comprehensive view of how these technologies and tools are used for agricultural process improvement, food safety, and food quality improvement. Covering topics such as diet assessment research, crop yield prediction, and precision farming, this premier reference source is an essential resource for food safety professionals, quality assurance professionals, agriculture specialists, crop managers, agricultural engineers, food scientists, computer scientists, AI specialists, students, libraries, government officials, researchers, and academicians.
| Author | : Haoyu Niu |
| Publisher | : Springer Nature |
| Total Pages | : 243 |
| Release | : |
| Genre | : |
| ISBN | : 3031526457 |
| Author | : Syed Nisar Hussain Bukhari |
| Publisher | : CRC Press |
| Total Pages | : 301 |
| Release | : 2024-06-13 |
| Genre | : Computers |
| ISBN | : 1040037232 |
In the dynamic realm of agriculture, artificial intelligence (AI) and machine learning (ML) emerge as catalysts for unprecedented transformation and growth. The emergence of big data, Internet of Things (IoT) sensors, and advanced analytics has opened up new possibilities for farmers to collect and analyze data in real-time, make informed decisions, and increase efficiency. AI and ML are key enablers of data-driven farming, allowing farmers to use algorithms and predictive models to gain insights into crop health, soil quality, weather patterns, and more. Agriculture is an industry that is deeply rooted in tradition, but the landscape is rapidly changing with the emergence of new technologies. Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture is a comprehensive guide that explores how the latest advances in technology can help farmers make better decisions and maximize yields. It offers a detailed overview of the intersection of data, AI, and ML in agriculture and offers real-world examples and case studies that demonstrate how these tools can help farmers improve efficiency, reduce waste, and increase profitability. Exploring how AI and ML can be used to achieve sustainable and profitable farming practices, the book provides an introduction to the basics of data-driven farming, including an overview of the key concepts, tools, and technologies. It also discusses the challenges and opportunities facing farmers in today’s data-driven landscape. Covering such topics as crop monitoring, weather forecasting, pest management, and soil health management, the book focuses on analyzing data, predicting outcomes, and optimizing decision-making in a range of agricultural contexts.
| Author | : Chandrasekar Vuppalapati |
| Publisher | : Springer Nature |
| Total Pages | : 481 |
| Release | : |
| Genre | : |
| ISBN | : 3031560973 |
| Author | : Chandrasekar Vuppalapati |
| Publisher | : Springer Nature |
| Total Pages | : 836 |
| Release | : 2023-11-15 |
| Genre | : Computers |
| ISBN | : 3031383990 |
Specialty crops are defined as fruits and vegetables, tree nuts, dried fruits, horticulture, and nursery crops including floriculture. The value of specialty crop production in the United States accounted for 18.44 % of the $433.569 billion in agriculture cash receipts collected in 2021. In 2020, that ratio was 21.47% of the $363.464 billion. Specialty crops are gaining increasing attention across nation as demonstrated in the 2018 farm bill (Agricultural Act of the 2018 Farm Bill (P.L. 115-334)) with the increased number of provisions addressing specialty crop issues, reflecting their growing role in the global economy. The cultivation of Specialty crops, nevertheless, has its own challenges. Specialty crops are generally more sensitive to climatic stressors and require more comprehensive management compared to traditional row crops. Specialty crops face significant financial risks threatening US$1.6 Trillion global market due to their higher water demand. The mission of the book is to prepare current and future software engineering teams, agriculture students, economists, macroeconomists with the skills and tools to fully utilize advanced data science, artificial intelligence, climate patterns, and economic models to develop software capabilities that help to achieve Specialty crops and economic sustainability, through improved productivity for years to come and ensure enough food for the future of the planet and generations to come!
| Author | : Elena G. Popkova |
| Publisher | : Springer Nature |
| Total Pages | : 153 |
| Release | : 2023-03-25 |
| Genre | : Business & Economics |
| ISBN | : 3031235118 |
This book explores issues of agricultural development and the provision of food security, providing a core framework and recommendations for implementing sustainable development goals in these areas. Focused on the period up to 2030 to match the timeline of the SDGs, the book surveys the current landscape and the prospects for agricultural development, demonstrating how Agriculture 4.0 based on AI and deep learning must follow from digitalization as the next step to ensuring food security. It brings together research based on analytical and statistical data, including the IMD World Digital Competitiveness Report and the Food Security Report from the Economist Intelligence Unit, and reliable mathematical tools including correlation and regression analysis, forecasting with histogramming, probability estimates, and the simplex method, as well as Game Theory methodology and the hierarchical procedure method of Thomas Lorie Saaty to forecast international scenarios for food security in the future economy. Containing theoretical and practical insights, the book will be of interest to those studying agricultural economics, the digital economy, and concepts of Agriculture 4.0 and Industry 4.0.
| Author | : Chandrasekar Vuppalapati |
| Publisher | : Springer Nature |
| Total Pages | : 611 |
| Release | : 2021-10-04 |
| Genre | : Business & Economics |
| ISBN | : 3030774856 |
This book discusses machine learning and artificial intelligence (AI) for agricultural economics. It is written with a view towards bringing the benefits of advanced analytics and prognostics capabilities to small scale farmers worldwide. This volume provides data science and software engineering teams with the skills and tools to fully utilize economic models to develop the software capabilities necessary for creating lifesaving applications. The book introduces essential agricultural economic concepts from the perspective of full-scale software development with the emphasis on creating niche blue ocean products. Chapters detail several agricultural economic and AI reference architectures with a focus on data integration, algorithm development, regression, prognostics model development and mathematical optimization. Upgrading traditional AI software development paradigms to function in dynamic agricultural and economic markets, this volume will be of great use to researchers and students in agricultural economics, data science, engineering, and machine learning as well as engineers and industry professionals in the public and private sectors.
