Federated Learning for Smart Agriculture and Food Quality Enhancement

This essential book provides a comprehensive, expert-led guide on how federated learning can revolutionize crop yield, enhance resource management, and ensure a pathway to sustainable food quality and safety.

The convergence of artificial intelligence, machine learning, and data science with agriculture and food, provides remarkable opportunities to improve quality, sustainability, and productivity in the agricultural sector. Federated Learning is a promising technology that has emerged at this intersection. In the context of smart agriculture, federated learning holds promise for improving crop yield, resource management, and decision-making. Additionally, federated learning provides greater clarity and understanding in the world of agriculture, encouraging stakeholders to explore and adopt this technology for improved farm management.

Readers will find the book:

• Explores the integration of federated learning, a novel machine learning technique, into the realm of agriculture and food quality enhancement, showcasing the latest advancements;
• Introduces real-world applications of federated learning in agriculture, and demonstrates the way this technology can transform farming practices, crop monitoring, pest control, and food quality assurance;
• By bridging the fields of agriculture, machine learning, and food science, it offers a holistic perspective on leveraging technology to address challenges in food production and quality management;
• Emphasizes the importance of sustainability in agriculture, exploring how federated learning can contribute to more efficient resource utilization, reduced environmental impact, and the overall sustainability of food production systems;
• Discusses the future directions of smart agriculture and food quality enhancement, envisioning how federated learning and other emerging technologies can continue to shape the industry and address evolving challenges.

Audience

Agriculture specialists, agricultural engineers, professionals associated with food safety, crop managers, quality assurance professionals, IT professionals, data scientists, and academics working towards improved quality and sustainability in agriculture.

Padmesh Tripathi, PhD is a Professor of Mathematics in the Department of AI and Data Science at the Delhi Technical Campus, Greater Noida, India with more than 24 years of teaching experience. He has published several articles in reputed journals, book chapters as well as several patents.

Bhanumati Panda, PhD is an Associate Professor in the Academy of Business and Engineering Science’s Engineering College, Ghaziabad, UP, India with more than two decades of teaching experience. Her teaching and research expertise spans a wide range of subjects, including engineering mathematics, operations research, numerical analysis, complex analysis, discrete mathematics, real analysis, and statistics.

Shanthi Makka, PhD is a Professor in the Department of Computer Science and Engineering and the Head of the Teaching Learning Center at the Vardhaman College of Engineering, Hyderabad, India with more than 19 years of academic experience. She has published one book, more than 28 papers in reputed international journals and conferences, and several patents.

Reeta Mishra is an Assistant Professor in the School of Computer Science and Engineering at IILM University, Greater Noida, India. She has contributed to many research papers in reputed national and international journals and published five patents.

S. Balamurugan, PhD is the Director of Intelligent Research Consultancy Services in Coimbatore India. He has published more than 70 books, 300 articles in international journals and conferences, and 300 patents, and serves as a research consultant to many companies and startups.

Sheng-Lung Peng, PhD is a Professor and the Director of the Department of Creative Technologies and Product Design at the National Taipei University of Business,Taiwan. He has published over 100 articles in international journals and conferences.