Federated Learning for Smart Agriculture and Food Quality Enhancement

Padmesh Tripathi, Bhanumati Panda, Shanthi Makka, Reeta Mishra, S. Balamurugan, Sheng-Lung Peng

• Publisher: Wiley-Scrivener
• Year: 2025
• Print ISBN: 9781394338696
• eBook ISBN: 9781394338702
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Agronomy
• Index: Technology & Engineering

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.

Table of contents

• Cover
• Table of Contents
• Series Page
• Title Page
• Copyright Page
• Preface
• 1 Harnessing the Power of Federated Learning for Agricultural Innovation
• 2 Federated Learning–Based Food Calorie Estimation
• 3 Federated Learning for Food Safety and Compliance
• 4 Federated Learning and Its Applications in Smart Agricultural Processes
• 5 Federated Learning in Food Inspection and Grading
• 6 Federated Learning–Based Approach for Crop Recommendation and Market Stability in Agriculture
• 7 Federated Learning for Plant Disease Detection
• 8 Federated Learning for Decentralized Smart Farm Network Applications: Enhancing Crop Classification Performance
• 9 Revolutionizing Agriculture Yields through Federated Learning
• 10 Federated Learning in Smart Agriculture: Applications, Challenges, and Solutions
• 11 Federated Learning and Its Impact on Decision-Making in Smart Agriculture
• 12 A Federated Differential Privacy Model with Pyramid Residual Network for Predicting Crop Yields
• 13 A Review on Detection of Adulteration in Food Using Federated Learning
• 14 Federated Learning for Crop Yield Prediction
• Index
• Also of Interest
• End User License Agreement