This book covers smart agricultural space and its further development with an emphasis on ultra-saving labor shortages using AI-based technologies. A transboundary approach, as well as artificial intelligence (AI) and big data for bioinformatics, are required to increase timeliness and supplement the labor shortages, ensure the safety of intangible labor migration system to achieve one of the sustainable development goals (SDG) to secure food security (Society 5.0, SDG 1 and 2). With this in mind, the book focuses on the solution through smart Internet of Things (IoT) and AI-based agriculture, such as automation navigation, insect infestation, and decreasing agricultural inputs such as water and fertilizer, to maintain food security while ensuring environmental sustainability. Readers will gain a solid foundation for developing new knowledge through the in-depth research and education orientation of the book on how the deployment of outdoor and indoor sensors, AI/machine learning (ML), and IoT setups for sensing, tracking, collection, processing, and storing information over cloud platforms is nurturing and driving the pace of smart agriculture outdoor and indoors at this current time. Furthermore, the book introduces the smart system for automation challenges that are important for an unmanned system for considering safety and security points. The book is designed for researchers, graduates, and undergraduate students working in any area of machine learning, deep learning in agricultural engineering, smart agriculture, and environmental science. The greatest care has been made to deliver a diverse range of resource areas, as well as enormous insights into the significance and scope of IoT, AI, and ML in the development of intelligent digital farming and smart agriculture, providing comprehensive information to the intended readers.
Chapter
1. Digital Innovations in Agri-Food Systems to Achieve SDGs and
Society 5.0.
Chapter
2. Short Strategic Notes (SSN): Smart Soil and Water
Management for Bioresources.
Chapter
3. Design of Navigation System for
Transportation Mobile Robot for Agricultural Farms.
Chapter
4. A New
Small-Scale Autonomous Multi-Crop Seeder.
Chapter
5. Automatic Navigation of
Pesticide Spraying Vehicle for Orchard Crops.
Chapter
6. Short Strategic
Notes (SSN): Advanced Machinery for Increasing Agriculture Productivity.-
Chapter
7. Navigation System for Autonomous Agricultural Vehicle for Orchard
Operations.
Chapter
8. Driver Safety System for Agricultural Machinery
Operations Using Deep Learning Algorithm.
Chapter
9. Navigation System for
Autonomous Agricultural Vehicle for Indoor Farms.
Chapter
10. Digital
Transformation of Horticultural Crops Pre-post-harvest Management Against
Pest Infestation in Sustainable Agricultural Productivity.
Chapter
11.
Challenges in Orchard Weeding Systems:A Perspectives of 3D-Camera and Lidar
Application Oriented Robots and their Potential.
Chapter
12. Development of
Automatic Navigation System for Mechanical Weeder in Cassava Field.
Chapter
13. Short Strategic Notes (SSN): Smart Automation System for Water Saving
Technology.
Chapter
14. Internet of Things (IoT)-based Smart Agriculture to
Increase Productivity and Aiming to Achieve SDGs.
Chapter
15. Maximizing
Water Use Efficiency Through Employing Smart Precision Irrigation
Technologies.
Chapter
16. AI-based IoT Greenhouse Control System for
Environmental Parameters.
Chapter
17. Advanced IoT Application in
Aquaculture for Fish Production Monitoring.
Chapter
18. Object Detection:
Challenges in Different Convolution Neural Network.
Chapter
19. Recognition
and Localization of Pears in Complex Orchards Using 3D Stereo Camera System
and Deep Learning Algorithm.
Chapter
20. Smart Automations for End-Effector
in Development of Horticultural Robots.
Chapter
21. Short Strategic
Notes(SSN): Advanced Integrated System for Green-house Livestocks and
Poultry Production.
Chapter
22. Fast and Non-Destructive Quail Egg Freshness
Assessment Using a Thermal Camera and Deep Learning.
Chapter
23. Conclusion:
The Future of Intelligence Systems for Sustainable Agri-Food Systems
Prioritizing SDGs and Society 5.0.
Tofael Ahamed is an Associate Professor from University of Tsukuba, Japan, performs research and supervises graduate students in the field of precision agriculture technology, agricultural robotics decision support systems and agricultural remote sensing. Tofael focuses on enabling smart application using Internet of Things and Artificial Intelligence in agriculture, where crop production varies spatially and temporally within the field boundaries depending on the soil, nutrient, and environmental conditions. He is also serving as one of the Associate Editors for the reputed journals of Computer and Electronics in Agriculture, Agricultural Information Research, Editorial Member for Asia-Pacific Journal of Regional Science. He is a Lead Author and Editor of number books, Guest Editor of Special Issues for Remote Sensing and Regional Application of Remote Sensing. Tofael has published in journals such as Computers and Electronics in Agriculture, BiosystemsEngineering, Transactions of ASABE, Sensors, Remote Sensing, and Journal of Japanese Society of Agricultural Machinery and Food Engineering. Tofael is recognized as one of the best faculty members for 2016 and 2022 at the University of Tsukuba, Japan for his outstanding contributions to research, education, university management and social contributions.
