This book reviews recent innovations in the smart agriculture space that use the Internet of Things (IoT) and sensing to deliver Artificial Intelligence (AI) solutionsto agricultural productivity in the agricultural production hubs. In this regard, South and Southeast Asia are one of the major agricultural hubs of the world, facing challenges of climate change and feeding the fast-growing population. To address such challenges, a transboundary approach along with AI and BIG data for bioinformatics are required to increase yield and minimize pre- and post-harvest losses in intangible climates to drive the sustainable development goal (SDG) for feeding a major part of the 9 billion population by 2050 (Society 5.0 SDG 1 & 2). Therefore, this book focuses on the solution through smart IoT and AI-based agriculture including pest infestation and minimizing agricultural inputs for in-house and fields production such as light, water, fertilizer and pesticides to ensure food security aligns with environmental sustainability. It provides a sound understanding for creating new knowledge in line with comprehensive research and education orientation on how the deployment of tiny sensors, AI/Machine Learning (ML), controlled UAVs, and IoT setups for sensing, tracking, collection, processing, and storing information over cloud platforms for nurturing and driving the pace of smart agriculture in this current time.
The book will appeal to several audiences and the contents are designed for researchers, graduates, and undergraduate students working in any area of machine learning, deep learning in agricultural engineering, smart agriculture, and environmental science disciplines. Utmost care has been taken to present a varied range of resource areas along with immense insights into the impact and scope of IoT, AI and ML in the growth of intelligent digital farming and smart agriculture which will give comprehensive information to the targeted readers.
Chapter 1. IoT x AI: Introducing Agricultural Innovation for Global Food
Production.
Chapter
2. Transforming Controlled Environment Plant Production
toward Circular Bioeconomy Systems.
Chapter
3. Artificial Lighting Systems
for Plant Growth and Development in Indoor Farming.
Chapter
4. An IoT-based
Precision Irrigation System to Optimize Plant Water Requirements for Indoor
and Outdoor Farming Systems.
Chapter
5. Artificial Intelligence & Internet
of Things: Application in Urban Water Management.
Chapter 6.Purification of
Agricultural Polluted Water Using Solar Distillation and Hot Water Producing
with Continuous Monitoring Based on IoT.
Chapter
7. Long Range Wide Area
Network (LoRaWAN) for Oil Palm Soil Monitoring.
Chapter
8. Application of
Smart Machine Vision in Agriculture, Forestry, Fishery, and Animal
Husbandry.
Chapter
9. Artificial Intelligence in Agriculture: Commitment to
Establish Society 5.0 .
Chapter
10. Potentials of Deep Learning Frameworks
for Tree Trunk Detection in Orchard to Enable Autonomous Navigation System.-
Chapter
11. Real Time Pear Fruit Detection and Counting Using YOLOv4 Models
and Deep SORT.
Chapter
12. Pear Recognition in an Orchard from 3D Stereo
Camera Datasets to Develop an Autonomous Mechanism Compared with Deep
Learning Algorithms.
Chapter
13. Thermal Imaging and Deep Learning Object
Detection Algorithms for Early Embryo Detection A Methodology Development
Addressed to Quail Precision Hatching.
Chapter
14. Intelligent Sensing and
Robotic Picking of Kiwifruit in Orchard.
Chapter
15. Low-cost Automatic
Machinery Development to Increase Timeliness and Efficiency of Operation for
Small Scale Farmers to Achieve SDGs.
Chapter
16. Vision-based Leader Vehicle
Trajectory Tracking for Multiple Agricultural Vehicles.
Chapter
17.
Autonomous Robots in Orchard Management: Present status and future trends.-
Chapter
18. Comparing Soil Moisture Retrieval from Water Cloud Model and
Neural Network Using PALSAR-2 for Oil Palm Estates.
Chapter
19. Development
of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles
Using a Machine Learning Approach.
Chapter
20. Basal Stem Rot Disease
Classification by Machine Learning Using Thermal Images and an Imbalanced
Data Approach.
Chapter
21. Early Detection of Plant Disease Infection using
Hyperspectral Data and Machine Learning.
Chapter
22. The Spectrum of
Autonomous Machinery Development to Increase Agricultural Productivity for
Achieving Society 5.0 in Japan.
Tofael Ahamed is an Associate Professor, at the Faculty of Life and Environmental Sciences, the University of Tsukuba, which is one of the leading universities in Japan. Dr. Ahamed performs research in the field of precision agriculture technology, agricultural robotics, and decision support systems. He focuses on enabling smart applicationsusing the Internet of Things (IoT) and Artificial Intelligence (AI) in agriculture, where crop production varies spatially and temporally within the field boundaries depending on the soil and environmental conditions. Dr. Ahamed is also member of the American Society of Agricultural and Biological Engineers, Japanese Society of Agricultural Machinery, Food Engineers, Japanese Society of Agricultural Information, and Japan Section of Regional Science Association. He is also serving as one of the Associate Editors for Computer and Electronics in Agriculture, Agricultural Information Research, Editorial Member for Asia-Pacific Journal of Regional Science, Author and Editor of serval books and Guest Editor of special issues for Remote Sensing.
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