IoT and AI in Agriculture: Self- sufficiency in Food Production to Achieve Society 5.0 and SDG's GloballyКНИГИ » СЕТЕВЫЕ ТЕХНОЛОГИИ
Название: IoT and AI in Agriculture: Self-sufficiency in Food Production to Achieve Society 5.0 and SDG's Globally Автор: Tofael Ahamed Издательство: Springer Год: 2023 Страниц: 469 Язык: английский Формат: pdf (true) Размер: 21.3 MB
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 Internet of Things (IoTs) technology serves as an alternative to better manage the main agricultural resources and water through smart irrigation. Additionally, the use of artificial intelligence (AI) and machine learning (ML) provides a new opportunity to improve the overall agriculture operations and production by involving real-time analysis and machinery automation. Therefore, the main purpose of the Chapter 1 is to highlight the future trend of agricultural innovation for realizing sustainable global food production.
In recent years, emerging cutting-edge technologies such as controlled environment agriculture (CEA), the Internet of Things (IoT), Machine Learning (ML), Artificial Intelligence (AI), Deep Learning (DL), unmanned aerial vehicles (UAV), and global positioning systems (GPS) have attracted much interest from both farmers and researchers to fulfill the rising demand for agricultural products and food. The adoption of these advanced technologies for remote and unmanned monitoring in agriculture fields, also by implementing solutions to create the most conducive environment for crop growth, has been proven to improve input management, reduce yield losses, and support growers and interveners in decision-making.
The combination of hardware and software technologies has optimized agricultural operations to improve production. Currently, there are many portable, low-cost, and power-efficient hardware and sensors with wireless connections that are widely implemented across both indoor and outdoor agriculture. The utilization of hardware and sensor networks to continuously monitor agricultural growth parameters such as temperature, relative humidity, and soil moisture provides farmers with essential information to allow better input management and plant monitoring and enhance quality and crop yield. Additionally, sophisticated hardware such as graphical processing units (GPUs) can process an enormous volume of data collected by these modules, as prompted by AI framework-based software.
Innovation toward modern agriculture, such as Artificial Intelligence (AI)-based systems, creates new opportunities and solutions to predict climate change hazards and minimize labor requirements. The Internet of Things (IoT) opened a new window with low bandwidth information sharing. In this regard, this book solely discusses AI and IoT systems and their potential outlined in the 23 chapters related to smart agriculture.
Chapter 8 reviews shortly the future of smart Machine Vision technology in agriculture, forestry, fisheries and animal husbandry to develop an automatic solutions to cater the labor shortages and replace human power. Chapter 9 discusses Artificial Intelligence (AI) in a general overview to open the discussion regarding this trending topic that has been changing our daily lives and society as well. This chapter unleashed the meaning and basic applications of AI, Machine Learning, and Deep Learning step by step in a time series of events, from their origins until the present. Furthermore, some of the applications of these new trends are explored for agricultural production to achieve the goals of sustainability and Society 5.0.
1. IoT ? AI: Introducing Agricultural Innovation for Global Food Production 2. Strategic Short Note: Transforming Controlled Environment Plant Production Toward Circular Bioeconomy Systems 3. Artificial Lighting Systems for Plant Growth and Development in Indoor Farming 4. An IoT-Based Precision Irrigation System to Optimize Plant Water Requirements for Indoor and Outdoor Farming Systems 5. Strategic Short Note: Artificial Intelligence and Internet of Things: Application in Urban Water Management 6. Purification of Agricultural Polluted Water Using Solar Distillation and Hot Water Producing with Continuous Monitoring Based on IoT 7. Long Range Wide Area Network (LoRaWAN) for Oil Palm Soil Monitoring 8. Strategic Short Note: Application of Smart Machine Vision in Agriculture, Forestry, Fishery, and Animal Husbandry 9. Artificial Intelligence in Agriculture: Commitment to Establish Society 5.0: An Analytical Concepts Mapping for Deep Learning Application 10. Potentials of Deep Learning Frameworks for Tree Trunk Detection in Orchard to Enable Autonomous Navigation System 11. Real-Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT 12. Pear Recognition System in an Orchard from 3D Stereo Camera Datasets Using Deep Learning Algorithms 13. Thermal Imaging and Deep Learning Object Detection Algorithms for Early Embryo Detection: A Methodology Development Addressed to Quail Precision Hatching 14. Strategic Short Note: Intelligent Sensing and Robotic Picking of Kiwifruit in Orchard 15. Low-Cost Automatic Machinery Development to Increase Timeliness and Efficiency of Operation for Small-Scale Farmers to Achieve SDGs 16. Vision-Based Leader Vehicle Trajectory Tracking for Multiple Agricultural Vehicles 17. Autonomous Robots in Orchard Management: Present Status and Future Trends 18. Strategic Short Note: Comparing Soil Moisture Retrieval from Water Cloud Model and Neural Network Using PALSAR-2 for Oil Palm Estates 19. Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach 20. Basal Stem Rot Disease Classification by Machine Learning Using Thermal Images and an Imbalanced Data Approach 21. Early Detection of Plant Disease Infection Using Hyperspectral Data and Machine Learning 22. Strategic Short Note: Development of an Automated Speed Sprayer for Apple Orchards in Japan 23. The Spectrum of Autonomous Machinery Development to Increase Agricultural Productivity for Achieving Society 5.0 in Japan
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The Digital Age in Agriculture Название: The Digital Age in Agriculture Автор: Mehmet Metin Ozguven Издательство: CRC Press Год: 2023 Страниц: 307 Язык: английский Формат: pdf...
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IoT and AI in Agriculture: Self- sufficiency in Food Production to Achieve Society 5.0 and SDG's Globally 
Название: IoT and AI in Agriculture: Self-sufficiency in Food Production to Achieve Society 5.0 and SDG's Globally
