Intro -- AI, Edge and IoT-based Smart Agriculture -- Copyright -- Contents -- Contributors -- Section 1: IoT and edge foundations and framework -- Chapter 1: Internet of things (IoT) and data analytics in smart agriculture: Benefits and challenges -- 1. Introduction -- 1.1. Understanding AI -- 2. IoT ecosystem in agriculture -- 2.1. Management techniques/systems (IoT and big data) -- 2.2. Smart information systems (SIS) in agriculture -- 3. Benefits of IoT in agriculture -- 3.1. Remote sensing as a major tool in agriculture -- 3.2. Weather forecasting as a prime IoT in agriculture -- 3.3. Agriculture drones -- 3.4. Crop monitoring -- 3.5. Smart irrigation -- 3.6. Greenhouse monitoring and automation system -- 4. Open issues and key challenges in the adoption of IoT in agriculture -- 4.1. Reliability -- 4.2. Data privacy protection and issues of ownership -- 4.3. Autonomy foreseeability and causation -- 4.4. Control -- 4.5. Opaque research and development -- 5. Legal issues in regulating AI in agriculture -- 5.1. Torts and contracts -- 5.2. Crimes -- 5.3. Law relating to accidents, health, and safety -- 5.4. Accidents and negligence -- 5.5. Environmental laws -- 6. Conclusion -- References -- Chapter 2: Edge computing-Foundations and applications -- 1. Introduction -- 2. Edge computing -- 3. Applications of edge computing -- 3.1. Future trends of edge computing -- 4. Conclusions -- References -- Chapter 3: IoT-based fuzzy logic-controlled novel and multilingual mobile application for hydroponic farming -- 1. Introduction -- 2. Literature review -- 3. Methodology -- 4. Proposed method -- 5. Results and discussion -- 6. Conclusion -- References -- Chapter 4: Functional framework for IoT-based agricultural system -- 1. Introduction -- 1.1. Overview of the cases -- 1.2. Challenges, opportunities, and use of IoT applications in agriculture.

1.2.1. Challenges -- 1.2.1.1. Software complexity -- 1.2.1.2. Security -- 1.2.1.3. Technical skill requirement -- 1.2.1.4. Lack of supporting infrastructure -- 1.3. Opportunities allied with the solicitation of IoT in agriculture -- 1.3.1. Low-power wireless sensor (LPS) -- 1.3.2. Better connectivity -- 1.3.3. Operational efficiency -- 1.3.4. Remote control management -- 1.4. The architecture of a smart farm monitoring system -- 1.5. Energy-saving technologies -- 1.6. Security mechanisms -- 1.7. Advantages of IoT in agriculture system -- 1.7.1. Climate conditions or agility -- 1.8. Precision farming -- 1.9. Smart greenhouse -- 1.10. Data analytics -- 1.11. Agricultural drones -- 1.12. Limitations of the existing proposed model -- 2. Methodology -- 2.1. Block diagram of proposed model -- 2.2. Flow diagram of controlling process of motor using sensors -- 2.3. IoT with transmitter and receiver wireless sensor model -- 3. Experimental results and discussion -- 3.1. Experimental work -- 3.2. Thingspeak cloud server -- 4. Results -- 4.1. Measurements at 14:30 when soil is dry -- 4.2. Measurements on May 14, 2020 -- time varies when soil is wet -- 4.3. Measurement in night, when soil is dry -- 4.4. Measurement in night, when soil is wet -- 5. Discussion -- 6. Conclusion and future scope -- 6.1. Future scope -- References -- Chapter 5: Functional framework for edge-based agricultural system -- 1. Introduction -- 2. Relevant technologies -- 3. Edge computing in agricultural sectors -- 3.1. Role of edge computing in multiple facets of agriculture -- 3.1.1. Smart farming -- 3.1.2. Aquafarming -- 3.1.3. Livestock -- 3.1.4. Dairy farming -- 3.1.5. Hydroponics -- 4. Edge computing framework design in agriculture -- 4.1. Communication -- 4.1.1. Low range wide area network protocol (LoraWan) -- 4.1.2. Message Queue-Telemetry Transport Protocol (MQTT).

4.1.3. Radio Frequency Identification (RFID) -- 4.1.4. SigFox -- 4.1.5. Zigbee -- 4.1.6. WiFi -- 4.1.7. Bluetooth -- 4.1.8. Worldwide Interoperability for Microwave Access (WiMAX) -- 4.1.9. Routing Protocol for Low-Power and Lossy Networks (RPL) -- 4.2. Processing/computation -- 4.3. Analytics -- 4.4. Storage -- 4.4.1. Local -- 4.4.2. Edge/Cloudlet -- 4.4.3. Cloud -- 4.5. Actuation -- 4.6. Sensing -- 5. Edge computing implementation -- 5.1. Hardware implementation -- 5.2. Data communication technologies -- 5.3. Data processing implementation -- 5.4. Experimental set-up of edge-based agricultural system -- 5.4.1. Edge node 1 -- 5.4.2. Edge node 2 -- 5.4.3. Edge server -- 5.4.4. Cloud server -- 6. Conclusion -- References -- Chapter 6: Precision agriculture: Weather forecasting for future farming -- 1. Introduction -- 1.1. Terminologies employed in precision agriculture -- 1.1.1. Application map -- 1.1.2. Class post mapping -- 1.1.3. Georeferencing -- 1.1.4. Geographical information systems (GIS) -- 1.1.5. Global positioning systems (GPS) -- 1.1.6. Grid sampling -- 1.1.7. Kriging -- 1.1.8. Management zone -- 1.1.9. ``On-the-goþþ sensing -- 1.1.10. Pixel -- 1.1.11. Precision farming -- 1.1.12. Remote sensing -- 1.1.13. Scouting -- 1.1.14. Smoothing -- 1.1.15. Spatial resolution -- 1.1.16. Variable rate technology (VRT) -- 1.1.17. Yield map -- 1.1.18. Yield monitor -- 1.2. Connection between precision agriculture and traditional agriculture -- 1.2.1. Information -- 1.2.2. Technology -- 1.2.3. Decision support -- 2. Weather and climate -- 2.1. Weather -- 2.2. Climate -- 2.2.1. Tropical climate -- 2.2.2. Arid climate -- 2.2.3. Mediterranean climate -- 2.2.4. Humid climate -- 2.2.5. Arctic climate -- 2.2.6. Highland climate -- 3. Agricultural implications of climate change -- 3.1. Reducing the burden of agriculture on climate change.

3.2. Exploring the climate change influence as an influential element in agricultural productivity -- 4. Modern tools and techniques for precision agriculture -- 4.1. Internet of Things (IoT) -- 4.2. Sensor technology -- 4.3. Unmanned aerial vehicles (UAVs) -- 4.4. Unmanned ground vehicles (UGVs) -- 4.5. Robots -- 4.6. Smartphone -- 4.7. Autoguidance equipment (AGE) -- 4.8. Variable rate technology -- 4.9. Grid sampling -- 5. Conclusion -- References -- Section 2: IoT use cases in smart farming and smart agriculture -- Chapter 7: Crop management system using IoT -- 1. Introduction -- 2. Background and related works -- 3. Proposed model -- 4. Methodology -- 5. Performance analysis -- 6. Future research direction -- 7. Conclusion -- References -- Chapter 8: Smart irrigation and crop security in agriculture using IoT -- 1. Introduction -- 1.1. Overview -- 1.2. Applications -- 1.3. Motivation -- 1.4. Objectives -- 2. Methodology -- 2.1. Basic building blocks of an IoT device -- 2.1.1. Components used -- 2.1.2. Node MCU -- 2.1.3. PIR motion sensor -- 2.1.4. Buzzer -- 2.1.5. Raspberry Pi camera -- 3. Algorithms -- 3.1. Design flow -- 4. Implementation -- 4.1. System process -- 5. Testing and results -- 6. Conclusion and future scope -- References -- Chapter 9: The Internet of Things in agriculture for sustainable rural development -- 1. Introduction -- 1.1. Literature survey -- 1.2. Present scenario -- 1.2.1. National perspective -- 1.2.2. International perspective -- 2. Background details -- 2.1. Internet of Things -- 2.1.1. History of the IoT -- 2.1.2. IoT devices -- 2.2. IoT in agriculture for rural development -- 2.2.1. Significance in agriculture -- 2.2.2. Benefits of IoT in agriculture -- 2.2.3. Applications of IoT in agriculture -- 3. IoT in agriculture: Use cases -- 4. Case studies: IoT-based agriculture for sustainable rural development.

4.1. Slashing water consumption in avocado -- 4.2. Smart dairy farming -- 5. Impact of IoT on food sustainability and socioeconomic uplift -- 5.1. Food sustainability -- 5.2. Socioeconomic uplift -- 6. Challenges and opportunities -- 6.1. Unstable Internet connection in farms -- 6.2. Disrupted connectivity to cloud servers -- 6.3. Costly hardware -- 7. Conclusion -- References -- Chapter 10: Internet of Things (IoT) in agriculture toward urban greening -- 1. Introduction -- 2. IOT architectures -- 2.1. Definitions of IoT -- 2.2. G-IoT -- 2.3. Architecture of IoT -- 3. G-IOT application -- 3.1. Green tags -- 3.2. Green sensing networks -- 3.3. Green Internet technologies -- 4. IOT applications -- 4.1. Smart industrial plants and machine-to-machine communications -- 4.2. Smart plant monitoring -- 4.3. Smart data collection -- 4.4. Smart sensing -- 4.5. Smart sports -- 4.6. Smart social networks -- 4.7. Smart agriculture -- 4.8. Smart waste -- 4.9. Smart environment -- 4.10. Smart grid -- 5. G-IOT challenges and opportunities -- 5.1. Green infrastructure -- 5.2. Green spectrum management -- 5.3. Green communication -- 5.4. Green security and management -- 6. Conclusion -- References -- Chapter 11: Smart e-agriculture monitoring systems -- 1. Introduction -- 2. Need for smart e-monitoring system for agriculture -- 3. System architecture -- 3.1. WSN-based architecture -- 3.2. IoT-Cloud based architecture -- 4. IoT and data analytics in agriculture -- 4.1. Devices deployed -- 4.2. Data acquisition -- 4.3. Data processing -- 4.4. Data analytics -- 5. Different types of solutions available -- 5.1. Botanicalls -- 5.2. Parrot flower power -- 5.3. HarvestGeek -- 5.4. Open garden -- 5.5. Automated hydroponics: Bitponics -- 5.6. Edyn -- 5.7. Koubachi -- 6. Research challenges -- 7. Case study on IoT-based monitoring systems.