3GE Collection On Agriculture AI Edge And LoT Based Smart Agriculture

[Farid-Khan]

? Pages: 293

? INFO: 2023 | ISBN: 1984680862 | English | 292 pages | True PDF | 19 MB

[color=#888888]? DESCRIPTION: Agriculture is the broadest economic sector and plays a vital role in the overall development of a nation. Technological advancements in the agricultural arena are essential to increasing the efficiency of various farming activities. Through the Internet of Things (IoT), we have revolutionized every facet of our daily lives by making systems increasingly intelligent. IoT is defined as a network of self-configuring objects; in this era of smart farming, IoT-based devices are transforming agricultural production by not only enhancing yields but also making operations more cost-effective.

The concept of precision agriculture is revolutionizing smart farming worldwide. By utilizing Artificial Intelligence (AI) and IoT in cyber-physical farm management, agriculture becomes significantly more effective. To produce optimal crop yields, smart agriculture relies heavily on the implementation of these technologies to monitor climate factors, soil characteristics, and moisture levels. IoT devices-including remote sensors, robots, and drones-can be linked via the internet to allow for automated control and data collection. Precision agriculture aims to increase production while minimizing the misuse of fertilizers and pesticides through improved spatial management practices.

This E-Collection on AI, Edge, and IoT-based Smart Agriculture highlights intelligent, secure systems equipped with wireless sensors implanted directly into farms. As food shortages and population growth present major challenges to global sustainable development, advanced technologies like AI, IoT, and the mobile internet provide realistic solutions. Therefore, this work focuses on new approaches to Smart Farming (SF). IoT serves as an essential pillar in these systems, connecting sensor devices to perform fundamental tasks.

For instance, smart irrigation systems utilize sensors to monitor water levels, irrigation efficiency, and local climate data, governed by smart controllers and mathematical models. Additionally, this work illustrates the application of Unmanned Aerial Vehicles (UAVs) and robots. These tools can perform several functions-such as harvesting, seeding, weed detection, irrigation, and pest spraying-in real-time using IoT, Deep Learning (DL), Machine Learning (ML), and wireless communications.

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