Abstract
The text discusses the integration of advanced technologies like wireless sensor networks, Internet of Things (IoT), robotics, and artificial intelligence in smart agriculture to improve agricultural services and decision-making. Smart agriculture utilizes various sensors to monitor environmental factors and optimize crop yield, distinguishing it from traditional agriculture by its deployment architectures and processing stages. The article also highlights the importance of addressing research challenges to enhance the adoption and deployment of smart agriculture in the future.
Introduction
The text discusses the integration of advanced technologies like wireless sensor networks, Internet of Things (IoT), and artificial intelligence in smart agriculture to enhance agricultural services and decision-making. It highlights the shift from traditional agriculture to smart agriculture, emphasizing the role of sensors in enabling precision farming practices and improving agricultural productivity through data-driven insights and automation. The authors also address the challenges and opportunities in adopting smart agriculture for sustainable food production in the future.
Smart ploughing
Smart ploughing is a crucial stage in smart agriculture where soil analysis and examination are conducted to assess the nutrient status of the field before sowing seeds. This process involves using sensing devices and platforms to monitor soil parameters, such as moisture and texture, to make informed decisions that optimize soil health and crop growth while minimizing the use of fertilizers. By implementing smart ploughing techniques, farmers can improve soil quality and enhance crop productivity in a sustainable manner.
Smart sowing
Smart sowing in agriculture involves using advanced technologies like vision-based systems and GPS to precisely calculate the distance and depth for planting seeds. Traditional methods such as using funnel-shaped tools for seed sowing are being replaced by smart agriculture techniques that optimize seed placement for improved crop yields. These technologies help reduce manual labor, save time, and ensure efficient seed planting in various agricultural practices, including greenhouse farming and hydroponics.
Irrigation smart tech
Smart irrigation refers to the use of IoT-based technologies to efficiently manage water resources in agriculture, aiming to prevent over or irregular watering. Various smart irrigation systems, such as sprinkler-controlled irrigation and drip irrigation, have been developed to optimize water usage and enhance crop yields. These systems enable real-time monitoring and actuation, contributing to significant water savings and sustainable agricultural practices.
Hardware platforms
The hardware platforms used in smart agriculture involve the integration of various sensing technologies to enhance agricultural practices. These platforms include unmanned vehicles, drones equipped with cameras and sensors for monitoring crops, and agbots designed to automate tasks like weeding and pest detection. The use of these technologies aims to improve efficiency, reduce workload for farmers, and optimize crop management in both open fields and greenhouses.
Integration with other technologies
The integration of sensing technologies with other emerging technologies in smart agriculture involves combining various sensors with communication, edge/fog computing, and cloud computing layers to enhance agricultural practices. This integration allows for real-time monitoring, controlled irrigation, livestock management, and weather station operations, facilitated by wireless communication technologies like RFID, Bluetooth, ZigBee, Wi-Fi, LoRa, and cellular networks. These technologies enable efficient data transfer and support different aspects of smart agriculture, contributing to the modernization of agricultural processes.
With edge and fog layer
The Smart Agriculture Edge/Fog Layer involves integrating agricultural sensing technologies with computing infrastructures to provide real-time access to essential data for agricultural applications. Edge and fog computing technologies play a crucial role in meeting various agricultural needs by utilizing shared resources close to the network, enabling tasks that require more computation or precision to be performed efficiently. These technologies also facilitate remote access to agricultural data for analysis and have the potential to address challenges related to security and privacy through the integration of blockchain technology.
Conclusion and future
The conclusion of the text summarizes the discussion on smart agriculture, covering various aspects such as sensing technologies, integration of different technologies like edge, fog, and cloud computing, and open challenges in the field. It emphasizes the importance of addressing issues like adoption in developed and developing countries, standardization, energy harvesting, implementation of agbots and drones, utilization of AI, and ensuring security in smart agriculture for future advancements in the industry.